# data[].attributes.headline
So many datas, unorganized and uncategorized stored in different dbs.

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So many datas, unorganized and uncategorized stored in different dbs.

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DATA IS ONLINE INFORMATION THAT HAS BEEN TRANSLATE INTO A FORM THAT IS EFFICIENT

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learning analytics refers to the collection and analysis of data about learners and their environments for the purpose of understanding and improving learning outcomes. Learning analytics is where big data meets traditional quantitative methods in education.

# data[].attributes.body
Big data refers to data sets that are too large or complex to be dealt with by traditional data-processing application software

# data[].attributes.body
the quantities, characters, or symbols on which operations are performed by a computer, which may be stored and transmitted in the form of electrical signals and recorded on magnetic, optical, or mechanical recording media.

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The ability to perform tasks in complex environments without constant guidance by a user.

# data[].attributes.body
Learning analytics is defined as the “measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs”

# data[].attributes.body
Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions.

# data[].attributes.body
Data is a set of qualitative or quantitative variables – it can be structured or unstructured, machine readable or not, digital or analogue, personal or not.

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BIG DATA can be a summary term to describe a set of tools, methodologies and techniques for being able to derive new “insight” out of extremely large, complex sample sizes of data and (most likely) combining multiple extremely large complex datasets.

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It is the measurement, collection, analysis and reporting of data about students and their contexts in order to understand learning and the environments in which it occurs.

# data[].attributes.body
It is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.

# data[].attributes.body
Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems.

# data[].attributes.body
Big data deal with too large or complex data sets which is difficult to manage in traditional data-processing application software.

# data[].attributes.body
Traditional data is the structured data that is being majorly maintained by all types of businesses starting from very small to big organizations.

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By integrating learning analytics into their lessons, teachers are given the opportunity to make better estimates of their students’ level of achievement. For example, let’s look at the topic of computational thinking. With the help of learning analytics, a teacher is able to track how a student is acquiring this skill. This gives teachers a very valuable insight into the learning process of each individual pupil. For example, when a class is assigned to develop a game, the teacher – via the learning analytics dashboard – can monitor how a particular student is learning Boolean logic or approaching the programming process. This can help the teacher to get a clearer idea of what aspects should be emphasised more or less in future computational thinking lessons.

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This report describes the current state of the art in artificial intelligence (AI) and its potential impact for learning, teaching, and education. It provides conceptual foundations for well-informed policy-oriented work, research, and forward-looking activities that address the opportunities and challenges created by recent developments in AI. The report is aimed for policy developers, but it also makes contributions that are of interest for AI technology developers and researchers studying the impact of AI on economy, society, and the future of education and learning.

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Big data in education: a state of the art, limitations, and future research directions - International Journal of Educational Technology in Higher Education

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Big data is an essential aspect of innovation which has recently gained major attention from both academics and practitioners. Considering the importance of the education sector, the current tendency is moving towards examining the role of big data in this sector. So far, many studies have been cond…

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While many companies don’t want to reveal the details of their algorithms, being aware of the basic principles helps you understand the potential implications: these involve so called filter bubbles, echo-chambers, troll factories, fake news, and new forms of propaganda.

# data[].attributes.body
Big data is an essential aspect of innovation which has recently gained major attention from both academics and practitioners. Considering the importance of the education sector, the current tendency is moving towards examining the role of big data in this sector. So far, many studies have been conducted to comprehend the application of big data in different fields for various purposes. However, a comprehensive review is still lacking in big data in education.

# data[].attributes.body
Learning Analytics refers to the collection and analysis of data about learners and their environments for the purpose of understanding and improving learning outcomes. Learning analytics is where big data meets traditional quantitative methods in education.

# data[].attributes.body
The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

# data[].attributes.body
Big data are extremely large data sets that may be analysed computationally to reveal patterns, trends and assiciations, especially relating to human behaviour and interactions.

# data[].attributes.body
Are a collection of discrete values that convey information, describing quantity, quality, fact, statistics, other basic units of meaning, or simply sequences of symbols that may be further interpreted, usually organized into structures such as tables that provide additional context and meaning, and which may themselves be used as data in larger structures.

# data[].attributes.body
Learning analytics is a variety of data to mobilize tools in the service of learning.

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Society for Learning Analytics Research (SoLAR)

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LEARNING ANALYTICS is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs, as defined back in 2011 for the first LAK, this general definition still holds true even as the field has grown. Learning analytics is both an academic field and commercial marketplace which have taken rapid shape over the last decade. As a research and teaching field, Learning Analytics sits at the convergence of Learning (e.g. educational research, learning and assessment sciences, educational technology), Analytics (e.g. statistics, visualization, computer/data sciences, artificial intelligence), and Human-Centered Design (e.g. usability, participatory design, sociotechnical systems thinking).

SO WHAT'S ALL THE FUSS ABOUT? People have been researching learning and teaching, tracking student progress, analysing school or university data, designing assessments and using evidence to improve teaching and learning for a long time. Learning Analytics builds on these well established disciplines, but seeks to exploit the new opportunities once we capture new forms of digital data from students' learning activity, and use computational analysis techniques from data science and AI.

KEY USES. Historically, some of the most common uses of learning analytics is prediction of student academic success, and more specifically, the identification of students who are at risk of failing a course or dropping out of their studies. While it is reasonable that these two problems attracted a lot of attention, learning analytics are far more powerful. The evidence from research and practice shows that there are far more productive and potent ways of using analytics for supporting teaching and learning. Some of the most popular goal of learning analytics include:

1. Supporting student development of lifelong learning skills and strategies

2. Provision of personalised and timely feedback to students regarding their learning

3. Supporting development of important skills such as collaboration, critical thinking, communication and creativity

4. Develop student awareness by supporting self-reflection

5. Support quality learning and teaching by providing empirical evidence on the success of pedagogical innovations

METHODOLOGIES
Descriptive Analytics: insight into the past

Uses data aggregation and data mining to understand trends and evaluative metrics over time. The majority of statistics use falls into this category which is limited to past data and includes:

Student feedback gathered from student satisfaction and graduate surveys

Analysis of data at all stages of the student lifecycle starting from admissions process , to student orientation, enrolments, pastoral care, study support, exams and graduations.

Diagnostic Analytics: why did it happen

This form of advanced analytics is characterised by techniques such as drill-down, data discovery, data mining and correlations to examine data or content to answer the question &dash; "Why did it happen?" and includes:

Analysis of data to inform and uplift key performance indicators across the organization

Analysis of patterns to design appropriate metrics

Equity access reporting and analysis of effective strategies to support students

Learning management system metrics to improve student engagement

Predictive Analytics: understanding the future

Combines historical data to identify patterns in the data and applies statistical models and algorithms to capture relationships between various data sets to forecast trends and includes:

Development of Staff Dashboards to help predict student numbers and cohort mobility through programs to assist in identifying areas for improvement

Prescriptive Analytics: advise on possible outcomes

Goes beyond descriptive and predictive by recommending one or more choices using a combination of machine learning, algorithms, business rules and computational modelling such as:

Focusing on subject/courses where small changes could have a big impact on improving student engagement, feedback and outcomes

Data visualisation via specific tools to provide program/degree level metrics on student enrolments, program stage, results and survey feedback to give teaching staff visual snapshots of students in their programs

WHO BENEFITS? Learning Analytics provides researchers with exciting new tools to study teaching and learning. Moreover, as data infrastructures improve - from data capture and analysis, to visualization and recommendation - we can close the feedback loop to learners, offering more timely, precise, actionable feedback. In addition, educators, instructional designers and institutional leaders gain new insights once the learning process is persistent and visible.

ORGANIZATIONAL INFRASTRUCTURES. The community that SoLAR is fostering is distinctive in its interest in understanding the organizational systems required to introduce and sustain analytics. SoLAR's events and publications draw an eclectic audience that welcomes leaders and policy makers in education/business/government concerned with strategies for organizational change.

ETHICS OF DATA, ANALYTICS & AI. There is rightly much public and professional debate around the ethics of 'Big Data' and AI, including privacy, the problem of opaque 'black box' algorithms, the risk of training machine learning classifiers on biased datasets, and the dangers of incorrectly predicting someone's behaviour. These concerns are just as relevant in education, so the ethics of educational data, analytics and AI are front and center in SoLAR's work, with a very active stream in our events and publications.

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What is Learning Analytics? - Society for Learning Analytics Research (SoLAR)

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LEARNING ANALYTICS is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs, as defined back in 2011 for the first LAK, this general definition still holds true even as the field has grown.

# data[].attributes.body
Big Data is the information on behaviour and performance. In education , student's background and learning behaviours are big Data.

# data[].attributes.body
Data is something that is given to any system in anticipation of an output
https://bscholarly.com/difference-between-data-and-information/#Meaning_of_Data

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Differences between Data and Information: Data and Information are normally used words in numerous fields in our world; digital as it has become. In this new age, an active user of both computer and internet services may not be able to go a day without arriving at these two words.

They are so far-reaching that they cover from very easy actions like sending a text or an Instagram DM to launching a missile. In the digital space, what you give is essentially what you get. This is why the terms 'data' and 'information are so relevant. They serve as the basis for all technological and digital milestones ever recorded in human history, and a quick scan of this article would show you just why.

In recent years, the focus has been on the particular data and information in circulation, rather than the industry in which such data or information is utilized. This is because these components have contributed to why we have the technology, electricity, and social media in our world today.
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Meaning of Information

Information is specified clearly as "news or knowledge received or given". The word comes from the Latin word īnfōrmātiō, meaning "formation or conception." In basic terms, Information refers to that which is received or knowledge transmitted.

Unlike data, it isn't in its raw form. It exists because data has been collected, and processed. It has value and is mostly legible. To get to information, there needs to be processing and organization of working data. Just like a car cannot run without fuel.
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Meaning of Data

This term is derived from 'Datum', a Latin word that mostly refers to 'anything that is given'. At face value, the word connotes something that is given to any system in anticipation of an output. This means that the word concerns itself mostly with the conception of most products. There are existing data for every product of technology and research.

Primary Data and Secondary Data are the two major kinds of Data, and their subdivisions include; external, internal, qualitative, and quantitative data.

To the digital world, the word data simply means a set of details such as numerical, texts, symbols, signs, or inscription. It can also include the analysis of any subject (in cases of research projects), coding language, and equations.
These characters usually come in simple forms such as 0s and 1s and upon translation, can form a fact or processed unit. What this means is that several texts put together would produce a unit of value.
A clear non-technical example would be the assembling of the texts; h/e/l/l/o, to form 'Hello.' Note that the former does not hold any meaning, unlike the latter which is an opening in a conversation. Take note, however, that in computers, data is usually in the form of 0s and 1s. In the past, data was classified in punched cards, which turned to magnetic tapes, then subsequently to disks.
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Main Differences Between Data and Information

Data and Information are two terms that hold different meanings in different fields. There are some similarities between the two concepts, but the differences are enough to completely tell them apart, and they include;

1. Data consists of raw details eg; digits, reports, texts. The information does not always consist of these things. The latter is also open to several interpretations.

2. As we explained earlier, data is related to input and giving relevant details to a system, while information concerns all forms of output.

3. For data to fully play its parts, it needs to go through a selection process, whereby certain aspects are filtered, processed, and turned to information, ready for direct assimilation.
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4. In journalism, the dynamics of data and information are slightly different. Data her is printable and legible. However, it contains the most basic detail derived from research and reports. It is only part of the journalist's material. It is information, however, that lends significance to these reports. This is what gets to the mail or papers.

5. In a computer system, data cannot be printed out, nor is it legible for example; the codes used in computer programming can only be understood by computer systems. Information on the other hand can be in print and available for easy dissemination.

6. Data in research is a set of sufficient details that explain the state of things. Researchers lookout for data in a particular field to solve a problem or crisis. It is the most important part of research -accumulating data. Information is the whole of the research purpose; thesis building, data accumulation, and solution.
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7. In statistics, data is largely still raw and unprocessed but is referred to as unprocessed information, while statistics takes the place of information in the definition. The role of Statistics, in this case, would be to accumulate and filter the unprocessed information.

8. While Information is organized and easy to assimilate, Data is largely in cluttered form but makes perfect sense to the computer system.

9. Unlike Information, Data is not specifically organized, neither does it translate directly to the solved answers where there are questions. This is because there is very little correlation between accumulated data and issues unless it is processed.

10. There is a significant difference between data and information when it comes to business and commerce. In the case of data, the content is mostly raw digits, while information is a set of data points that explains the already solved equation.
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The most important you should take away from this article is the fact that these two terms, regardless of their differences, work together to achieve the most. Today, several problems from very vast fields are processed at record time. In the medical field, there is an increasing inclusion of technology and the use of data in the treatment of patients. There are currently more scans and data machines in various hospitals than ever before.

Humans are starting to care a little more about history and data because medicine has become a little easier. After all, doctors and scientists can now refer to the medical history and past medications in the treatment of patients.

Also, in Banking, there is an accountability system for saving, and withdrawing because there is existing data for any such transactions.

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10 Differences between Data and Information - Bscholarly

# data[].attributes.attachment_link.description
Differences between Data and Information: Data and Information are normally used words in numerous fields in our world; digital as it has become. In this new age, an active user of both computer and internet services may not be able to go a day without arriving at these two words.

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AI has not a single dimension, is a collection of concepts, problems and methods for solving them.

https://www.elementsofai.com/

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In spring 2018, MinnaLearn and the University of Helsinki came together with the aim of helping people to be empowered, not threatened, by artificial intelligence. Together, they built the Elements of AI to teach the basics of AI to people from a wide range of backgrounds.

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A free online introduction to artificial intelligence for non-experts

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Learn more about MinnaLearn's and the University of Helsinki's AI course - no programming or complicated math required.

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Katarina, Serbia: Learning analytics is the measurement collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environmemt in which it occurs, as defined back in 2011, for the first LAK, this general definition still holds true

# data[].attributes.subject
Katarina, Serbia: Learning analytics is the measurement collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environmemt in which it occurs, as defined back in 2011, for the first LAK, this general definition still holds true

# data[].attributes.headline
Katarina, Serbia: Artificial intelligence is the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages

# data[].attributes.subject
Katarina, Serbia: Artificial intelligence is the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages

# data[].attributes.headline
Katarina, Serbia: Big data are extremely large data sets that may be analysed computationally to reveal patterns, trends and assiciations, especially relating to human behaviour and interactions.

# data[].attributes.subject
Katarina, Serbia: Big data are extremely large data sets that may be analysed computationally to reveal patterns, trends and assiciations, especially relating to human behaviour and interactions.

# data[].attributes.headline
Data are facts and statistics collected together for reference and analysis.

# data[].attributes.subject
Data are facts and statistics collected together for reference and analysis.

# data[].attributes.body
The analisis of learning.

# data[].attributes.body
Artifical human inteligence

# data[].attributes.body
Big data refer at data sets.

# data[].attributes.body
Data is online information.

# data[].attributes.body
Is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs.

# data[].attributes.body
Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision.

# data[].attributes.body
Big data refers to data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many fields (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Big data analysis challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy, and data source.

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Data is information that has been translated into a form that is efficient for movement or processing. Relative to today's computers and transmission media, data is information converted into binary digital form

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.... is the measurement, collection, analysis, and reporting of data about learners, learning experiences, and learning programs for purposes of understanding and optimizing learning and its impact on an organization’s performance.

https://www.watershedlrs.com/resources/definition/what-is-learning-analytics/

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Learning analytics is the measurement, collection, analysis, and reporting of data about learners, learning experiences, and learning programs for purposes of understanding and optimizing learning and its impact on an organization's performance.

Most accepted definitions refer to analytics in the education space, not the corporate space. At Watershed, we've built on these definitions to create a description for these analytics in the corporate space. The key part of this definition is the second half. In a corporate context, learning analytics ultimately serves the purpose of improving organizational performance.

The Categories of Learning Analytics

There are many types of learning analytics and things you can measure and analyze. We segment these analytics into three categories: learning experience analytics, learner analytics, and learning program analytics.What is learning experience analytics?

What is learning experience analytics?

Learning experience analytics seek to understand more about a specific learning activity. The learning experience category often answers questions about usage patterns for a specific activity, such as:

How much is it being used?

Is there "scrap learning" that isn't being used?

When is it being used and for how long?

What resources or topics do learners search for most?

How do learners navigate the experience?
[Read More on Learning Experience Analytics]​

What is learner analytics?

Learner analytics seek to understand more about a specific person or group of people engaged in activities where learning is one of the outputs. It covers questions about usage patterns and performance for specific learners, such as:

Who is training the most?

Has everyone in this group completed compliance training?

What skills does this person/group have? Where are the gaps?

Who needs development? Who are high-potential employees?

What topics are these learners interested in?
[Read More on Learner Analytics]

What is learning program analytics?

Learning program analytics seek to understand how an overall learning program is performing. A learning program typically encompasses many learners and many learning experiences (although it could easily contain just a few). Learning program analytics answer questions about learning's strategic impact on the business, such as:

Do learners behave differently after completing training?

Has organizational performance improved because of learning?

Has this method of learning saved the company money?

Which learning methodology is more effective?
[Read More on Learning Program Analytics]

The Levels of Learning Analytics

We define four levels of these analytics: measurement, evaluation, advanced evaluation, and predictive and prescriptive analytics. Although each of these levels are correctly referred to as analytics, they mean vastly different things in terms of complexity, difficulty, and power.

Measurement

Analytics start with measurement, or the simple act of tracking things and recording values to tell us what happened. Measurement doesn't require complicated math or statistics, but you must start by gathering data. Otherwise, it's impossible to do any analytics.

Data Evaluation

Once the data has been captured, it's time to start evaluating it and assessing whether the data means something good or bad. At this level, we're applying high-school level math-averages, means, modes, and basic statistics-to aggregate the data and establish benchmarks.

In current practice, most analytics fall into the basic data evaluation category, and that's OK. There's tremendous value here, and opportunities for some huge wins.

Advanced Evaluation

Exciting things start to happen as we get into advanced evaluation and apply college-level math. Here, we're looking at things such as correlations and regression analysis.

We're applying statistical techniques to understand, not just what happened, but why it happened. Advanced evaluation creates theories about causation, allowing us to focus on what works best and scrap ineffective learning.

Predictive & Prescriptive Analytics

The most sophisticated levels of analytics are predictive and prescriptive analytics, which require graduate-level math and often rely on AI or machine learning powered by big data sets. Predictive analytics say, "based on what's happened in the past, here's what is most likely to happen next." Prescriptive analytics take that a step further and say, "based on what's most likely to happen next, here's the action we should take to optimize the outcome."

Ultimately, when we get here, we rely on highly intelligent recommendation engines that deliver just the right learning, at just the right moment, in just the right way to significantly improve performance. As an industry, we're not there yet, but we can get there if we start measuring and work our way up.
[Read more on Learning Analytics Complexities]
Combining Categories & Complexity

Learning analytics don't have to be complicated. Start with the basic categories and complexities that comprise these analytics and work your way outward. When these categories and complexities are combined, they form the matrix-or pyramid-of learning analytics.

Many organizations master the lowest level of complexity within a specific category and then work their ways outward. And some organizations may reach high levels in one category, but reach lower levels in other categories. As long as you identify where you fall within the analytics pyramid, you'll know where you're going-which helps you set goals, determine metrics, and evaluate the maturity of your program.

Hear from your peers.

Read tips from practitioners who have implemented various levels of learning analytics in their organizations.

"Be relentless in your vision and flexible with the details of how you approach understanding and improving a culture of learning."
Gordon Trujillo, Visa

"Instead of building Excel reports on a full-time basis, our analyst gets to probe the data to uncover stories and experiment information."
Andy Webb, Applied Industrial Technologies

"You have to pick your battles and understand what is critical to know and report and what isn't relevant to moving the needles."
Monica Griggs, Bridgestone

"The better we understand the process as applied in reality, the better we can evaluate our data and see if we are missing anything."
Amir Bar, Halliburton

What is a Learning Analytics Platform (LAP)?

Similar to a learning record store (LRS), an LAP typically takes advantage of xAPI to aggregate data about all of the training and learning events across your organization. However, an LAP takes it a step further by applying sophisticated reporting and analytics capabilities to that data.
1) It isn't an LMS. 2) It isn't really a BI tool. 3) And, it isn't just an LRS.
Read more about how a learning analytics platform works alongside an LMS or BI tool on the Insights blog.

Want to get started with learning analytics?

Download our eGuide that explores the five steps to start using learning analytics, covers different technologies that support learning analytics programs, and shares approaches to consider.

# data[].attributes.attachment_link.description
Learning analytics is the measurement, collection, analysis, and reporting of data about learners, learning experiences, and learning programs for purposes of understanding and optimizing learning and its impact on an organization's performance. Most accepted definitions refer to analytics in the education space, not the corporate space.

# data[].attributes.body
.... is a branch of computer science dealing with the simulation of intelligent behavior in computers or ...
... the capability of a machine to imitate intelligent human behavior

https://www.merriam-webster.com/dictionary/artificial%20intelligence

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self-evident

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The Exceptions Quiz III

Which of these words does not mean "nonsense"?

Test your visual vocabulary with our 10-question challenge!

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Test Your Knowledge - and learn some interesting things along the way.

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# data[].attributes.attachment_link.description
self-evident Get Word of the Day daily email! The Exceptions Quiz III Which of these words does not mean "nonsense"? Test your visual vocabulary with our 10-question challenge! TAKE THE QUIZ Test Your Knowledge - and learn some interesting things along the way. TAKE THE QUIZ

# data[].attributes.body
... an accumulation of data that is too large and complex for processing by traditional database management tools

https://www.merriam-webster.com/dictionary/big%20data

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Did you know?

Big data is a new addition to our language, but exactly how new is not an easy matter to determine. A 1980 paper by Charles Tilly provides an early documented use of big data, but Tilly wasn't using the word in the exact same way we use it today; rather, he used the phrase "big-data people" to refer to historians engaged in data-rich fields such as cliometrics. Today, big data can refer to large data sets or to systems and solutions developed to manage such large accumulations of data, as well as for the branch of computing devoted to this development. Francis X. Diebold, a University of Pennsylvania economist, who has written a paper exploring the origin of big data as a term, a phenomenon, and a field of study, believes the term "probably originated in lunch-table conversations at Silicon Graphics Inc. (SGI) in the mid 1990s...."

Example Sentences

These example sentences are selected automatically from various online news sources to reflect current usage of the word 'big data.' Views expressed in the examples do not represent the opinion of Merriam-Webster or its editors. Send us feedback.

Word History

1996, in the meaning defined above

The first known use of big data was in 1996

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Dictionary Entries Near big data

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Merriam-Webster unabridged

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# data[].attributes.attachment_link.description
Big data is a new addition to our language, but exactly how new is not an easy matter to determine. A 1980 paper by Charles Tilly provides an early documented use of big data, but Tilly wasn't using the word in the exact same way we use it today; rather, he used the phrase "big-data people" to refer to historians engaged in data-rich fields such as cliometrics.

# data[].attributes.body
... a factual information (such as measurements or statistics) used as a basis for reasoning, discussion, or calculation

https://www.merriam-webster.com/dictionary/data

# data[].attributes.attachment_link.description
Data definition is - factual information (such as measurements or statistics) used as a basis for reasoning, discussion, or calculation. How to use data in a sentence. Is data singular or plural?

# data[].attributes.body
LEARNING ANALYTICS is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs.

# data[].attributes.body
Artificial intelligence - the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

# data[].attributes.body
Big Data -extremely large data sets that may be analysed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interactions.

# data[].attributes.body
Data is information such as facts and numbers used to analyze something or make decisions. Computer data is information in a form that can be processed by a computer.

# data[].attributes.body
LEARNING ANALYTICS is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs, as defined back in 2011 for the first LAK, this general definition still holds true even as the field has grown. Learning analytics is both an academic field and commercial marketplace which have taken rapid shape over the last decade.

# data[].attributes.body
Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision.

https://www.techtarget.com/searchenterpriseai/definition/AI-Artificial-Intelligence

# data[].attributes.body
Big data refers to the large, diverse sets of information that grow at ever-increasing rates. It encompasses the volume of information, the velocity or speed at which it is created and collected, and the variety or scope of the data points being covered (known as the "three v's" of big data). Big data often comes from data mining and arrives in multiple formats.
https://www.investopedia.com/terms/b/big-data.asp

# data[].attributes.body
Data is information such as facts and numbers used to analyze something or make decisions. Computer data is information in a form that can be processed by a computer.

Data is the plural of Latin datum, "something given." In scientific use, the word data is often considered to be a plural noun meaning "pieces of information": The data are reliable. But most people think of data as a mass noun meaning "information" and use the word with a singular verb and pronoun: The data is reliable because it was tested by experts.

https://www.vocabulary.com/dictionary/data

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Analitika uĉenja je mjerenje, prikupljanje, analiziranje i prikaz podataka o učenicima (studenti) i njihovim okruženjima u svrhu razumijevanja i optimiziranja procesa učenja i okruženja u kojem se ono odvija.
https://www.srce.unizg.hr/files/srce/docs/CEU/moodlemoot/2022/braunager_zizak_analitike_ucenja_moodlemoot_2022.pdf

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Umjetna inteligencija je sposobnost nekog uređaja da oponaša ljudske aktivnosti poput zaključivanja, učenja, planiranja i kreativnosti.

Umjetna inteligencija omogućuje tehničkim sustavima percipiranje okruženja, uzimanje u obzir onog što vide i rješavanje problema kako bi postigli neki cilj. Računalo prima podatke (koji su već pripremljeni ili prikupljeni s pomoću vlastitih senzora, npr. fotoaparata), obrađuje ih i daje odgovore.
https://www.europarl.europa.eu/news/hr/headlines/society/20200827STO85804/sto-je-umjetna-inteligencija-i-kako-se-upotrebljava

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Podaci su prikaz kvalitativne ili kvantitativne varijable. Dakle, dodjeljuje mu se broj, slovo ili simbol.Podaci mogu biti različitih vrsta. Međutim, jedan od načina njihove klasifikacije bio bi sljedeći:

Numerički: To je lik koji može biti cjelovit ili s decimalama.

Tekst: To može biti slovo, riječ ili niz znakova poredanih u određenom slijedu.

Također se mogu klasificirati na sljedeći način:

Kvalitativno: Opisuju karakteristiku koja nije mjerljiva brojevima. Primjerice, nacionalnost.

Kvantitativni: To su podaci koji se mogu opisati brojkama.

https://hr.economy-pedia.com/11036214-fact

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prikupljene skupove podataka koji su toliko veliki i složeni da ih moraju obrađivati nove tehnologije, kao što je umjetna inteligencija. Podaci dolaze iz mnogih različitih izvora. Često su iste vrste, na primjer, GPS podaci iz milijuna mobilnih telefona upotrebljavaju se za smanjenje prometnih gužvi; no mogu se i kombinirati, kao na primjer podaci iz zdravstvenih kartona i aplikacija za pacijente. Tehnologija omogućuje vrlo brzo prikupljanje tih podataka, skoro u stvarnom vremenu, i njihovu analizu kako bi se dobile nove spoznaje.
https://www.europarl.europa.eu/news/hr/headlines/society/20210211STO97614/veliki-podaci-definicija-koristi-izazovi-infografika

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Sve više koristimo elektroničke uređaje, a proizvodni procesi sve se više digitaliziraju. To znači da se svakodnevno goleme količine digitalnih podataka stvaraju u gospodarstvu i kod privatnih i društvenih aktivnosti ljudi.

Komisija predviđa da će ukupna količina podataka do 2025. porasti za 530 posto u odnosu na 2018. godinu.

Podaci su važan dio digitalne transformacije EU-a. Umjetna inteligencija, prioritet EU-a, ovisi o podacima, a njezin razvoj ovisi o načinu na koji će se upravljati podacima u Europi. Podaci su sastavni dio digitalnih usluga koje oblikuju naš svakodnevni život i gospodarstvo. Parlament je usvojio prijedlog za zakonodavstvo o podacima kako bi građani, poduzeća, oporavak i zelena tranzicija imali koristi od učinkovite podatkovne strategije.

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Veliki podaci: definicija, koristi, izazovi (infografika) | Vijesti | Europski parlament

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Sve više koristimo elektroničke uređaje, a proizvodni procesi sve se više digitaliziraju. To znači da se svakodnevno goleme količine digitalnih podataka stvaraju u gospodarstvu i kod privatnih i društvenih aktivnosti ljudi. Komisija predviđa da će ukupna količina podataka do 2025. porasti za 530 posto u odnosu na 2018. godinu.

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Learning analytics refers to the collection and analysis of data about learners and their environments for the purpose of understanding and improving learning outcomes. Learning analytics is where big data meets traditional quantitative methods in education.

Source: https://www.northeastern.edu/graduate/blog/learning-analytics/

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From: https://www.europarl.europa.eu/news/hr/headlines/priorities/umjetna-inteligencija-u-eu-u/20200827STO85804/sto-je-umjetna-inteligencija-i-kako-se-upotrebljava

Artificial intelligence is the ability of a device to imitate human activities such as thinking, learning, planning and creativity.
Artificial intelligence systems can adjust their behavior to a certain extent by analyzing previous situations and working independently.

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The definition of big data is data that contains greater variety, arriving in increasing volumes and with more velocity. This is also known as the three Vs.

Put simply, big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems you wouldn’t have been able to tackle before.

Source: https://www.oracle.com/big-data/what-is-big-data/#defined

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In computing, data is information that has been translated into a form that is efficient for movement or processing. Relative to today's computers and transmission media, data is information converted into binary digital form. It is acceptable for data to be used as a singular subject or a plural subject.

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A short introduction to learning analytics

By Yi-Shan Tsai, University of Edinburgh

LEARNING ANALYTICS is the measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs, as defined back in 2011 for the first LAK, this general definition still holds true even as the field has grown. Learning analytics is both an academic field and a commercial marketplace which have taken rapid shape over the last decade. As a research and teaching field, Learning Analytics sits at the convergence of Learning (e.g. educational research, learning and assessment sciences, educational technology), Analytics (e.g. statistics, visualization, computer/data sciences, artificial intelligence), and Human-Centered Design (e.g. usability, participatory design, sociotechnical systems thinking).

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Artificial Intelligence (AI) is the combination of algorithms designed to create machines with the same capabilities as human beings. A technology that still seems distant and mysterious to us, but which has been present in our daily lives at all times for some years now.
Computer science experts Stuart Russell and Peter Norvig differentiate between several types of artificial intelligence:

Systems that think like humans

Systems that act like humans

Systems that think rationally

Systems that act rationally

PRACTICAL APPLICATIONS OF ARTIFICIAL INTELLIGENCE

AI is present in mobile face detection, in virtual voice assistants such as Apple's Siri, Amazon's Alexa, or Microsoft's Cortana, and is integrated into our everyday devices through bots (short for robots) or mobile apps, such as LyliExternal link, opens in a new window., a personal shopper in digital version; ParlaExternal link opens in a new window., conceived to help us with language learning; the EmsExternal link opens in a new window., designed to make the arduous task of finding a new apartment a little more bearable for us; or GyantExternal link opens in a new window., a Facebook virtual assistant that issues medical 'diagnoses'. The goal of all of them: is to make people's lives easier.

Advances in AI are already driving the use of big data because of its ability to process huge amounts of data and provide communication, commercial, and business advantages that have led it to position itself as the essential technology of the coming decades. Transportation, education, healthcare, culture... no sector will resist its charms.

IN THE ATTACHED IMAGE: MAIN APPLICATIONS OF ARTIFICIAL INTELLIGENCE:

1. VIRTUAL PERSONAL ASSISTANTS,

2. CLIMATE,

3. AGRICULTURE,

4. FINANCE,

5. LOGISTICS AND TRANSPORTATION,

6. EDUCATION,

7. COMMERCE

8. AND HEALTHCARE

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Big data is a large volume of data. An informational asset that is characterized by high quantity, speed, and diversity. Large data sets that are difficult to process, control or examine in a traditional way. Big data is generally characterized into 3 Vs which are Volume, Variety, and Velocity. Volume refers to as a large amount of data or increasing scale of data. The size of big data can be measured in terabytes and petabytes. In order to cater for the large volume of data, high capacity storage systems are required. The variety refers to as a type or heterogeneity of data. The data can be in a structured format (databases) or unstructured format (images, video, emails). Big data analytical tools are helpful in handling unstructured data. Velocity refers to as the speed at which big data can access. The data is virtually present in a real-time environment (Internet logs).

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Big data in education: a state of the art, limitations, and future research directions - International Journal of Educational Technology in Higher Education

# data[].attributes.attachment_link.description
Big data is an essential aspect of innovation which has recently gained major attention from both academics and practitioners. Considering the importance of the education sector, the current tendency is moving towards examining the role of big data in this sector. So far, many studies have been cond…

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information, especilly facts or numbers, collected to be examined and considered and used to help decision-making, or information electronic form that can be stored and used by a computer

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data definition: 1. information, especially facts or numbers, collected to be examined and considered and used to.... Learn more.

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https://www.solaresearch.org/about/what-is-learning-analytics/
LEARNING ANALYTICS is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs, as defined back in 2011 for the first LAK, this general definition still holds true even as the field has grown. Learning analytics is both an academic field and commercial marketplace which have taken rapid shape over the last decade. As a research and teaching field, Learning Analytics sits at the convergence of Learning (e.g. educational research, learning and assessment sciences, educational technology), Analytics (e.g. statistics, visualization, computer/data sciences, artificial intelligence), and Human-Centered Design (e.g. usability, participatory design, sociotechnical systems thinking).

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Society for Learning Analytics Research (SoLAR)

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LEARNING ANALYTICS is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs, as defined back in 2011 for the first LAK, this general definition still holds true even as the field has grown. Learning analytics is both an academic field and commercial marketplace which have taken rapid shape over the last decade. As a research and teaching field, Learning Analytics sits at the convergence of Learning (e.g. educational research, learning and assessment sciences, educational technology), Analytics (e.g. statistics, visualization, computer/data sciences, artificial intelligence), and Human-Centered Design (e.g. usability, participatory design, sociotechnical systems thinking).

SO WHAT'S ALL THE FUSS ABOUT? People have been researching learning and teaching, tracking student progress, analysing school or university data, designing assessments and using evidence to improve teaching and learning for a long time. Learning Analytics builds on these well established disciplines, but seeks to exploit the new opportunities once we capture new forms of digital data from students' learning activity, and use computational analysis techniques from data science and AI.

KEY USES. Historically, some of the most common uses of learning analytics is prediction of student academic success, and more specifically, the identification of students who are at risk of failing a course or dropping out of their studies. While it is reasonable that these two problems attracted a lot of attention, learning analytics are far more powerful. The evidence from research and practice shows that there are far more productive and potent ways of using analytics for supporting teaching and learning. Some of the most popular goal of learning analytics include:

1. Supporting student development of lifelong learning skills and strategies

2. Provision of personalised and timely feedback to students regarding their learning

3. Supporting development of important skills such as collaboration, critical thinking, communication and creativity

4. Develop student awareness by supporting self-reflection

5. Support quality learning and teaching by providing empirical evidence on the success of pedagogical innovations

METHODOLOGIES
Descriptive Analytics: insight into the past

Uses data aggregation and data mining to understand trends and evaluative metrics over time. The majority of statistics use falls into this category which is limited to past data and includes:

Student feedback gathered from student satisfaction and graduate surveys

Analysis of data at all stages of the student lifecycle starting from admissions process , to student orientation, enrolments, pastoral care, study support, exams and graduations.

Diagnostic Analytics: why did it happen

This form of advanced analytics is characterised by techniques such as drill-down, data discovery, data mining and correlations to examine data or content to answer the question &dash; "Why did it happen?" and includes:

Analysis of data to inform and uplift key performance indicators across the organization

Analysis of patterns to design appropriate metrics

Equity access reporting and analysis of effective strategies to support students

Learning management system metrics to improve student engagement

Predictive Analytics: understanding the future

Combines historical data to identify patterns in the data and applies statistical models and algorithms to capture relationships between various data sets to forecast trends and includes:

Development of Staff Dashboards to help predict student numbers and cohort mobility through programs to assist in identifying areas for improvement

Prescriptive Analytics: advise on possible outcomes

Goes beyond descriptive and predictive by recommending one or more choices using a combination of machine learning, algorithms, business rules and computational modelling such as:

Focusing on subject/courses where small changes could have a big impact on improving student engagement, feedback and outcomes

Data visualisation via specific tools to provide program/degree level metrics on student enrolments, program stage, results and survey feedback to give teaching staff visual snapshots of students in their programs

WHO BENEFITS? Learning Analytics provides researchers with exciting new tools to study teaching and learning. Moreover, as data infrastructures improve - from data capture and analysis, to visualization and recommendation - we can close the feedback loop to learners, offering more timely, precise, actionable feedback. In addition, educators, instructional designers and institutional leaders gain new insights once the learning process is persistent and visible.

ORGANIZATIONAL INFRASTRUCTURES. The community that SoLAR is fostering is distinctive in its interest in understanding the organizational systems required to introduce and sustain analytics. SoLAR's events and publications draw an eclectic audience that welcomes leaders and policy makers in education/business/government concerned with strategies for organizational change.

ETHICS OF DATA, ANALYTICS & AI. There is rightly much public and professional debate around the ethics of 'Big Data' and AI, including privacy, the problem of opaque 'black box' algorithms, the risk of training machine learning classifiers on biased datasets, and the dangers of incorrectly predicting someone's behaviour. These concerns are just as relevant in education, so the ethics of educational data, analytics and AI are front and center in SoLAR's work, with a very active stream in our events and publications.

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What is Learning Analytics? - Society for Learning Analytics Research (SoLAR)

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LEARNING ANALYTICS is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs, as defined back in 2011 for the first LAK, this general definition still holds true even as the field has grown.

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https://www.britannica.com/technology/artificial-intelligence
artificial intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.

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What is artificial intelligence?

Are artificial intelligence and machine learning the same?

artificial intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience. Since the development of the digital computer in the 1940s, it has been demonstrated that computers can be programmed to carry out very complex tasks-such as discovering proofs for mathematical theorems or playing chess-with great proficiency. Still, despite continuing advances in computer processing speed and memory capacity, there are as yet no programs that can match full human flexibility over wider domains or in tasks requiring much everyday knowledge. On the other hand, some programs have attained the performance levels of human experts and professionals in performing certain specific tasks, so that artificial intelligence in this limited sense is found in applications as diverse as medical diagnosis, computer search engines, voice or handwriting recognition, and chatbots.
(Read Ray Kurzweil's Britannica essay on the future of "Nonbiological Man.")

What is intelligence?

All but the simplest human behaviour is ascribed to intelligence, while even the most complicated insect behaviour is usually not taken as an indication of intelligence. What is the difference? Consider the behaviour of the digger wasp, Sphex ichneumoneus. When the female wasp returns to her burrow with food, she first deposits it on the threshold, checks for intruders inside her burrow, and only then, if the coast is clear, carries her food inside. The real nature of the wasp's instinctual behaviour is revealed if the food is moved a few inches away from the entrance to her burrow while she is inside: on emerging, she will repeat the whole procedure as often as the food is displaced. Intelligence-conspicuously absent in the case of Sphex -must include the ability to adapt to new circumstances.
(Read Yuval Noah Harari's Britannica essay on the future of "Nonconscious Man.")

Computers and Technology Quiz

Psychologists generally characterize human intelligence not by just one trait but by the combination of many diverse abilities. Research in AI has focused chiefly on the following components of intelligence: learning, reasoning, problem solving, perception, and using language.

There are a number of different forms of learning as applied to artificial intelligence. The simplest is learning by trial and error. For example, a simple computer program for solving mate-in-one chess problems might try moves at random until mate is found. The program might then store the solution with the position so that the next time the computer encountered the same position it would recall the solution. This simple memorizing of individual items and procedures-known as rote learning-is relatively easy to implement on a computer. More challenging is the problem of implementing what is called generalization. Generalization involves applying past experience to analogous new situations. For example, a program that learns the past tense of regular English verbs by rote will not be able to produce the past tense of a word such as jump unless it previously had been presented with jumped, whereas a program that is able to generalize can learn the "add ed" rule and so form the past tense of jump based on experience with similar verbs.

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Artificial intelligence (AI) | Definition, Examples, Types, Applications, Companies, & Facts

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Artificial intelligence, the ability of a computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems with the ability to reason, discover meaning, generalize, or learn from past experiences.

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https://www.oracle.com/big-data/what-is-big-data/#defined and then downloaded file about evolution of big data
Big data is a concept that describes data sets that exceed the size that can be managed by traditional tools. It is defined by “three Vs”: growing variety, arriving in increasing volumes and with more velocity, or the high rate at which data is received and acted on.

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Big data defined

What exactly is big data?

The definition of big data is data that contains greater variety, arriving in increasing volumes and with more velocity. This is also known as the three Vs.

Put simply, big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can't manage them. But these massive volumes of data can be used to address business problems you wouldn't have been able to tackle before.

The three Vs of big data

Volume

The amount of data matters. With big data, you'll have to process high volumes of low-density, unstructured data. This can be data of unknown value, such as Twitter data feeds, clickstreams on a web page or a mobile app, or sensor-enabled equipment. For some organizations, this might be tens of terabytes of data. For others, it may be hundreds of petabytes.

Velocity

Velocity is the fast rate at which data is received and (perhaps) acted on. Normally, the highest velocity of data streams directly into memory versus being written to disk. Some internet-enabled smart products operate in real time or near real time and will require real-time evaluation and action.

Variety

Variety refers to the many types of data that are available. Traditional data types were structured and fit neatly in a relational database. With the rise of big data, data comes in new unstructured data types. Unstructured and semistructured data types, such as text, audio, and video, require additional preprocessing to derive meaning and support metadata.

The value-and truth-of big data

Two more Vs have emerged over the past few years: value and veracity. Data has intrinsic value. But it's of no use until that value is discovered. Equally important: How truthful is your data-and how much can you rely on it?

Today, big data has become capital. Think of some of the world's biggest tech companies. A large part of the value they offer comes from their data, which they're constantly analyzing to produce more efficiency and develop new products.

Recent technological breakthroughs have exponentially reduced the cost of data storage and compute, making it easier and less expensive to store more data than ever before. With an increased volume of big data now cheaper and more accessible, you can make more accurate and precise business decisions.

Finding value in big data isn't only about analyzing it (which is a whole other benefit). It's an entire discovery process that requires insightful analysts, business users, and executives who ask the right questions, recognize patterns, make informed assumptions, and predict behavior.

But how did we get here?

The history of big data

Although the concept of big data itself is relatively new, the origins of large data sets go back to the 1960s and '70s when the world of data was just getting started with the first data centers and the development of the relational database.

Around 2005, people began to realize just how much data users generated through Facebook, YouTube, and other online services. Hadoop (an open-source framework created specifically to store and analyze big data sets) was developed that same year. NoSQL also began to gain popularity during this time.

The development of open-source frameworks, such as Hadoop (and more recently, Spark) was essential for the growth of big data because they make big data easier to work with and cheaper to store. In the years since then, the volume of big data has skyrocketed. Users are still generating huge amounts of data-but it's not just humans who are doing it.

With the advent of the Internet of Things (IoT), more objects and devices are connected to the internet, gathering data on customer usage patterns and product performance. The emergence of machine learning has produced still more data.

While big data has come far, its usefulness is only just beginning. Cloud computing has expanded big data possibilities even further. The cloud offers truly elastic scalability, where developers can simply spin up ad hoc clusters to test a subset of data. And graph databases are becoming increasingly important as well, with their ability to display massive amounts of data in a way that makes analytics fast and comprehensive.

Big data benefits:

Big data makes it possible for you to gain more complete answers because you have more information.

More complete answers mean more confidence in the data-which means a completely different approach to tackling problems.

Big data use cases

Big data can help you address a range of business activities, from customer experience to analytics. Here are just a few.

Product development

Companies like Netflix and Procter & Gamble use big data to anticipate customer demand. They build predictive models for new products and services by classifying key attributes of past and current products or services and modeling the relationship between those attributes and the commercial success of the offerings. In addition, P&G uses data and analytics from focus groups, social media, test markets, and early store rollouts to plan, produce, and launch new products.

Predictive maintenance

Factors that can predict mechanical failures may be deeply buried in structured data, such as the year, make, and model of equipment, as well as in unstructured data that covers millions of log entries, sensor data, error messages, and engine temperature. By analyzing these indications of potential issues before the problems happen, organizations can deploy maintenance more cost effectively and maximize parts and equipment uptime.

Customer experience

The race for customers is on. A clearer view of customer experience is more possible now than ever before. Big data enables you to gather data from social media, web visits, call logs, and other sources to improve the interaction experience and maximize the value delivered. Start delivering personalized offers, reduce customer churn, and handle issues proactively.

Fraud and compliance

When it comes to security, it's not just a few rogue hackers-you're up against entire expert teams. Security landscapes and compliance requirements are constantly evolving. Big data helps you identify patterns in data that indicate fraud and aggregate large volumes of information to make regulatory reporting much faster.

Machine learning

Machine learning is a hot topic right now. And data-specifically big data-is one of the reasons why. We are now able to teach machines instead of program them. The availability of big data to train machine learning models makes that possible.

Operational efficiency

Operational efficiency may not always make the news, but it's an area in which big data is having the most impact. With big data, you can analyze and assess production, customer feedback and returns, and other factors to reduce outages and anticipate future demands. Big data can also be used to improve decision-making in line with current market demand.

Drive innovation

Big data can help you innovate by studying interdependencies among humans, institutions, entities, and process and then determining new ways to use those insights. Use data insights to improve decisions about financial and planning considerations. Examine trends and what customers want to deliver new products and services. Implement dynamic pricing. There are endless possibilities.

Big data challenges

While big data holds a lot of promise, it is not without its challenges.

First, big data is...big. Although new technologies have been developed for data storage, data volumes are doubling in size about every two years. Organizations still struggle to keep pace with their data and find ways to effectively store it.

But it's not enough to just store the data. Data must be used to be valuable and that depends on curation. Clean data, or data that's relevant to the client and organized in a way that enables meaningful analysis, requires a lot of work. Data scientists spend 50 to 80 percent of their time curating and preparing data before it can actually be used.

Finally, big data technology is changing at a rapid pace. A few years ago, Apache Hadoop was the popular technology used to handle big data. Then Apache Spark was introduced in 2014. Today, a combination of the two frameworks appears to be the best approach. Keeping up with big data technology is an ongoing challenge.
Discover more big data resources:

How big data works

Big data gives you new insights that open up new opportunities and business models. Getting started involves three key actions:

1. Integrate
Big data brings together data from many disparate sources and applications. Traditional data integration mechanisms, such as extract, transform, and load (ETL) generally aren't up to the task. It requires new strategies and technologies to analyze big data sets at terabyte, or even petabyte, scale.

During integration, you need to bring in the data, process it, and make sure it's formatted and available in a form that your business analysts can get started with.

2. Manage
Big data requires storage. Your storage solution can be in the cloud, on premises, or both. You can store your data in any form you want and bring your desired processing requirements and necessary process engines to those data sets on an on-demand basis. Many people choose their storage solution according to where their data is currently residing. The cloud is gradually gaining popularity because it supports your current compute requirements and enables you to spin up resources as needed.

3. Analyze
Your investment in big data pays off when you analyze and act on your data. Get new clarity with a visual analysis of your varied data sets. Explore the data further to make new discoveries. Share your findings with others. Build data models with machine learning and artificial intelligence. Put your data to work.

Big data best practices

To help you on your big data journey, we've put together some key best practices for you to keep in mind. Here are our guidelines for building a successful big data foundation.

Align big data with specific business goals

More extensive data sets enable you to make new discoveries. To that end, it is important to base new investments in skills, organization, or infrastructure with a strong business-driven context to guarantee ongoing project investments and funding. To determine if you are on the right track, ask how big data supports and enables your top business and IT priorities. Examples include understanding how to filter web logs to understand ecommerce behavior, deriving sentiment from social media and customer support interactions, and understanding statistical correlation methods and their relevance for customer, product, manufacturing, and engineering data.

Ease skills shortage with standards and governance

One of the biggest obstacles to benefiting from your investment in big data is a skills shortage. You can mitigate this risk by ensuring that big data technologies, considerations, and decisions are added to your IT governance program. Standardizing your approach will allow you to manage costs and leverage resources. Organizations implementing big data solutions and strategies should assess their skill requirements early and often and should proactively identify any potential skill gaps. These can be addressed by training/cross-training existing resources, hiring new resources, and leveraging consulting firms.

Optimize knowledge transfer with a center of excellence

Use a center of excellence approach to share knowledge, control oversight, and manage project communications. Whether big data is a new or expanding investment, the soft and hard costs can be shared across the enterprise. Leveraging this approach can help increase big data capabilities and overall information architecture maturity in a more structured and systematic way.

Top payoff is aligning unstructured with structured data

It is certainly valuable to analyze big data on its own. But you can bring even greater business insights by connecting and integrating low density big data with the structured data you are already using today.

Whether you are capturing customer, product, equipment, or environmental big data, the goal is to add more relevant data points to your core master and analytical summaries, leading to better conclusions. For example, there is a difference in distinguishing all customer sentiment from that of only your best customers. Which is why many see big data as an integral extension of their existing business intelligence capabilities, data warehousing platform, and information architecture.

Keep in mind that the big data analytical processes and models can be both human- and machine-based. Big data analytical capabilities include statistics, spatial analysis, semantics, interactive discovery, and visualization. Using analytical models, you can correlate different types and sources of data to make associations and meaningful discoveries.

Plan your discovery lab for performance

Discovering meaning in your data is not always straightforward. Sometimes we don't even know what we're looking for. That's expected. Management and IT needs to support this "lack of direction" or "lack of clear requirement."

At the same time, it's important for analysts and data scientists to work closely with the business to understand key business knowledge gaps and requirements. To accommodate the interactive exploration of data and the experimentation of statistical algorithms, you need high-performance work areas. Be sure that sandbox environments have the support they need-and are properly governed.

Align with the cloud operating model

Big data processes and users require access to a broad array of resources for both iterative experimentation and running production jobs. A big data solution includes all data realms including transactions, master data, reference data, and summarized data. Analytical sandboxes should be created on demand. Resource management is critical to ensure control of the entire data flow including pre- and post-processing, integration, in-database summarization, and analytical modeling. A well-planned private and public cloud provisioning and security strategy plays an integral role in supporting these changing requirements.

Learn more about big data at Oracle

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Big data is larger, more complex data sets, especially from new data sources. Learn about the definition and history, in addition to big data benefits, challenges, and best practices.

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https://www.merriam-webster.com/dictionary/data

1: factual information (such as measurements or statistics) used as a basis for reasoning, discussion, or calculation

the data is plentiful and easily available
H. A. Gleason, Jr.
comprehensive data on economic growth have been published
N. H. Jacoby

2: information in digital form that can be transmitted or processed

3: information output by a sensing device or organ that includes both useful and irrelevant or redundant information and must be processed to be meaningful

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Data definition is - factual information (such as measurements or statistics) used as a basis for reasoning, discussion, or calculation. How to use data in a sentence. Is data singular or plural?

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Learning analytics is defined as “the measurement, collection, analysis, and reporting of data about learners, learning environments and contexts to understand and optimize learning and their environments” (Siemens and Gasevic, 2012).

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A date is synonymous with a piece of information. The word is mostly used in the plural (data, information). Strictly speaking, however, the two terms are different, data can come from a series of disparate and uncoordinated measurements or observations, while information is based on coordinated, organized, coherent data that usually also has meaning some. As a format data can be made up of numbers, letters, images and others.
https://ro.wikipedia.org/wiki/Dat%C4%83

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De la Wikipedia, enciclopedia liberă

Acest articol se referă la datele din domeniul informaticii. Pentru domeniul calendaristic, vedeți Dată (calendar).

De obicei o dată este sinonimă cu o informație. Cuvântul se folosește mai ales la plural (date, informații). Luați strict însă, cei doi termeni sunt diferiți, datele putând proveni de la un șir de măsurători sau observații disparate și necoordonate între ele, în timp ce informația se bazează pe date coordonate între ele, organizate, coerente, care au de obicei și un sens oarecare. Ca format datele pot fi alcătuite din numere, litere, imagini și altele.

Cuvântul „ dată" provine din cuvântul latin datum, care gramatical este participiul trecut al verbului latin dare, "a da". Încă din antichitate propozițiile sau faptele considerate evident adevărate erau numite "date".

Date experimentale sunt date generate special în cadrul unor cercetări științifice.

Datele inițiale neprelucrate (engl.: raw data, date brute) sunt, în cel mai larg sens, numere, litere, imagini și alte forme de date produse de aparate care convertesc mărimile fizice în simboluri. Aceste date sunt prelucrate apoi de oameni, sau pot fi și introduse în calculatoare cu scopul de a fi stocate și prelucrate acolo, sau și pentru a fi transmise la alte persoane și calculatoare. De multe ori prelucrarea datelor în calculator are loc în faze, fiecare fază predând datele ei deja prelucrate fazei următoare, pentru a fi prelucrate mai departe.

În calculatoarele actuale datele sunt reprezentate intern prin simboluri din cadrul unui " alfabet" de simboluri permise. Această reprezentare internă este de obicei numerică ( digitală). Cel mai des întâlnite sunt simbolurile "0" și "1" care, înșiruite după anumite sisteme în număr suficient de mare, pot reprezenta (în mod poate surprinzător) orice date și informații finite. Aceste 2 simboluri sau cifre constituie așa-numitul alfabet binar. Sistemul de numerație bazat pe ele se numește Sistem binar.

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De la Wikipedia, enciclopedia liberă Acest articol se referă la datele din domeniul informaticii. Pentru domeniul calendaristic, vedeți Dată (calendar). De obicei o dată este sinonimă cu o informație. Cuvântul se folosește mai ales la plural (date, informații).

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The term Big Data (big data, metadata) refers to the extraction, manipulation and analysis of data sets that are too large to be handled in the usual way.
https://ro.wikipedia.org/wiki/Big_data

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Artificial intelligence (AI) has the ability of systems or machines to imitate human intelligence as faithfully as possible, in order to solve problems and achieve the goal.
https://www.colorful.hr

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Inteligenta artificiala (IA) reprezinta capacitatea sistemelor sau masinilor de a imita cat mai fidel inteligenta umana, pentru a rezolva probleme si pentru a atinge obiective. Sistemele de Inteligenta Artificiala se adapteaza, analizeaza date, prevad viitoare actiuni pe baza informatiilor existente si functioneaza autonom. Mai jos, poti afla mai multe despre modul de functionare al Inteligentei Artificiale, cum este folosita in viata de zi cu zi si pe piata muncii, precum si care sunt avantajele si provocarile pe care le aduce.

Importanta Inteligentei Artificiale

Inteligenta artificiala este cu mult mai importanta astazi, fata de momentul aparitiei sale, in urma cu peste 50 de ani. In zilele noastre, puterea de calcul a crescut exponential, exista cantitati enorme de date, iar algoritmii au dus la progrese majore in inteligenta artificiala a ultimilor ani.

Viteza si gradul de adaptare fac din inteligenta artificiala un instrument care poate fi implementat indiferent de domeniu. Dovezile succesului sau stau in productivitatea crescuta din industrie, precum si in avansul din domeniul medical.

Cum functioneaza Inteligenta Artificiala

Inteligenta artificiala are un sistem de functionare asemanator omului. Invata lucruri noi, le aplica si se adapteaza in functie de conditiile din jur. Mai jos sunt cateva elemente-cheie ale modului de functionare al inteligentei artificiale:

Tipuri de Inteligenta Artificiala

In linii mari, inteligenta artificiala este de doua tipuri: software si incorporata. Inteligenta artificiala software include asistenti virtuali , motoare de cautare, sisteme de recunoastere faciala si vocala, precum si programe informatice de analiza a imaginilor.

Inteligenta artificiala incorporata include roboti, drone, masini autonome si internetul obiectelor.

Totodata, in functie de performanta, puterea de procesare si capacitatea de a realiza anumite sarcini, inteligenta artificiala este de patru tipuri:

Masina reactiva - are o capacitate restransa de a realiza sarcini umane. Poate raspunde la anumite evenimente, insa nu se poate folosi de experientele anterioare pentru a realiza sarcini. Masinile reactive sunt considerate sisteme primitive de inteligenta artificiala;

Memoria limitata - poate invata din experientele anterioare, pentru a lua decizii. Aproape toate sistemele de IA intra in aceasta categorie, folosind invatarea masina si invatarea profunda, pentru a extrage informatiile esentiale din depozitele de date;

Teoria mintii - este un domeniu in plina expansiune si inovatie. Reprezinta o forma avansata de inteligenta artificiala. Intelege informatiile si oamenii prin analiza datelor, a nevoilor, credintelor si emotiilor;

Constiinta de sine - este una dintre tipurile de IA care exista doar teoretic, in prezent. Are drept obiectiv imitarea cat mai fidela a creierului uman. Se crede ca vor trece cateva decenii, pentru a construi un sistem de acest fel. Poate recunoaste, isi poate aminti emotii si poate lua decizii. Doua exemple de acest fel sunt inteligenta artificiala generala (AGI) si superinteligenta artificiala (ASI). Expertii cred ca ar putea face omul redundant.

Aplicabilitatea in viata de zi cu zi

Inteligenta artificiala este tot mai folosita, inclusiv in viata de zi cu zi. Mai jos sunt cateva dintre aplicatiile sale, care ne fac viata mai usoara:

Utilizarea inteligentei artificiale pe piata muncii

Incet-incet, inteligenta artificiala transforma majoritatea industriilor. Mai jos sunt cateva exemple edificatoare:

Avantajele Inteligentei Artificiale

Inteligenta artificiala aduce cu sine numeroase avantaje. Mai jos sunt cele mai importante dintre ele:

Citeste si: Trei moduri in care Inteligenta Artificiala a schimbat locul de munca

Provocarile Inteligentei Artificiale

Impactul inteligentei artificiale a fost imens in ultimii ani, iar expertii spun ca acest fenomen va aduce in economia globala 15,7 mii de miliarde de dolari, pana in 2030. In cadrul companiilor, folosirea de IA poate creste productivitatea cu pana la 40%, iar datele arata ca numarul startup-urilor din domeniul inteligentei artificiale a crescut de 14 ori, din 2000. Totusi, in ciuda acestor cifre optimiste, exista si o serie de provocari care vor trebui solutionate in anii ce urmeaza. Acestea includ:

Citeste si: Inteligenta Artificiala reinventeaza HR-ul. Cum poate AI sa optimizeze munca profesionistilor in resurse umane si a angajatilor
Masinile autonome si chiar zburatoare probabil ca vor fi o realitate, in cativa ani. In prezent, sistemele inteligente realizeaza sarcini mai mult sau mai putin complexe, in majoritatea domeniilor, de la sanatate si industrie la resurse umane, educatie si transport. Speranta este ca eliminarea unor sarcini de rutina si monotone ne va elibera timpul, pentru a ne concentra asupra lucrurilor care conteaza atat in viata profesionala, cat si in cea personala.
Sursa Foto: Shutterstock

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Inteligenta artificiala: ce este si cum ajuta piata muncii?

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Inteligenta artificiala (IA) reprezinta capacitatea sistemelor sau masinilor de a imita cat mai fidel inteligenta umana, pentru a rezolva probleme si pentru a atinge obiective. Sistemele de Inteligenta Artificiala se adapteaza, analizeaza date, prevad viitoare actiuni pe baza informatiilor existente si functioneaza autonom.

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It is the science and engineering of making intelligent machines, specially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable - John McCarthy et.al (2004)

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Sets of information that are too large or too complex to handle, analyse or use with standard methods - Oxford Dictionary Online.

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Facts or information, specially when examined and used to find out things or make decisions - Oxford Dictionary Online.

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Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.[1] The growth of online learning since the 1990s, particularly in higher education, has contributed to the advancement of Learning Analytics as student data can be captured and made available for analysis.[2][3][4] When learners use an LMS, social media, or similar online tools, their clicks, navigation patterns, time on task, social networks, information flow, and concept development through discussions can be tracked. The rapid development of massive open online courses (MOOCs) offers additional data for researchers to evaluate teaching and learning in online environments.[5]

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The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

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Extremely large data sets that may be analysed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interactions.
"much IT investment is going towards managing and maintaining big data"

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Facts and statistics collected together for reference or analysis

The quantities, characters, or symbols on which operations are performed by a computer, which may be stored and transmitted in the form of electrical signals and recorded on magnetic, optical, or mechanical recording media.

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Learning Analytics
is a measurement and analysis about learners and their contexts to understand and optimise learning and the environments.https://www.solaresearch.org/about/what-is-learning-analytics/

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Society for Learning Analytics Research (SoLAR)

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LEARNING ANALYTICS is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs, as defined back in 2011 for the first LAK, this general definition still holds true even as the field has grown. Learning analytics is both an academic field and commercial marketplace which have taken rapid shape over the last decade. As a research and teaching field, Learning Analytics sits at the convergence of Learning (e.g. educational research, learning and assessment sciences, educational technology), Analytics (e.g. statistics, visualization, computer/data sciences, artificial intelligence), and Human-Centered Design (e.g. usability, participatory design, sociotechnical systems thinking).

SO WHAT'S ALL THE FUSS ABOUT? People have been researching learning and teaching, tracking student progress, analysing school or university data, designing assessments and using evidence to improve teaching and learning for a long time. Learning Analytics builds on these well established disciplines, but seeks to exploit the new opportunities once we capture new forms of digital data from students' learning activity, and use computational analysis techniques from data science and AI.

KEY USES. Historically, some of the most common uses of learning analytics is prediction of student academic success, and more specifically, the identification of students who are at risk of failing a course or dropping out of their studies. While it is reasonable that these two problems attracted a lot of attention, learning analytics are far more powerful. The evidence from research and practice shows that there are far more productive and potent ways of using analytics for supporting teaching and learning. Some of the most popular goal of learning analytics include:

1. Supporting student development of lifelong learning skills and strategies

2. Provision of personalised and timely feedback to students regarding their learning

3. Supporting development of important skills such as collaboration, critical thinking, communication and creativity

4. Develop student awareness by supporting self-reflection

5. Support quality learning and teaching by providing empirical evidence on the success of pedagogical innovations

METHODOLOGIES
Descriptive Analytics: insight into the past

Uses data aggregation and data mining to understand trends and evaluative metrics over time. The majority of statistics use falls into this category which is limited to past data and includes:

Student feedback gathered from student satisfaction and graduate surveys

Analysis of data at all stages of the student lifecycle starting from admissions process , to student orientation, enrolments, pastoral care, study support, exams and graduations.

Diagnostic Analytics: why did it happen

This form of advanced analytics is characterised by techniques such as drill-down, data discovery, data mining and correlations to examine data or content to answer the question &dash; "Why did it happen?" and includes:

Analysis of data to inform and uplift key performance indicators across the organization

Analysis of patterns to design appropriate metrics

Equity access reporting and analysis of effective strategies to support students

Learning management system metrics to improve student engagement

Predictive Analytics: understanding the future

Combines historical data to identify patterns in the data and applies statistical models and algorithms to capture relationships between various data sets to forecast trends and includes:

Development of Staff Dashboards to help predict student numbers and cohort mobility through programs to assist in identifying areas for improvement

Prescriptive Analytics: advise on possible outcomes

Goes beyond descriptive and predictive by recommending one or more choices using a combination of machine learning, algorithms, business rules and computational modelling such as:

Focusing on subject/courses where small changes could have a big impact on improving student engagement, feedback and outcomes

Data visualisation via specific tools to provide program/degree level metrics on student enrolments, program stage, results and survey feedback to give teaching staff visual snapshots of students in their programs

WHO BENEFITS? Learning Analytics provides researchers with exciting new tools to study teaching and learning. Moreover, as data infrastructures improve - from data capture and analysis, to visualization and recommendation - we can close the feedback loop to learners, offering more timely, precise, actionable feedback. In addition, educators, instructional designers and institutional leaders gain new insights once the learning process is persistent and visible.

ORGANIZATIONAL INFRASTRUCTURES. The community that SoLAR is fostering is distinctive in its interest in understanding the organizational systems required to introduce and sustain analytics. SoLAR's events and publications draw an eclectic audience that welcomes leaders and policy makers in education/business/government concerned with strategies for organizational change.

ETHICS OF DATA, ANALYTICS & AI. There is rightly much public and professional debate around the ethics of 'Big Data' and AI, including privacy, the problem of opaque 'black box' algorithms, the risk of training machine learning classifiers on biased datasets, and the dangers of incorrectly predicting someone's behaviour. These concerns are just as relevant in education, so the ethics of educational data, analytics and AI are front and center in SoLAR's work, with a very active stream in our events and publications.

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What is Learning Analytics? - Society for Learning Analytics Research (SoLAR)

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LEARNING ANALYTICS is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs, as defined back in 2011 for the first LAK, this general definition still holds true even as the field has grown.

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Artificial intellience
is described as any task performed by amachine considered to require human intelligence before.To modern definitions,it is tied to a system's ability to adopt and improvise in a new environment.https://www.zdnet.com/article/what-is-ai-heres-everything-you-need-to-know-about-artificial-intelligence/

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What is AI? Everything to know about artificial intelligence

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If you want to know about the fascinating and fast-developing technologies of artificial intelligence, we cover everything from machine learning and general AI to neural networks.

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Big data is a large volume of data.  big data described as large data sets that are difficult to process, control or examine in a traditional way. With the advent of big data, now teachers can access student’s academic performance, learning patterns and provide instant feedback.
https://educationaltechnologyjournal.springeropen.com/articles/10.1186/s41239-020-00223-0

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Big Data is
essential aspect of innovation.It is also larger and more complex data so is provides more complete answers because of more info. so this means more confidence.

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Big data defined

What exactly is big data?

The definition of big data is data that contains greater variety, arriving in increasing volumes and with more velocity. This is also known as the three Vs.

Put simply, big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can't manage them. But these massive volumes of data can be used to address business problems you wouldn't have been able to tackle before.

The three Vs of big data

Volume

The amount of data matters. With big data, you'll have to process high volumes of low-density, unstructured data. This can be data of unknown value, such as Twitter data feeds, clickstreams on a web page or a mobile app, or sensor-enabled equipment. For some organizations, this might be tens of terabytes of data. For others, it may be hundreds of petabytes.

Velocity

Velocity is the fast rate at which data is received and (perhaps) acted on. Normally, the highest velocity of data streams directly into memory versus being written to disk. Some internet-enabled smart products operate in real time or near real time and will require real-time evaluation and action.

Variety

Variety refers to the many types of data that are available. Traditional data types were structured and fit neatly in a relational database. With the rise of big data, data comes in new unstructured data types. Unstructured and semistructured data types, such as text, audio, and video, require additional preprocessing to derive meaning and support metadata.

The value-and truth-of big data

Two more Vs have emerged over the past few years: value and veracity. Data has intrinsic value. But it's of no use until that value is discovered. Equally important: How truthful is your data-and how much can you rely on it?

Today, big data has become capital. Think of some of the world's biggest tech companies. A large part of the value they offer comes from their data, which they're constantly analyzing to produce more efficiency and develop new products.

Recent technological breakthroughs have exponentially reduced the cost of data storage and compute, making it easier and less expensive to store more data than ever before. With an increased volume of big data now cheaper and more accessible, you can make more accurate and precise business decisions.

Finding value in big data isn't only about analyzing it (which is a whole other benefit). It's an entire discovery process that requires insightful analysts, business users, and executives who ask the right questions, recognize patterns, make informed assumptions, and predict behavior.

But how did we get here?

The history of big data

Although the concept of big data itself is relatively new, the origins of large data sets go back to the 1960s and '70s when the world of data was just getting started with the first data centers and the development of the relational database.

Around 2005, people began to realize just how much data users generated through Facebook, YouTube, and other online services. Hadoop (an open-source framework created specifically to store and analyze big data sets) was developed that same year. NoSQL also began to gain popularity during this time.

The development of open-source frameworks, such as Hadoop (and more recently, Spark) was essential for the growth of big data because they make big data easier to work with and cheaper to store. In the years since then, the volume of big data has skyrocketed. Users are still generating huge amounts of data-but it's not just humans who are doing it.

With the advent of the Internet of Things (IoT), more objects and devices are connected to the internet, gathering data on customer usage patterns and product performance. The emergence of machine learning has produced still more data.

While big data has come far, its usefulness is only just beginning. Cloud computing has expanded big data possibilities even further. The cloud offers truly elastic scalability, where developers can simply spin up ad hoc clusters to test a subset of data. And graph databases are becoming increasingly important as well, with their ability to display massive amounts of data in a way that makes analytics fast and comprehensive.

Big data benefits:

Big data makes it possible for you to gain more complete answers because you have more information.

More complete answers mean more confidence in the data-which means a completely different approach to tackling problems.

Big data use cases

Big data can help you address a range of business activities, from customer experience to analytics. Here are just a few.

Product development

Companies like Netflix and Procter & Gamble use big data to anticipate customer demand. They build predictive models for new products and services by classifying key attributes of past and current products or services and modeling the relationship between those attributes and the commercial success of the offerings. In addition, P&G uses data and analytics from focus groups, social media, test markets, and early store rollouts to plan, produce, and launch new products.

Predictive maintenance

Factors that can predict mechanical failures may be deeply buried in structured data, such as the year, make, and model of equipment, as well as in unstructured data that covers millions of log entries, sensor data, error messages, and engine temperature. By analyzing these indications of potential issues before the problems happen, organizations can deploy maintenance more cost effectively and maximize parts and equipment uptime.

Customer experience

The race for customers is on. A clearer view of customer experience is more possible now than ever before. Big data enables you to gather data from social media, web visits, call logs, and other sources to improve the interaction experience and maximize the value delivered. Start delivering personalized offers, reduce customer churn, and handle issues proactively.

Fraud and compliance

When it comes to security, it's not just a few rogue hackers-you're up against entire expert teams. Security landscapes and compliance requirements are constantly evolving. Big data helps you identify patterns in data that indicate fraud and aggregate large volumes of information to make regulatory reporting much faster.

Machine learning

Machine learning is a hot topic right now. And data-specifically big data-is one of the reasons why. We are now able to teach machines instead of program them. The availability of big data to train machine learning models makes that possible.

Operational efficiency

Operational efficiency may not always make the news, but it's an area in which big data is having the most impact. With big data, you can analyze and assess production, customer feedback and returns, and other factors to reduce outages and anticipate future demands. Big data can also be used to improve decision-making in line with current market demand.

Drive innovation

Big data can help you innovate by studying interdependencies among humans, institutions, entities, and process and then determining new ways to use those insights. Use data insights to improve decisions about financial and planning considerations. Examine trends and what customers want to deliver new products and services. Implement dynamic pricing. There are endless possibilities.

Big data challenges

While big data holds a lot of promise, it is not without its challenges.

First, big data is...big. Although new technologies have been developed for data storage, data volumes are doubling in size about every two years. Organizations still struggle to keep pace with their data and find ways to effectively store it.

But it's not enough to just store the data. Data must be used to be valuable and that depends on curation. Clean data, or data that's relevant to the client and organized in a way that enables meaningful analysis, requires a lot of work. Data scientists spend 50 to 80 percent of their time curating and preparing data before it can actually be used.

Finally, big data technology is changing at a rapid pace. A few years ago, Apache Hadoop was the popular technology used to handle big data. Then Apache Spark was introduced in 2014. Today, a combination of the two frameworks appears to be the best approach. Keeping up with big data technology is an ongoing challenge.
Discover more big data resources:

How big data works

Big data gives you new insights that open up new opportunities and business models. Getting started involves three key actions:

1. Integrate
Big data brings together data from many disparate sources and applications. Traditional data integration mechanisms, such as extract, transform, and load (ETL) generally aren't up to the task. It requires new strategies and technologies to analyze big data sets at terabyte, or even petabyte, scale.

During integration, you need to bring in the data, process it, and make sure it's formatted and available in a form that your business analysts can get started with.

2. Manage
Big data requires storage. Your storage solution can be in the cloud, on premises, or both. You can store your data in any form you want and bring your desired processing requirements and necessary process engines to those data sets on an on-demand basis. Many people choose their storage solution according to where their data is currently residing. The cloud is gradually gaining popularity because it supports your current compute requirements and enables you to spin up resources as needed.

3. Analyze
Your investment in big data pays off when you analyze and act on your data. Get new clarity with a visual analysis of your varied data sets. Explore the data further to make new discoveries. Share your findings with others. Build data models with machine learning and artificial intelligence. Put your data to work.

Big data best practices

To help you on your big data journey, we've put together some key best practices for you to keep in mind. Here are our guidelines for building a successful big data foundation.

Align big data with specific business goals

More extensive data sets enable you to make new discoveries. To that end, it is important to base new investments in skills, organization, or infrastructure with a strong business-driven context to guarantee ongoing project investments and funding. To determine if you are on the right track, ask how big data supports and enables your top business and IT priorities. Examples include understanding how to filter web logs to understand ecommerce behavior, deriving sentiment from social media and customer support interactions, and understanding statistical correlation methods and their relevance for customer, product, manufacturing, and engineering data.

Ease skills shortage with standards and governance

One of the biggest obstacles to benefiting from your investment in big data is a skills shortage. You can mitigate this risk by ensuring that big data technologies, considerations, and decisions are added to your IT governance program. Standardizing your approach will allow you to manage costs and leverage resources. Organizations implementing big data solutions and strategies should assess their skill requirements early and often and should proactively identify any potential skill gaps. These can be addressed by training/cross-training existing resources, hiring new resources, and leveraging consulting firms.

Optimize knowledge transfer with a center of excellence

Use a center of excellence approach to share knowledge, control oversight, and manage project communications. Whether big data is a new or expanding investment, the soft and hard costs can be shared across the enterprise. Leveraging this approach can help increase big data capabilities and overall information architecture maturity in a more structured and systematic way.

Top payoff is aligning unstructured with structured data

It is certainly valuable to analyze big data on its own. But you can bring even greater business insights by connecting and integrating low density big data with the structured data you are already using today.

Whether you are capturing customer, product, equipment, or environmental big data, the goal is to add more relevant data points to your core master and analytical summaries, leading to better conclusions. For example, there is a difference in distinguishing all customer sentiment from that of only your best customers. Which is why many see big data as an integral extension of their existing business intelligence capabilities, data warehousing platform, and information architecture.

Keep in mind that the big data analytical processes and models can be both human- and machine-based. Big data analytical capabilities include statistics, spatial analysis, semantics, interactive discovery, and visualization. Using analytical models, you can correlate different types and sources of data to make associations and meaningful discoveries.

Plan your discovery lab for performance

Discovering meaning in your data is not always straightforward. Sometimes we don't even know what we're looking for. That's expected. Management and IT needs to support this "lack of direction" or "lack of clear requirement."

At the same time, it's important for analysts and data scientists to work closely with the business to understand key business knowledge gaps and requirements. To accommodate the interactive exploration of data and the experimentation of statistical algorithms, you need high-performance work areas. Be sure that sandbox environments have the support they need-and are properly governed.

Align with the cloud operating model

Big data processes and users require access to a broad array of resources for both iterative experimentation and running production jobs. A big data solution includes all data realms including transactions, master data, reference data, and summarized data. Analytical sandboxes should be created on demand. Resource management is critical to ensure control of the entire data flow including pre- and post-processing, integration, in-database summarization, and analytical modeling. A well-planned private and public cloud provisioning and security strategy plays an integral role in supporting these changing requirements.

Learn more about big data at Oracle

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Big data is larger, more complex data sets, especially from new data sources. Learn about the definition and history, in addition to big data benefits, challenges, and best practices.

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Data is information that has been translated into a form for movement and processing.It is represented including videos,sounds or texts ... and also it is different type of information.

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By

In computing, data is information that has been translated into a form that is efficient for movement or processing. Relative to today's computers and transmission media, data is information converted into binary digital form. It is acceptable for data to be used as a singular subject or a plural subject. Raw data is a term used to describe data in its most basic digital format.

The concept of data in the context of computing has its roots in the work of Claude Shannon, an American mathematician known as the father of information theory. He ushered in binary digital concepts based on applying two-value Boolean logic to electronic circuits. Binary digit formats underlie the CPUs, semiconductor memories and disk drives, as well as many of the peripheral devices common in computing today. Early computer input for both control and data took the form of punch cards, followed by magnetic tape and the hard disk.

Early on, data's importance in business computing became apparent by the popularity of the terms "data processing" and "electronic data processing," which, for a time, came to encompass the full gamut of what is now known as information technology. Over the history of corporate computing, specialization occurred, and a distinct data profession emerged along with growth of corporate data processing.

How data is stored

The range of digital data over time has grown from bits and bytes to brontobytes, with yet bigger data measure to come.

Types of data

Growth of the web and smartphones over the past decade led to a surge in digital data creation. Data now includes text, audio and video information, as well as log and web activity records. Much of that is unstructured data.

The term big data has been used to describe data in the petabyte range or larger. A shorthand take depicts big data with 3Vs -- volume, variety and velocity. As web-based e-commerce has spread, big data-driven business models have evolved which treat data as an asset in itself. Such trends have also spawned greater preoccupation with the social uses of data and data privacy.

Data has meaning beyond its use in computing applications oriented toward data processing. For example, in electronic component interconnection and network communication, the term data is often distinguished from "control information," "control bits," and similar terms to identify the main content of a transmission unit. Moreover, in science, the term data is used to describe a gathered body of facts. That is also the case in fields such as finance, marketing, demographics and health.

Data management and use

With the proliferation of data in organizations, added emphasis has been placed on ensuring data quality by reducing duplication and guaranteeing the most accurate, current records are used. The many steps involved with modern data management include data cleansing, as well as extract, transform and load (ETL) processes for integrating data. Data for processing has come to be complemented by metadata, sometimes referred to as "data about data," that helps administrators and users understand database and other data.

Analytics that combine structured and unstructured data have become useful, as organizations seek to capitalize on such information. Systems for such analytics increasingly strive for real-time performance, so they are built to handle incoming data consumed at high ingestion rates, and to process data streams for immediate use in operations.

Data professionals

The database administrator profession is an offshoot of IT. These database experts work on designing, tuning and maintaining the database.

Today, companies employ data management professionals or assign workers the role of data stewardship, which involves carrying out data usage and security policies as outlined in data governance initiatives.

A distinct title -- the data scientist -- has appeared to describe professionals focused on data mining and analysis. The benefit of presenting data science in an evocative manner has even given rise to the data artist; that is, an individual adept at graphing and visualizing data in creative ways.

Next Steps

Learn how to protect sensitive data by creating a data flow map.

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What is Data? - Definition from WhatIs.com

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This definition explains the meaning of data, which is information that has been translated into a form that supports efficient movement or processing.

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Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.

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AI - Artificial intelligence is intelligence - perceiving, synthesizing, and infering information - demonstrated by machines, as opposed to intelligence displayed by animals and humans

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The term big data has been used to describe data in the petabyte range or larger

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data is information that has been translated into a form that is efficient for movement or processing. Relative to today's computers and transmission media, data is information converted into binary digital form.
https://www.techtarget.com/searchdatamanagement/definition/big-data

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What is big data?

Big data is a combination of structured, semistructured and unstructured data collected by organizations that can be mined for information and used in machine learning projects, predictive modeling and other advanced analytics applications.

Systems that process and store big data have become a common component of data management architectures in organizations, combined with tools that support big data analytics uses. Big data is often characterized by the three V's:

the large volume of data in many environments;

the wide variety of data types frequently stored in big data systems; and

the velocity at which much of the data is generated, collected and processed.

These characteristics were first identified in 2001 by Doug Laney, then an analyst at consulting firm Meta Group Inc.; Gartner further popularized them after it acquired Meta Group in 2005. More recently, several other V's have been added to different descriptions of big data, including veracity, value and variability.

Although big data doesn't equate to any specific volume of data, big data deployments often involve terabytes, petabytes and even exabytes of data created and collected over time.

Why is big data important?

Companies use big data in their systems to improve operations, provide better customer service, create personalized marketing campaigns and take other actions that, ultimately, can increase revenue and profits. Businesses that use it effectively hold a potential competitive advantage over those that don't because they're able to make faster and more informed business decisions.

For example, big data provides valuable insights into customers that companies can use to refine their marketing, advertising and promotions in order to increase customer engagement and conversion rates. Both historical and real-time data can be analyzed to assess the evolving preferences of consumers or corporate buyers, enabling businesses to become more responsive to customer wants and needs.

Big data is also used by medical researchers to identify disease signs and risk factors and by doctors to help diagnose illnesses and medical conditions in patients. In addition, a combination of data from electronic health records, social media sites, the web and other sources gives healthcare organizations and government agencies up-to-date information on infectious disease threats or outbreaks.

Here are some more examples of how big data is used by organizations:

In the energy industry, big data helps oil and gas companies identify potential drilling locations and monitor pipeline operations; likewise, utilities use it to track electrical grids.

Financial services firms use big data systems for risk management and real-time analysis of market data.

Manufacturers and transportation companies rely on big data to manage their supply chains and optimize delivery routes.

Other government uses include emergency response, crime prevention and smart city initiatives.

These are some of the business benefits organizations can get by using big data.

What are examples of big data?

Big data comes from myriad sources -- some examples are transaction processing systems, customer databases, documents, emails, medical records, internet clickstream logs, mobile apps and social networks. It also includes machine-generated data, such as network and server log files and data from sensors on manufacturing machines, industrial equipment and internet of things devices.

In addition to data from internal systems, big data environments often incorporate external data on consumers, financial markets, weather and traffic conditions, geographic information, scientific research and more. Images, videos and audio files are forms of big data, too, and many big data applications involve streaming data that is processed and collected on a continual basis.

Breaking down the V's of big data

Volume is the most commonly cited characteristic of big data. A big data environment doesn't have to contain a large amount of data, but most do because of the nature of the data being collected and stored in them. Clickstreams, system logs and stream processing systems are among the sources that typically produce massive volumes of data on an ongoing basis.

Big data also encompasses a wide variety of data types, including the following:

structured data, such as transactions and financial records;

unstructured data, such as text, documents and multimedia files; and

semistructured data, such as web server logs and streaming data from sensors.

Various data types may need to be stored and managed together in big data systems. In addition, big data applications often include multiple data sets that may not be integrated upfront. For example, a big data analytics project may attempt to forecast sales of a product by correlating data on past sales, returns, online reviews and customer service calls.

Velocity refers to the speed at which data is generated and must be processed and analyzed. In many cases, sets of big data are updated on a real- or near-real-time basis, instead of the daily, weekly or monthly updates made in many traditional data warehouses. Managing data velocity is also important as big data analysis further expands into machine learning and artificial intelligence ( AI), where analytical processes automatically find patterns in data and use them to generate insights.

More characteristics of big data

Looking beyond the original three V's, here are details on some of the other ones that are now often associated with big data:

Veracity refers to the degree of accuracy in data sets and how trustworthy they are. Raw data collected from various sources can cause data quality issues that may be difficult to pinpoint. If they aren't fixed through data cleansing processes, bad data leads to analysis errors that can undermine the value of business analytics initiatives. Data management and analytics teams also need to ensure that they have enough accurate data available to produce valid results.

Some data scientists and consultants also add value to the list of big data's characteristics. Not all the data that's collected has real business value or benefits. As a result, organizations need to confirm that data relates to relevant business issues before it's used in big data analytics projects.

Variability also often applies to sets of big data, which may have multiple meanings or be formatted differently in separate data sources -- factors that further complicate big data management and analytics.

Some people ascribe even more V's to big data; various lists have been created with between seven and 10.

The characteristics of big data are commonly described by using words that begin with 'v,' including these six.

How is big data stored and processed?

Big data is often stored in a data lake. While data warehouses are commonly built on relational databases and contain structured data only, data lakes can support various data types and typically are based on Hadoop clusters, cloud object storage services, NoSQL databases or other big data platforms.

Many big data environments combine multiple systems in a distributed architecture; for example, a central data lake might be integrated with other platforms, including relational databases or a data warehouse. The data in big data systems may be left in its raw form and then filtered and organized as needed for particular analytics uses. In other cases, it's preprocessed using data mining tools and data preparation software so it's ready for applications that are run regularly.

Big data processing places heavy demands on the underlying compute infrastructure. The required computing power often is provided by clustered systems that distribute processing workloads across hundreds or thousands of commodity servers, using technologies like Hadoop and the Spark processing engine.

Getting that kind of processing capacity in a cost-effective way is a challenge. As a result, the cloud is a popular location for big data systems. Organizations can deploy their own cloud-based systems or use managed big-data-as-a-service offerings from cloud providers. Cloud users can scale up the required number of servers just long enough to complete big data analytics projects. The business only pays for the storage and compute time it uses, and the cloud instances can be turned off until they're needed again.

How big data analytics works

To get valid and relevant results from big data analytics applications, data scientists and other data analysts must have a detailed understanding of the available data and a sense of what they're looking for in it. That makes data preparation, which includes profiling, cleansing, validation and transformation of data sets, a crucial first step in the analytics process.

Once the data has been gathered and prepared for analysis, various data science and advanced analytics disciplines can be applied to run different applications, using tools that provide big data analytics features and capabilities. Those disciplines include machine learning and its deep learning offshoot, predictive modeling, data mining, statistical analysis, streaming analytics, text mining and more.

Using customer data as an example, the different branches of analytics that can be done with sets of big data include the following:

Comparative analysis. This examines customer behavior metrics and real-time customer engagement in order to compare a company's products, services and branding with those of its competitors.

Social media listening. This analyzes what people are saying on social media about a business or product, which can help identify potential problems and target audiences for marketing campaigns.

Marketing analytics. This provides information that can be used to improve marketing campaigns and promotional offers for products, services and business initiatives.

Sentiment analysis. All of the data that's gathered on customers can be analyzed to reveal how they feel about a company or brand, customer satisfaction levels, potential issues and how customer service could be improved.

Big data management technologies

Hadoop, an open source distributed processing framework released in 2006, initially was at the center of most big data architectures. The development of Spark and other processing engines pushed MapReduce, the engine built into Hadoop, more to the side. The result is an ecosystem of big data technologies that can be used for different applications but often are deployed together.

Big data platforms and managed services offered by IT vendors combine many of those technologies in a single package, primarily for use in the cloud. Currently, that includes these offerings, listed alphabetically:

Amazon EMR (formerly Elastic MapReduce)

Cloudera Data Platform

Google Cloud Dataproc

HPE Ezmeral Data Fabric (formerly MapR Data Platform)

Microsoft Azure HDInsight

For organizations that want to deploy big data systems themselves, either on premises or in the cloud, the technologies that are available to them in addition to Hadoop and Spark include the following categories of tools:

storage repositories, such as the Hadoop Distributed File System (HDFS) and cloud object storage services that include Amazon Simple Storage Service (S3), Google Cloud Storage and Azure Blob Storage;

cluster management frameworks, like Kubernetes, Mesos and YARN, Hadoop's built-in resource manager and job scheduler, which stands for Yet Another Resource Negotiator but is commonly known by the acronym alone;

stream processing engines, such as Flink, Hudi, Kafka, Samza, Storm and the Spark Streaming and Structured Streaming modules built into Spark;

NoSQL databases that include Cassandra, Couchbase, CouchDB, HBase, MarkLogic Data Hub, MongoDB, Neo4j, Redis and various other technologies;

data lake and data warehouse platforms, among them Amazon Redshift, Delta Lake, Google BigQuery, Kylin and Snowflake; and

SQL query engines, like Drill, Hive, Impala, Presto and Trino.

Big data challenges

In connection with the processing capacity issues, designing a big data architecture is a common challenge for users. Big data systems must be tailored to an organization's particular needs, a DIY undertaking that requires IT and data management teams to piece together a customized set of technologies and tools. Deploying and managing big data systems also require new skills compared to the ones that database administrators and developers focused on relational software typically possess.

Both of those issues can be eased by using a managed cloud service, but IT managers need to keep a close eye on cloud usage to make sure costs don't get out of hand. Also, migrating on-premises data sets and processing workloads to the cloud is often a complex process.

Other challenges in managing big data systems include making the data accessible to data scientists and analysts, especially in distributed environments that include a mix of different platforms and data stores. To help analysts find relevant data, data management and analytics teams are increasingly building data catalogs that incorporate metadata management and data lineage functions. The process of integrating sets of big data is often also complicated, particularly when data variety and velocity are factors.

Keys to an effective big data strategy

In an organization, developing a big data strategy requires an understanding of business goals and the data that's currently available to use, plus an assessment of the need for additional data to help meet the objectives. The next steps to take include the following:

prioritizing planned use cases and applications;

identifying new systems and tools that are needed;

creating a deployment roadmap; and

evaluating internal skills to see if retraining or hiring are required.

To ensure that sets of big data are clean, consistent and used properly, a data governance program and associated data quality management processes also must be priorities. Other best practices for managing and analyzing big data include focusing on business needs for information over the available technologies and using data visualization to aid in data discovery and analysis.

Big data collection practices and regulations

As the collection and use of big data have increased, so has the potential for data misuse. A public outcry about data breaches and other personal privacy violations led the European Union to approve the General Data Protection Regulation ( GDPR), a data privacy law that took effect in May 2018. GDPR limits the types of data that organizations can collect and requires opt-in consent from individuals or compliance with other specified reasons for collecting personal data. It also includes a right-to-be-forgotten provision, which lets EU residents ask companies to delete their data.

While there aren't similar federal laws in the U.S., the California Consumer Privacy Act ( CCPA) aims to give California residents more control over the collection and use of their personal information by companies that do business in the state. CCPA was signed into law in 2018 and took effect on Jan. 1, 2020.

To ensure that they comply with such laws, businesses need to carefully manage the process of collecting big data. Controls must be put in place to identify regulated data and prevent unauthorized employees from accessing it.

The human side of big data management and analytics

Ultimately, the business value and benefits of big data initiatives depend on the workers tasked with managing and analyzing the data. Some big data tools enable less technical users to run predictive analytics applications or help businesses deploy a suitable infrastructure for big data projects, while minimizing the need for hardware and distributed software know-how.

Big data can be contrasted with small data, a term that's sometimes used to describe data sets that can be easily used for self-service BI and analytics. A commonly quoted axiom is, "Big data is for machines; small data is for people."

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What is Big Data and Why is it Important?

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Learn about the characteristics of big data, how it's used by organizations, its business benefits, big data technologies, the challenges that users face.

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LEARNING ANALYTICS is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs, as defined back in 2011 for the first LAK, this general definition still holds true even as the field has grown. Learning analytics is both an academic field and commercial marketplace which have taken rapid shape over the last decade. As a research and teaching field, Learning Analytics sits at the convergence of Learning (e.g. educational research, learning and assessment sciences, educational technology), Analytics (e.g. statistics, visualization, computer/data sciences, artificial intelligence), and Human-Centered Design (e.g. usability, participatory design, sociotechnical systems thinking).

SO WHAT’S ALL THE FUSS ABOUT? People have been researching learning and teaching, tracking student progress, analysing school or university data, designing assessments and using evidence to improve teaching and learning for a long time. Learning Analytics builds on these well established disciplines, but seeks to exploit the new opportunities once we capture new forms of digital data from students’ learning activity, and use computational analysis techniques from data science and AI.

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Society for Learning Analytics Research (SoLAR)

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LEARNING ANALYTICS is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs, as defined back in 2011 for the first LAK, this general definition still holds true even as the field has grown. Learning analytics is both an academic field and commercial marketplace which have taken rapid shape over the last decade. As a research and teaching field, Learning Analytics sits at the convergence of Learning (e.g. educational research, learning and assessment sciences, educational technology), Analytics (e.g. statistics, visualization, computer/data sciences, artificial intelligence), and Human-Centered Design (e.g. usability, participatory design, sociotechnical systems thinking).

SO WHAT'S ALL THE FUSS ABOUT? People have been researching learning and teaching, tracking student progress, analysing school or university data, designing assessments and using evidence to improve teaching and learning for a long time. Learning Analytics builds on these well established disciplines, but seeks to exploit the new opportunities once we capture new forms of digital data from students' learning activity, and use computational analysis techniques from data science and AI.

KEY USES. Historically, some of the most common uses of learning analytics is prediction of student academic success, and more specifically, the identification of students who are at risk of failing a course or dropping out of their studies. While it is reasonable that these two problems attracted a lot of attention, learning analytics are far more powerful. The evidence from research and practice shows that there are far more productive and potent ways of using analytics for supporting teaching and learning. Some of the most popular goal of learning analytics include:

1. Supporting student development of lifelong learning skills and strategies

2. Provision of personalised and timely feedback to students regarding their learning

3. Supporting development of important skills such as collaboration, critical thinking, communication and creativity

4. Develop student awareness by supporting self-reflection

5. Support quality learning and teaching by providing empirical evidence on the success of pedagogical innovations

METHODOLOGIES
Descriptive Analytics: insight into the past

Uses data aggregation and data mining to understand trends and evaluative metrics over time. The majority of statistics use falls into this category which is limited to past data and includes:

Student feedback gathered from student satisfaction and graduate surveys

Analysis of data at all stages of the student lifecycle starting from admissions process , to student orientation, enrolments, pastoral care, study support, exams and graduations.

Diagnostic Analytics: why did it happen

This form of advanced analytics is characterised by techniques such as drill-down, data discovery, data mining and correlations to examine data or content to answer the question &dash; "Why did it happen?" and includes:

Analysis of data to inform and uplift key performance indicators across the organization

Analysis of patterns to design appropriate metrics

Equity access reporting and analysis of effective strategies to support students

Learning management system metrics to improve student engagement

Predictive Analytics: understanding the future

Combines historical data to identify patterns in the data and applies statistical models and algorithms to capture relationships between various data sets to forecast trends and includes:

Development of Staff Dashboards to help predict student numbers and cohort mobility through programs to assist in identifying areas for improvement

Prescriptive Analytics: advise on possible outcomes

Goes beyond descriptive and predictive by recommending one or more choices using a combination of machine learning, algorithms, business rules and computational modelling such as:

Focusing on subject/courses where small changes could have a big impact on improving student engagement, feedback and outcomes

Data visualisation via specific tools to provide program/degree level metrics on student enrolments, program stage, results and survey feedback to give teaching staff visual snapshots of students in their programs

WHO BENEFITS? Learning Analytics provides researchers with exciting new tools to study teaching and learning. Moreover, as data infrastructures improve - from data capture and analysis, to visualization and recommendation - we can close the feedback loop to learners, offering more timely, precise, actionable feedback. In addition, educators, instructional designers and institutional leaders gain new insights once the learning process is persistent and visible.

ORGANIZATIONAL INFRASTRUCTURES. The community that SoLAR is fostering is distinctive in its interest in understanding the organizational systems required to introduce and sustain analytics. SoLAR's events and publications draw an eclectic audience that welcomes leaders and policy makers in education/business/government concerned with strategies for organizational change.

ETHICS OF DATA, ANALYTICS & AI. There is rightly much public and professional debate around the ethics of 'Big Data' and AI, including privacy, the problem of opaque 'black box' algorithms, the risk of training machine learning classifiers on biased datasets, and the dangers of incorrectly predicting someone's behaviour. These concerns are just as relevant in education, so the ethics of educational data, analytics and AI are front and center in SoLAR's work, with a very active stream in our events and publications.

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What is Learning Analytics? - Society for Learning Analytics Research (SoLAR)

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LEARNING ANALYTICS is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs, as defined back in 2011 for the first LAK, this general definition still holds true even as the field has grown.

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What exactly is big data?

The definition of big data is data that contains greater variety, arriving in increasing volumes and with more velocity. This is also known as the three Vs.

Put simply, big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems you wouldn’t have been able to tackle before.

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Big data defined

What exactly is big data?

The definition of big data is data that contains greater variety, arriving in increasing volumes and with more velocity. This is also known as the three Vs.

Put simply, big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can't manage them. But these massive volumes of data can be used to address business problems you wouldn't have been able to tackle before.

The three Vs of big data

Volume

The amount of data matters. With big data, you'll have to process high volumes of low-density, unstructured data. This can be data of unknown value, such as Twitter data feeds, clickstreams on a web page or a mobile app, or sensor-enabled equipment. For some organizations, this might be tens of terabytes of data. For others, it may be hundreds of petabytes.

Velocity

Velocity is the fast rate at which data is received and (perhaps) acted on. Normally, the highest velocity of data streams directly into memory versus being written to disk. Some internet-enabled smart products operate in real time or near real time and will require real-time evaluation and action.

Variety

Variety refers to the many types of data that are available. Traditional data types were structured and fit neatly in a relational database. With the rise of big data, data comes in new unstructured data types. Unstructured and semistructured data types, such as text, audio, and video, require additional preprocessing to derive meaning and support metadata.

The value-and truth-of big data

Two more Vs have emerged over the past few years: value and veracity. Data has intrinsic value. But it's of no use until that value is discovered. Equally important: How truthful is your data-and how much can you rely on it?

Today, big data has become capital. Think of some of the world's biggest tech companies. A large part of the value they offer comes from their data, which they're constantly analyzing to produce more efficiency and develop new products.

Recent technological breakthroughs have exponentially reduced the cost of data storage and compute, making it easier and less expensive to store more data than ever before. With an increased volume of big data now cheaper and more accessible, you can make more accurate and precise business decisions.

Finding value in big data isn't only about analyzing it (which is a whole other benefit). It's an entire discovery process that requires insightful analysts, business users, and executives who ask the right questions, recognize patterns, make informed assumptions, and predict behavior.

But how did we get here?

The history of big data

Although the concept of big data itself is relatively new, the origins of large data sets go back to the 1960s and '70s when the world of data was just getting started with the first data centers and the development of the relational database.

Around 2005, people began to realize just how much data users generated through Facebook, YouTube, and other online services. Hadoop (an open-source framework created specifically to store and analyze big data sets) was developed that same year. NoSQL also began to gain popularity during this time.

The development of open-source frameworks, such as Hadoop (and more recently, Spark) was essential for the growth of big data because they make big data easier to work with and cheaper to store. In the years since then, the volume of big data has skyrocketed. Users are still generating huge amounts of data-but it's not just humans who are doing it.

With the advent of the Internet of Things (IoT), more objects and devices are connected to the internet, gathering data on customer usage patterns and product performance. The emergence of machine learning has produced still more data.

While big data has come far, its usefulness is only just beginning. Cloud computing has expanded big data possibilities even further. The cloud offers truly elastic scalability, where developers can simply spin up ad hoc clusters to test a subset of data. And graph databases are becoming increasingly important as well, with their ability to display massive amounts of data in a way that makes analytics fast and comprehensive.

Big data benefits:

Big data makes it possible for you to gain more complete answers because you have more information.

More complete answers mean more confidence in the data-which means a completely different approach to tackling problems.

Big data use cases

Big data can help you address a range of business activities, from customer experience to analytics. Here are just a few.

Product development

Companies like Netflix and Procter & Gamble use big data to anticipate customer demand. They build predictive models for new products and services by classifying key attributes of past and current products or services and modeling the relationship between those attributes and the commercial success of the offerings. In addition, P&G uses data and analytics from focus groups, social media, test markets, and early store rollouts to plan, produce, and launch new products.

Predictive maintenance

Factors that can predict mechanical failures may be deeply buried in structured data, such as the year, make, and model of equipment, as well as in unstructured data that covers millions of log entries, sensor data, error messages, and engine temperature. By analyzing these indications of potential issues before the problems happen, organizations can deploy maintenance more cost effectively and maximize parts and equipment uptime.

Customer experience

The race for customers is on. A clearer view of customer experience is more possible now than ever before. Big data enables you to gather data from social media, web visits, call logs, and other sources to improve the interaction experience and maximize the value delivered. Start delivering personalized offers, reduce customer churn, and handle issues proactively.

Fraud and compliance

When it comes to security, it's not just a few rogue hackers-you're up against entire expert teams. Security landscapes and compliance requirements are constantly evolving. Big data helps you identify patterns in data that indicate fraud and aggregate large volumes of information to make regulatory reporting much faster.

Machine learning

Machine learning is a hot topic right now. And data-specifically big data-is one of the reasons why. We are now able to teach machines instead of program them. The availability of big data to train machine learning models makes that possible.

Operational efficiency

Operational efficiency may not always make the news, but it's an area in which big data is having the most impact. With big data, you can analyze and assess production, customer feedback and returns, and other factors to reduce outages and anticipate future demands. Big data can also be used to improve decision-making in line with current market demand.

Drive innovation

Big data can help you innovate by studying interdependencies among humans, institutions, entities, and process and then determining new ways to use those insights. Use data insights to improve decisions about financial and planning considerations. Examine trends and what customers want to deliver new products and services. Implement dynamic pricing. There are endless possibilities.

Big data challenges

While big data holds a lot of promise, it is not without its challenges.

First, big data is...big. Although new technologies have been developed for data storage, data volumes are doubling in size about every two years. Organizations still struggle to keep pace with their data and find ways to effectively store it.

But it's not enough to just store the data. Data must be used to be valuable and that depends on curation. Clean data, or data that's relevant to the client and organized in a way that enables meaningful analysis, requires a lot of work. Data scientists spend 50 to 80 percent of their time curating and preparing data before it can actually be used.

Finally, big data technology is changing at a rapid pace. A few years ago, Apache Hadoop was the popular technology used to handle big data. Then Apache Spark was introduced in 2014. Today, a combination of the two frameworks appears to be the best approach. Keeping up with big data technology is an ongoing challenge.
Discover more big data resources:

How big data works

Big data gives you new insights that open up new opportunities and business models. Getting started involves three key actions:

1. Integrate
Big data brings together data from many disparate sources and applications. Traditional data integration mechanisms, such as extract, transform, and load (ETL) generally aren't up to the task. It requires new strategies and technologies to analyze big data sets at terabyte, or even petabyte, scale.

During integration, you need to bring in the data, process it, and make sure it's formatted and available in a form that your business analysts can get started with.

2. Manage
Big data requires storage. Your storage solution can be in the cloud, on premises, or both. You can store your data in any form you want and bring your desired processing requirements and necessary process engines to those data sets on an on-demand basis. Many people choose their storage solution according to where their data is currently residing. The cloud is gradually gaining popularity because it supports your current compute requirements and enables you to spin up resources as needed.

3. Analyze
Your investment in big data pays off when you analyze and act on your data. Get new clarity with a visual analysis of your varied data sets. Explore the data further to make new discoveries. Share your findings with others. Build data models with machine learning and artificial intelligence. Put your data to work.

Big data best practices

To help you on your big data journey, we've put together some key best practices for you to keep in mind. Here are our guidelines for building a successful big data foundation.

Align big data with specific business goals

More extensive data sets enable you to make new discoveries. To that end, it is important to base new investments in skills, organization, or infrastructure with a strong business-driven context to guarantee ongoing project investments and funding. To determine if you are on the right track, ask how big data supports and enables your top business and IT priorities. Examples include understanding how to filter web logs to understand ecommerce behavior, deriving sentiment from social media and customer support interactions, and understanding statistical correlation methods and their relevance for customer, product, manufacturing, and engineering data.

Ease skills shortage with standards and governance

One of the biggest obstacles to benefiting from your investment in big data is a skills shortage. You can mitigate this risk by ensuring that big data technologies, considerations, and decisions are added to your IT governance program. Standardizing your approach will allow you to manage costs and leverage resources. Organizations implementing big data solutions and strategies should assess their skill requirements early and often and should proactively identify any potential skill gaps. These can be addressed by training/cross-training existing resources, hiring new resources, and leveraging consulting firms.

Optimize knowledge transfer with a center of excellence

Use a center of excellence approach to share knowledge, control oversight, and manage project communications. Whether big data is a new or expanding investment, the soft and hard costs can be shared across the enterprise. Leveraging this approach can help increase big data capabilities and overall information architecture maturity in a more structured and systematic way.

Top payoff is aligning unstructured with structured data

It is certainly valuable to analyze big data on its own. But you can bring even greater business insights by connecting and integrating low density big data with the structured data you are already using today.

Whether you are capturing customer, product, equipment, or environmental big data, the goal is to add more relevant data points to your core master and analytical summaries, leading to better conclusions. For example, there is a difference in distinguishing all customer sentiment from that of only your best customers. Which is why many see big data as an integral extension of their existing business intelligence capabilities, data warehousing platform, and information architecture.

Keep in mind that the big data analytical processes and models can be both human- and machine-based. Big data analytical capabilities include statistics, spatial analysis, semantics, interactive discovery, and visualization. Using analytical models, you can correlate different types and sources of data to make associations and meaningful discoveries.

Plan your discovery lab for performance

Discovering meaning in your data is not always straightforward. Sometimes we don't even know what we're looking for. That's expected. Management and IT needs to support this "lack of direction" or "lack of clear requirement."

At the same time, it's important for analysts and data scientists to work closely with the business to understand key business knowledge gaps and requirements. To accommodate the interactive exploration of data and the experimentation of statistical algorithms, you need high-performance work areas. Be sure that sandbox environments have the support they need-and are properly governed.

Align with the cloud operating model

Big data processes and users require access to a broad array of resources for both iterative experimentation and running production jobs. A big data solution includes all data realms including transactions, master data, reference data, and summarized data. Analytical sandboxes should be created on demand. Resource management is critical to ensure control of the entire data flow including pre- and post-processing, integration, in-database summarization, and analytical modeling. A well-planned private and public cloud provisioning and security strategy plays an integral role in supporting these changing requirements.

Learn more about big data at Oracle

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Big data is larger, more complex data sets, especially from new data sources. Learn about the definition and history, in addition to big data benefits, challenges, and best practices.

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In computing, data is information that has been translated into a form that is efficient for movement or processing. Relative to today's computers and transmission media, data is information converted into binary digital form. It is acceptable for data to be used as a singular subject or a plural subject.

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By

In computing, data is information that has been translated into a form that is efficient for movement or processing. Relative to today's computers and transmission media, data is information converted into binary digital form. It is acceptable for data to be used as a singular subject or a plural subject. Raw data is a term used to describe data in its most basic digital format.

The concept of data in the context of computing has its roots in the work of Claude Shannon, an American mathematician known as the father of information theory. He ushered in binary digital concepts based on applying two-value Boolean logic to electronic circuits. Binary digit formats underlie the CPUs, semiconductor memories and disk drives, as well as many of the peripheral devices common in computing today. Early computer input for both control and data took the form of punch cards, followed by magnetic tape and the hard disk.

Early on, data's importance in business computing became apparent by the popularity of the terms "data processing" and "electronic data processing," which, for a time, came to encompass the full gamut of what is now known as information technology. Over the history of corporate computing, specialization occurred, and a distinct data profession emerged along with growth of corporate data processing.

How data is stored

The range of digital data over time has grown from bits and bytes to brontobytes, with yet bigger data measure to come.

Types of data

Growth of the web and smartphones over the past decade led to a surge in digital data creation. Data now includes text, audio and video information, as well as log and web activity records. Much of that is unstructured data.

The term big data has been used to describe data in the petabyte range or larger. A shorthand take depicts big data with 3Vs -- volume, variety and velocity. As web-based e-commerce has spread, big data-driven business models have evolved which treat data as an asset in itself. Such trends have also spawned greater preoccupation with the social uses of data and data privacy.

Data has meaning beyond its use in computing applications oriented toward data processing. For example, in electronic component interconnection and network communication, the term data is often distinguished from "control information," "control bits," and similar terms to identify the main content of a transmission unit. Moreover, in science, the term data is used to describe a gathered body of facts. That is also the case in fields such as finance, marketing, demographics and health.

Data management and use

With the proliferation of data in organizations, added emphasis has been placed on ensuring data quality by reducing duplication and guaranteeing the most accurate, current records are used. The many steps involved with modern data management include data cleansing, as well as extract, transform and load (ETL) processes for integrating data. Data for processing has come to be complemented by metadata, sometimes referred to as "data about data," that helps administrators and users understand database and other data.

Analytics that combine structured and unstructured data have become useful, as organizations seek to capitalize on such information. Systems for such analytics increasingly strive for real-time performance, so they are built to handle incoming data consumed at high ingestion rates, and to process data streams for immediate use in operations.

Data professionals

The database administrator profession is an offshoot of IT. These database experts work on designing, tuning and maintaining the database.

Today, companies employ data management professionals or assign workers the role of data stewardship, which involves carrying out data usage and security policies as outlined in data governance initiatives.

A distinct title -- the data scientist -- has appeared to describe professionals focused on data mining and analysis. The benefit of presenting data science in an evocative manner has even given rise to the data artist; that is, an individual adept at graphing and visualizing data in creative ways.

Next Steps

Learn how to protect sensitive data by creating a data flow map.

Continue Reading About data

Dig Deeper on Data governance

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What is Data? - Definition from WhatIs.com

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This definition explains the meaning of data, which is information that has been translated into a form that supports efficient movement or processing.

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artificial intelligence

noun

the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.
Oxford Languages and Google - English | Oxford Languages (oup.com)

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Oxford Languages and Google - English | Oxford Languages

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Google's English dictionary is provided by Oxford Languages. Oxford Languages is the world's leading dictionary publisher, with over 150 years of experience creating and delivering authoritative dictionaries globally in more than 50 languages.

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Characteristics or information, usually numerical, that are collected through observation.

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Definition:

Characteristics or information, usually numerical, that are collected through observation.

Context:

Data is the physical representation of information in a manner suitable for communication, interpretation, or processing by human beings or by automatic means (Economic Commission for Europe of the United Nations (UNECE), "Terminology on Statistical Metadata", Conference of European Statisticians Statistical Standards and Studies, No. 53, Geneva, 2000).

Statistical data refers to data from a survey or administrative source used to produce statistics. (OECD, IMF, ILO, Interstate Statistical Committee of the Commonwealth of Independent States, "Measuring the Non-Observed Economy: A Handbook", Annex 2, Glossary, Paris, 2002, available at
http://www.oecd.org/dataoecd/9/20/1963116.pdf).

Source Publication:

The International Statistical Institute, "The Oxford Dictionary of Statistical Terms", edited by Yadolah Dodge, Oxford University Press, 2003.

Statistical Theme: Methodological information (metadata)

Glossary Output Segments:

SDMX

Created on Tuesday, September 25, 2001

Last updated on Wednesday, March 8, 2006

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OECD Glossary of Statistical Terms - Data Definition

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Definition: Characteristics or information, usually numerical, that are collected through observation. Context: Data is the physical representation of information in a manner suitable for communication, interpretation, or processing by human beings or by automatic means (Economic Commission for Europe of the United Nations (UNECE), "Terminology on Statistical Metadata", Conference of European Statisticians Statistical Standards and Studies, No.

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Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.
Wikipedia

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Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.

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Big data is data that contains greater variety, arriving in increasing volumes and with more velocity.

# data[].attributes.body
Data is information that has been translated into a form that is efficient for movement or processing. Data is information converted into binary digital form.

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Generally speaking, learning analytics refers to the collection and analysis of data about learners and their environments for the purpose of understanding and improving learning outcomes.

Learning analytics is where big data meets traditional quantitative methods in education. Governments, universities, testing organizations, and massive open online course providers are collecting data about learners and how they learn. All that data, however, has been mostly untapped until the fairly recent development of the methods and tools to do so.

Much of the data currently available does not come in neat, well-organized, and collected formats. It exists in varied forms across systems and locations. Analysts today need the skills to access and transform this data, so we can better understand not only what students know, but how they know it. Learning analytics and educational data mining are the tools to transform this data into knowledge and lead, in the end, to improved education.

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Big data, predictive analytics, data mining-the list goes on. These terms, absent 20 years ago, are now part of our everyday lexicon. Roughly 2.5 quintillion bytes of data are created daily, impacting our lives and careers in unexpected ways .

But big data is not about the data . It is about what you do with it. It is about the analysis that leads to decisions that impact people. And one of the biggest areas where that analysis can make a difference is in education, particularly through the practice of learning analytics.

Download Our Free Guide to Breaking Into Analytics

A guide to what you need to know, from the industry's most popular positions to today's sought-after data skills.

What is Learning Analytics?

Generally speaking, learning analytics refers to the collection and analysis of data about learners and their environments for the purpose of understanding and improving learning outcomes.

Learning analytics is where big data meets traditional quantitative methods in education. Governments, universities, testing organizations, and massive open online course providers are collecting data about learners and how they learn. All that data, however, has been mostly untapped until the fairly recent development of the methods and tools to do so.

Much of the data currently available does not come in neat, well-organized, and collected formats. It exists in varied forms across systems and locations. Analysts today need the skills to access and transform this data, so we can better understand not only what students know, but how they know it. Learning analytics and educational data mining are the tools to transform this data into knowledge and lead, in the end, to improved education.

How is Learning Analytics Used?

The Horizon Report: 2019 Higher Education Edition , produced by the EDUCAUSE Learning Initiative, identifies learning analytics as one of the digital strategies and technologies expected to enter mainstream use in the near future. As such, skilled data analysts are needed to support these analytics-driven initiatives and bring about institutional success.

"One of the driving forces behind the growth in the learning analytics field is that educational institutions are now facing the challenge of limited resources and increased accountability, which are propelling creativity and discovery," says Melisa LaCroix, a database administrator and Northeastern University alumna. "The analytics programs that were once reserved for big businesses are now being widely used in higher education and K-12 institutions to measure student growth, inform curriculum decisions, and identify students at risk for failing a course or program. "

In addition to these practical uses of learning analytics, the practice is often used to:

Measure key indicators of student performance

Support student development

Understand and improve the effectiveness of teaching practices

Inform institutional decisions and strategy

"Having an analytics program in place is not a magical solution to educational challenges, however," LaCroix notes.

Instead, any learning analytics initiative should be complemented by strong communication between the analyst and educators in order to successfully leverage the insights gleaned by the effort.

"To build a successful learning analytics program at an educational institution, it's important to engage and inform school leaders, listen to the needs of teachers and students, and educate users on how to consume and act on the data that's presented. Teachers and administrators will only be committed to data-driven decision making if they can see its value and are educated about how to turn insights gleaned from data into action."

Impacting the Future of Education

Now is the time to enter the learning analytics field and make a difference in the education of current students and lifelong learners. To do so requires a solid foundational understanding of quantitative methods, combined with expertise in constructing, merging, managing, cleaning, and analyzing data using cutting-edge software and techniques.

After making a career change and pursuing an education focused on learning analytics, LaCroix has been able to find success working in the field.

"As I've worked in the field of learning analytics and learned more about it through my graduate program, I see how it has the potential to help educators decide where limited resources are best spent, to increase their accountability to parents and other stakeholders, to make their results more transparent, to inform their instruction, and to put more power into students' hands for creating and improving their own learning," LaCroix says.

Beginning a career in this dynamic field often begins by developing your skills in data analysis . Northeastern offers programs to prepare students to meet the growing demand for professionals in the field, including the Master's in Analytics and Graduate Certificate in Learning Analytics . Students learn key analytics concepts and theories, and discover how to select, prepare, implement, interpret, and evaluate learning analytic models appropriately.

According to LaCroix, "Learning analytics has enabled me to combine my skills as an instructional designer, an advocate and communicator, and a technology professional."

What impact do you want to have on the future of education?
If you're interested in breaking into the world of learning analytics, learn how earning a Master's in Analytics from Northeastern can help you accelerate your career.

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What is Learning Analytics & How Can it Be Used?

# data[].attributes.attachment_link.description
Big data, predictive analytics, data mining-the list goes on. These terms, absent 20 years ago, are now part of our everyday lexicon. Roughly 2.5 quintillion bytes of data are created daily, impacting our lives and careers in unexpected ways . But big data is not about the data .

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Artificial Intelligence is defined as the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. AI is also defined as,

An Intelligent Entity Created By humans

Capable of Performing Tasks intelligently without being explicitly instructed.

Capable of thinking and acting rationally and humanely.

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What is Artificial Intelligence in 2023? Types, Trends, and Future of it?

# data[].attributes.attachment_link.description
Artificial Intelligence is defined as the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. AI is also defined as, An Intelligent Entity Created By humans Capable of Performing Tasks intelligently without being explicitly instructed. Capable of thinking and acting rationally and humanely.

# data[].attributes.body
Big data refers to large data sets that can be studied to reveal patterns, trends, and associations. The vast amount of data collection avenues that exist means that data can now come in larger quantities, be gathered much more quickly, and exist in a greater variety of different formats than ever before. This new, larger, and more complex data is collectively called big data.

# data[].attributes.attachment_link.description
Big data is the newly vast amount of data that can be studied to show patterns, trends, and associations.