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16 Oct 2023

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Make Informed Business Decisions with Cognitive Analytics

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How Cognitive Analytics Can Empower Your Business Decisions

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How Cognitive Data Analytics mines big data to provide insights that accelerate decision-making and increase customer engagement for faster business growth.

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Cognitive analytics might be the one you need for your business.

Data has become one of the most vital elements in today's business landscape. Before you make a decision, whether big or small, you must go through all the data at your disposal. This way, you have a competitive advantage over your competitors and a high return on investment.

But with the huge volumes of structured and unstructured data, how do you arrive at business solutions in the shortest time possible? Thanks to the availability of analytics tools, you can now analyze all the data collected from different IoT devices in real time and from anywhere. While analytics help you get ahead of the competition, you still need knowledge-based insights for better decision-making.

This is where cognitive analytics comes in. In fact, the cognitive computing market is estimated to reach about $87 billion by 2026 as businesses look for ways to make massive advances. So, what is cognitive analytics, how can companies use it, and why is it important? Let's get into more in-depth details about this new paradigm shift.

What is Cognitive Analytics?

Cognitive analytics seeks to exploit the advances made in computing by using both data analytics and AI practices. In other words, it's a field of analytics that mimics the human thought process to draw insights and inferences from existing data models. It helps enterprises arrive at better conclusions and decisions based on the available information.

Also, this combines technologies like Deep Learning, Machine Learning, algorithms of Artificial Intelligence, and Semantics. By applying these technologies, cognitive analytics delivers more effective outcomes due to the interactions with humans and data. You can easily search through the information in a knowledge base and arrive at conclusions in real time. You can find answers through unstructured data sources like social media posts, images, and text documents and get real-time solutions.

Why Cognitive Analytics Is Important

The major reason why many businesses have adopted cognitive analytics is to overcome the challenge of creating an algorithm. After collecting large volumes of data, enterprises needed to develop a customized methodology to get critical insights. To tackle these challenges, cognitive analytics and machine learning make it more effective for organizations. By implementing this new technology, you can expect increased speed when searching for specific information.

Who Can Use Cognitive Analytics?

Several industries that deal with large amounts of data have implemented cognitive computing to boost their growth. Some of the major sectors that have adopted cognitive analytics include:

Retail industry

Banking and Finance

Cybersecurity

Power and energy

Health

Logistics and transportation

Education

How Your Business Can Use Cognitive Analytics?

Businesses that embraced cognitive analytics in their early stages have seen tremendous growth in various aspects. Statistics from IBM show that approximately 65% of the early adopters of cognitive analytics view it as a very critical aspect of their success. Even better, using this technology gives enterprises a significant edge over other organizations. Below are some of the areas where you can leverage this key technological advancement in your business.

Customer Engagement

Customer engagement is one of the most critical aspects of growing your enterprise. Better engaged customers know you value their money, and they always prioritize your brand. Adopting cognitive analytics will help you identify your client's unique needs and wants, enabling you to deliver personalized customer experiences. As a result, you can deliver tailored offers, products, or messages to each individual, which makes them feel more valued.

Cognitive analytics also helps companies to increase customer engagement by providing faster and more accurate responses. The rate at which you respond to a customer's query also shows you care about them, and they can trust your brand.

Customer Acquisition

Whether you have just started your enterprise or have been in business for decades, you want to keep attracting new customers. Using cognitive analytics, you will sift through volumes of data to arrive at relevant and accurate insights. By utilizing human-like intelligence, this technology recommends solutions based on patterns and trends.

Companies can also respond quickly to changes in the market to suit new and existing customers. For instance, organizations can refine prices based on market trends and purchases, which increases customer loyalty, which is vital for sustainable business growth.

Improve Customer Service

Your customer service team acts as the face of your organization, and customers expect to have a seamless experience every time they interact with your agents. You can leverage cognitive analytics to automate routine customer service operations. This allows your agents to concentrate on high-performance interactions, which improves efficiency.

Plus, using this technology in conjunction with artificial intelligence eliminates common poor customer service habits. Your customers will no longer have to repeat similar issues to different agents without getting a solution, wait for long periods while on hold, and more.

Risk Management

With this technology, you can get relevant recommendations and insights much faster than relying solely on humans. Whatever form of data you have, you can rely on cognitive computing to process it, which increases business capabilities to mitigate risks.

Notably, businesses in the financial sector rely on cognitive analytics to aggregate information from various documents, reports, and medical and economic histories. This helps to reduce risks and improve regulatory compliance.

Increase Productivity and Efficiency

Limited resources make it difficult for many businesses to grow. You can overcome this problem by harnessing the power of cognitive analytics. This technology enables organizations to gather valuable predictive data that assists in increasing productivity and efficiency.

Staying competitive in the current digital market requires you to derive predictive insights accurately. Thanks to cognitive analytics, you can find answers to relevant questions from data in various formats. This is also critical for reducing costs and improving decision-making.

Bottom Line

Cognitive analytics gives you immense power to take your enterprise to the next level by providing robust and agile solutions. And since cognitive analytics is an extension of human intelligence, leveraging this new technology will help strengthen your overall business capabilities. That's why several modern organizations have started harnessing the power of cognitive analytics to help them solve issues.

You risk falling behind the competition if you've not yet started implementing this technology in your business. This is the next big thing, and your organization should be part of it.

How DashboardFox Can Help with Cognitive Analytics

DashboardFox does not currently focus on cognitive analytics. We're upfront about that; you will not find us on the artificial intelligence, ChatGPT, machine learning bandwagon at this moment.

However, here's the difference between us and others in the BI industry hyping those features: while we believe there are legit use cases for them, we don't believe in using them as marketing buzz pretending our BI tool does it.

Here's why DashboardFox still fits in your BI stack, however. Our focus is on the presentation layer. Securely connecting to your sources of data and communicating that to the people you need in the know.

We can also make it very easy for you to extract data to pass into the tools of your stack (or the data analysts who need it) to run it through a cognitive analytics algorithm. And when you're ready to communicate those results in self-service, ad-hoc reports and dashboards, DashboardFox shines.

With DashboardFox, you just need to drag and drop features, organize them, and get started. We can help you with embedded analytics, data visualization, dashboard creation, user management tools, and much more.

Plus, it's intuitive enough to be used by anyone regardless of technical background or experience, thanks to its codeless build reporting. No need to have extensive knowledge of computers and technology when using DashboardFox.

Also, you would be delighted to hear that you don't have to keep any sort of subscription when using DashboardFox. Pay once, and use everything forever!

What are you waiting for? Take advantage of our free live demo sessions or discuss everything with us through a meeting with our experts. We'll wait for you!

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What is Cognitive Analytics (And How Can Your Business Use It)?

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Cognitive analytics might be the one you need to keep your business operations smooth sailing. Learn more in this post here.

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Cognitive analytics is a type of analytics that uses artificial intelligence (AI) to analyze data and identify patterns and trends that would otherwise be difficult or impossible to see. Cognitive analytics can be used to improve decision-making, increase efficiency, and improve customer service.

Cognitive analytics is a new field, but it is growing rapidly. There are many cognitive analytics tools and platforms available, and the field is constantly evolving.

Here are some of the benefits of using cognitive analytics:

Improved decision-making: Cognitive analytics can help businesses make better decisions by providing them with insights into data that would otherwise be hidden. This can help businesses to identify new opportunities, mitigate risks, and improve their bottom line.

Increased efficiency: Cognitive analytics can help businesses to identify areas where they can improve their efficiency by automating tasks and identifying areas of waste or inefficiency. This can help businesses to save time and money.

Improved customer service: Cognitive analytics can help businesses to improve their customer service by providing them with insights into customer behavior and preferences. This can help businesses to provide better service for their customers and to increase customer satisfaction.

If you are looking to improve your business, cognitive analytics is a suitable place to start. It is a powerful tool that can help you to make better decisions, increase efficiency, and improve customer service.

Here are some of the challenges of using cognitive analytics:

Data quality: The quality of the data used to train cognitive analytics models is critical to the accuracy of the results. If the data is not accurate, the results will be inaccurate as well.

Data volume: Cognitive analytics can be computationally expensive, so it is important to have a large enough dataset to train accurate models. If the dataset is too small, the models will not be able to learn from the data and will not be able to make accurate predictions.

Data complexity: Cognitive analytics models can be complex, so it is important to have the expertise to interpret the results. If the models are too complex, it will be difficult to understand the results and to implement them.

Model maintenance: Cognitive models need to be updated regularly to reflect changes in the data. If the models are not updated regularly, they will become outdated, and the results will no longer be accurate.

Despite the challenges, cognitive analytics can be a valuable tool for businesses that are looking to improve their performance. If you are considering using cognitive analytics, it is important to carefully consider the challenges and make sure that you have the resources to overcome them.

Here are some of the most common cognitive analytics techniques:

Natural language processing (NLP): NLP is a technique that allows computers to understand human language. This can be used to analyze text data, such as customer reviews or social media posts, to identify patterns and trends.

Machine learning: Machine learning is a technique that allows computers to learn from data without being explicitly programmed. This can be used to build models that can predict future outcomes or identify patterns in data.

Deep learning: Deep learning is a type of machine learning that uses artificial neural networks to learn from data. This can be used to build models that can perform complex tasks, such as image recognition or natural language translation.

Cognitive analytics is a powerful tool that can be used to improve businesses of all sizes. If you are looking to gain insights into your data and identify areas where you can improve, cognitive analytics is a suitable place to start.
Today's article concludes our series on Data Analytics - Types and Details. We hope you found this series useful. If you'd like to know more or continue the conversation, please reach out. Let us know what you think in the comments!

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Cognitive Analytics: How can we continue to learn and improve?

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Cognitive analytics is a type of analytics that uses artificial intelligence (AI) to analyze data and identify patterns and trends that would otherwise be difficult or impossible to see. Cognitive analytics can be used to improve decision-making, increase efficiency, and improve customer service.

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Everything You Need to Know About Cognitive Analytics

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Most of the data sources are now accessible to decision-making and business intelligence analytics processes thanks to the application of cognitive analytics and intelligent technology. It is changing business patterns.

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What is Cognitive Analytics and how is it applied in eDiscovery? - Salient eDiscovery

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In generic terms, cognitive analytics is the process of applying human-like intelligence to unstructured data in order to enrich the value we can extract from

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Cognitive analytics plays a crucial role in the initial stages of data analysis. It can quickly sift through large quantities of data to identify relevant insights and process them for further research.

empowers organizations to make informed decisions quickly by providing real-time, data-driven insights.

In today's technology-driven world, the sheer volume of data organizations generate is staggering. From customer interactions to operational processes, businesses are inundated with overwhelming information. Cognitive analytics has emerged as a powerful tool in IT to leverage this data and gain actionable insights.

By combining artificial intelligence (AI) and advanced analytics techniques, cognitive analytics has revolutionized how businesses extract value from their data. This blog will explore the concept of cognitive analytics in IT and its transformative potential for intelligent decision-making.

What is Cognitive Analytics?

Cognitive analytics refers to applying AI and machine learning (ML) algorithms to analyze complex data sets, understand patterns, and extract meaningful insights. Unlike traditional analytics, which rely on predefined rules and static models, cognitive analytics leverages the power of AI to learn from data, adapt, and make intelligent predictions.

By resembling human thought processes, cognitive analytics enables systems to understand and interpret unstructured data, including text, images, videos, and audio.

Role of Cognitive Analytics in IT

Cognitive analytics plays a crucial role in the initial stages of data analysis. It can quickly sift through massive data volumes to identify relevant insights and process them for further analysis. By automating data scrubbing, transformation, and integration, cognitive analytics saves time and enhances data quality, ensuring accurate insights.

Cognitive analytics excels at recognizing patterns and anomalies within data. It can identify hidden correlations, trends, and outliers that might go unnoticed by human analysts. By uncovering these insights, organizations can proactively address issues, optimize processes, and seize opportunities before they arise.

3) Text Analytics and NLP

With the rise of unstructured data, such as customer feedback, social media posts, and support tickets, NLP and text analytics have become vital components of cognitive analytics. These techniques enable computers to understand and extract meaning from textual data, providing valuable insights into customer sentiments, emerging trends, and competitive landscapes.

4) Predictive Analytics

Cognitive analytics can accurately predict future events and outcomes by leveraging historical data and applying machine learning algorithms. This capability empowers organizations to anticipate customer behavior, forecast demand, optimize resource allocation, and make informed decisions based on data-driven insights.

5) Intelligent Automation

Cognitive analytics can automate repetitive and time-consuming tasks that traditionally required human intervention. Robotic process automation (RPA) and machine learning can streamline IT processes, leading to improved operational efficiency and reduced costs.

Working of Cognitive Analytics

The process of cognitive analytics typically follows an organized pattern. Data is first collected from various sources, including structured and unstructured data such as text, audio, and video.

This retrieved data is processed and cleared to ensure its quality and suitability for analysis.

Later, AI and ML algorithms are applied to the data to identify patterns, correlations, and anomalies.

NLP techniques may be used to extract meaning from textual data, enabling the system to understand and interpret human language.

Once the data has been analyzed, cognitive analytics systems can generate valuable insights and predictions.

These insights can be used to understand customer behavior, optimize business processes, detect fraud, improve decision-making, and support various industry applications.

The ultimate goal of the overall process is to replicate human-like cognitive abilities, enabling organizations to make data-driven decisions and gain a deeper understanding of complex phenomena more efficiently and accurately.

Benefits of Cognitive Analytics

1) Enhanced Decision-making

Cognitive analytics empowers organizations to make informed decisions quickly by providing real-time, data-driven insights. This agility enables businesses to respond to market dynamics, identify opportunities, and mitigate risks to a large extent.

2) Improved Customer Experience

Cognitive analytics enables businesses to understand customer needs, preferences, and behaviors more precisely. This comprehension allows organizations to personalize their offerings, deliver targeted marketing campaigns, and provide exceptional customer experiences.

3) Fraud Detection and Security

Cognitive analytics can analyze many datasets in real time, identifying suspicious patterns and anomalies that may indicate fraud or security breaches. By promptly detecting and preventing fraudulent activities, businesses can protect their assets and maintain customer trust.

Use Cases of Cognitive Analytics

Cognitive analytics finds its application in the crucial task of data resolution. Some early adopters that work on logical assessments with its integration are as follows:

Both patients and doctors leverage the advantages. Cognitive analytics can manage unstructured data from various sources, including patient reports, medical histories, diagnoses, ailments, and more, to avail sustainable advice to medical professionals. This aids doctors in selecting the best course of treatment and delivering better patient care.

Cognitive analytics can offer individualized suggestions that adhere to the requirements by examining the consumer's basic information and the product specifications they are seeking.

Cognitive AI collects data from various sources, including social media attributes, previous user preferences, customer reviews, or the customer's physical location.

The unstructured information is collected in various formats, then processed to generate a buyer persona based on demographics.

Banks have been interacting with clients through chatbots to quench their concerns. Customers' comments, inquiries, and other input can be analyzed via cognitive analysis to build a database on its own.

By analyzing loan demands based on customers' financial transactions, needs, inquiries, and preferences, financial institutions and customers can work on decision-making about loan management. Besides, the category and type of loan can also be proposed, and new products can be developed to expand the bank's portfolio.

Financial advisors can manage customer portfolios by recommending the appropriate loans and investments using the information gathered via cognitive insights.

Cognitive analytics has been a game-changer IT ecosystem, revolutionizing how organizations analyze and extract insights from their data. Businesses can uncover hidden patterns, make accurate predictions, and automate decision-making processes by leveraging AI, machine learning, and advanced analytics techniques.

The benefits of cognitive analytics extend beyond the IT domain, impacting various aspects of business operations, customer experiences, and strategic planning. As organizations continue to generate vast amounts of data, adopting cognitive analytics will be critical to stay competitive and thrive in the future's data-driven economy.

Explore more data-related whitepapers for further insights.

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Cognitive Analytics: Transforming Data into Actionable Insights | DemandTalk

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Highlights: Cognitive analytics plays a crucial role in the initial stages of data analysis. It can quickly sift through large quantities of data to identify relevant insights and process them fo....

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We create more than 2.5 million terabytes of data every day. The total amount of data produced, captured, and consumed globally will increase dramatically, and it is projected to reach over 250 zettabytes in 2027.

This data comes from everywhere: purchase transaction records, smartphone GPS signals, sensors used to collect climate information, content posted on social media platforms, images and videos captured by satellites, and more.

To make sense of this data and uncover hidden patterns and insights, it is important efficiently analyze it. Today, almost all companies use data analytics to reduce expenses, make evidence-based decisions, develop and market new products and services, and identify new opportunities.

Over the years, data analytics has evolved from Descriptive (what happened in the past) to Diagnostic (why it happened) to Predictive (what is most likely to happen in the future) to Prescriptive (what can be done to affect future outcomes).

The next major paradigm shift is more likely to be toward Cognitive Analytics, which will harness the power of high-performance processors and Artificial Intelligence algorithms to uncover complex patterns hidden in massive volumes of data.

Below, you will learn why Cognitive Analytics is important, how it works, what are the future applications and market size, and which companies are heavily investing in cognitive analytics tools. Let's start with a basic question.

What Exactly Is Cognitive Analytics?

Cognitive Analytics is a smart technology that covers various analytical methods to examine structured and unstructured data. It can search through complex datasets and find answers that make sense for the questions posed.

Think of cognitive analytics as a system with human-like intelligence. Instead of just scanning the data, it understands the context, interprets the tone of a sentence, and recognizes specific objects in images or videos.

Cognitive Analytics often utilizes artificial intelligence, machine learning, neural networks, and semantics to uncover intricate patterns and insights (that conventional analytics cannot). It improves itself over time by continuously learning from data and human-machine interactions. This means the more you feed data, the more intelligent the analytics becomes.

Most companies use cognitive analytics to track customer behavior patterns and identify potential opportunities. This way, they can estimate future outcomes and make data-driven decisions to enhance performance.

Some segments of cognitive analytics overlap with predictive analytics. These segments generally belong to information in business intelligence that is used to predict future events.
Note: Cognitive Analytics is also a name of a private firm, as well as a trademarked name for business services. Though market analysts generally use this term when talking about big data and business intelligence.
How Does It Work?

Cognitive analytics is a data-forward approach that begins and ends with what's inside the data. It uses advanced techniques that process the entire data (all types and at any scale) to reveal unprecedented and unexpected insights. While cognitive analytics systems may use various algorithms to extract actionable insights, their fundamental architecture remains the same, and they all follow a basic sequence of procedures:
1. Search and Gather Data
It searches the entire "knowledge base" to pinpoint real-time data. This data can come straight from sensors, IoT devices, robots, and equipment. It is then organized and integrated with existing or historical data from previous quarters.

Some cognitive analytics systems show data via intuitive dashboards, equipment user interface, and other representations. An easy-to-understand data visualization tool can significantly reduce the time required to find common patterns and carry out repairs.
2. Analyze data
The next step is to carefully analyze text, audio, and video to understand natural language and human interactions. To do this, cognitive analytics combines techniques of artificial intelligence, machine learning, neural network, and semantics.

It primarily relies on the Deep Learning Neural Network, a subset of machine learning. A neural network mimics the way the human brain operates. It consists of multiple layers (an input layer, an output layer, and hidden layers in between) that are connected via nodes, and these connections form a 'network.'

These interconnected nodes function much like neurons in the human brain. They read all the incoming data and recognize complex patterns using algorithms. The network can cluster and classify raw data, find correlations, and continuously learn and improve over time.
3. Generate and evaluate the Evidence-based hypothesis
The identified patterns and insights are then assessed to find whether they are evidence-based hypotheses. If there is concrete evidence to support the hypothesis, the system evaluates it to determine its potential value. This helps in identifying possible future threats and opportunities for improvement.

4. Adapt and learn from user selections and responses

Since cognitive analytics utilizes artificial intelligence, it can adapt its behavior according to the users' interactions. For example, if someone is constantly seeking a specific type of information, the system will gradually learn to provide similar information without wasting a lot of computing resources.

Benefits

Cognitive analytics is applied in enterprises and organizations to bridge the gap between massive amounts of data and the need to make better decisions. The detailed insights into the business help companies improve their products, provide more personalized services to customers, and see how their competitors are performing.

For large companies, cognitive analytics have been proven very effective in three areas:
1. Customer Engagement
The elusive 360-degree view of the customers' behavior allows companies to create a seamless, friction-free customer experience. Also, it allows companies to develop a deeper understanding of users' buying preferences, anticipate their needs, and ensure better user segmentation.
2. Productivity and Efficiency
Prioritize and resolve issues faster, leverage crucial information from user conversations, automate actions for common requests, reduce expenditure on less-important resources, and empower team members to respond quickly and dynamically. And since cognitive analytics merges past information with current data to continuously improve the outcomes, you can quickly adapt to external changes and market trends.
3. Business Growth
Cognitive analytics allows enterprises to expand the business into new markets, find new opportunities in specific locations, mine untapped data sources, and innovate new products and services. It also makes it easier for analysts and market researchers to unlock the value of big data and solve problems in their business premises.

Early Adoption

Over the past decade, several tech giants and startups have invested hundreds of millions in the emerging field of cognitive technology. Google, Microsoft, IBM, Amazon, Cisco, HPE, SparkCognition, and Data Robot are among the top contributors.

According to the study conducted by IBM, the early adopters have already seen positive outcomes from their cognitive initiatives - from better customer engagement to product innovation and business growth.

These early adopters leverage various cognitive capabilities, ranging from machine learning and natural language processing to unlocking values from different data sources, both structured and unstructured. Some companies develop custom platforms, while others utilize APIs to deploy cognitive technology into their systems.

IBM investigated over 600 early adopters who put cognitive technology to work. Their report shows that cognitive capabilities play an important role in the company's growth. About 65% of early adopters believe cognitive technology is crucial to their business strategy and success, while 58% say it is an essential part of their company's digital transformation.
The New Era of Analytics
Since we are entering an era where everything is more likely to be connected, businesses will require cognitive analytics and tools to maximize their growth and adapt themselves according to the customers' needs.

More specifically, cognitive tools can help companies truly understand customer behavior patterns, identify root causes of customer dissatisfaction, and find innovative ways to enhance customer experiences. Cognitive analytics can also be a great fit in optimizing operational costs, increasing customer retention, and decreasing resolution time significantly.

Real-Life Examples of Cognitive Analytics

Cognitive analytics is now being used in almost every industry to identify patterns, potential threats, and opportunities for improvements. For example,
Banking and Finance
Cognitive computing can radically change the banking and finance sector. Unlike conventional systems that model customer behavior based on past actions and treat all customers the same, cognitive systems make more precise and timely analyses of individuals based on their age, income, and transactions.

Cognitive analytics can help banks customize produces and services for each customer, intelligently target high-net-worth individuals, offer better investment plans, and apply proficient knowledge to solve issues quickly.

All in all, this emerging computing paradigm can be applied in three areas:

Customer service: Since a cognitive system understands the problem, it can provide accurate solutions without redirecting customers to different departments and thus saving everyone's time.

Decision-making and advisory: It can enhance the loan underwriting process, which requires a deeper understanding of businesses to make data-driven decisions.

Investment consulting: Cognitive systems allow banks and non-banking financial sectors to quickly go through investment sources to explore the latest market trends, evaluate risks, and provide up-to-date advice to customers.

Cybersecurity
Artificial intelligence and deep neural networks make it easier for cognitive systems to continuously extract data from various sources and acquire knowledge through advanced analytics. The neural network improves itself with every iteration, allowing the system to learn to predict threats and create proactive solutions.

With increasing cyberattacks and a shortage of skilled cybersecurity specialists, a tool like cognitive analytics works wonders. It can process and analyze massive amounts of structured and unstructured data (to identify various types of threats and risks) that would be next to impossible for a traditional analytics system.
Healthcare
The healthcare-related information (such as radiology images, lab results, pathology reports, electronic health records, clinical research, and more) is difficult to analyze and share because it is fragmented. This is where cognitive systems come in handy. They can smartly and quickly parse through different types of information to help healthcare professionals.

The insights offered by cognitive analytics make it easier for healthcare providers to understand the health of individual patients, make informed decisions, and provide more personalized care.

Today, cognitive computing is used in numerous oncology centers worldwide. In MD Anderson (Houston) and Memorial Sloan Kettering (NYC), for example, cognitive analytics is used to assess petabytes of information (including published clinical studies, clinical trial data, and best practice data) to make diagnoses and treatment decisions.
Power and Energy
The oil and gas industry continuously faces challenges locating, refining, and distributing cruise oil and its byproducts. With cognitive analytics, technical professionals and skilled engineers can make important decisions like which location to explore, how to allocate resources, what products should be produced first, and how to make existing facilities more efficient.

Cognitive platforms are already being utilized by many utility companies in the United States, such as Mega Energy, Arizona Public Service Electric, Gexa Energy, Portland General Electric, and Avangrid. These systems provide several benefits, from forecasting loads to mapping rate structures against individual cost models.
Logistics and Retail
Cognitive analytics is useful at every stage of the Logistics and Supply chain, from warehouse management and automation to packaging and palletization. It analyzes data from both internal (inventory, point of sale) and external (market trends, weather) ecosystems. The aim is to improve supply chain efficiency, minimize cost, and provide valuable insights to businesses so they can respond quickly to the dynamically changing scenarios.

Cognitive analytics also plays a crucial role in the retail industry. It can be integrated with eCommerce platforms to extract data about customers and their buying behavior. For offline stores, cognitive systems can help retailers optimize inventories and provide more personalized products at attractive prices.
Education and Learning
Cognitive computing can yield better results for students and educators. It can identify underperforming schools and teachers, predict student performance in specific disciplines, and provide personalized study material.

In 2003, the Miami-Dade County public schools in Florida adopted a Cognitive Tutor Software to provide feedback to students as they are working through problems. It has been found that this software is far more effective than the conventional curriculum alone.

Well-Known Cognitive Analytics Tools

Now that you know the real-life applications of cognitive analytics, let's explore different kinds of cognitive tools used in various fields.

1. OpenSMILE

OpenSMILE (Open-source Speech and Music Interpretation by Large-space Extraction) automatically extracts features from audio signals and classifies speech and music signals. It is widely used for speech recognition, emotion recognition, speaker identification, chord detection, and beat tracking. It can also detect the speaker's states, such as intoxication, depression, or vocal pathological disorders.

Since the software program is mostly written in C++, it runs on all platforms smoothly, including embedded platforms like macOS, iOS, Windows, Android, Linux, ad Raspberry Pi.

2. IBM Watson

Watson applies more than 100 different techniques to analyze natural language, find sources, create hypotheses, score evidence, and combine sand ranks hypotheses. Basically, it applies advanced natural language processing, advanced reasoning, knowledge representation, and machine learning algorithms to the field of open domain question answering.

With Watson, businesses get access to a complete portfolio of AI capabilities, whether it's tools for detecting emerging trends or developing applications for customers. It saves time and automates more processes, so employees can focus on growth and new opportunities.

3. NuPIC

NuPIC (Numenta Platform for Intelligent Computing) is an open-source AI platform that implements HTM (Hierarchical Temporal Memory) learning algorithms. It is suited for a wide range of problems, especially anomaly detection and prediction of streaming data sources.

4. Iris by Apixio

The United States produces more than 1.3 billion clinical care documents annually; about 80% of this data is unstructured and difficult to access. That's where Iris comes in handy. It's a cognitive computing platform that extracts valuable insights from vast amounts of clinical data and other information in the health system.

Iris's analytics engine utilizes machine learning and natural language processing technologies to generate patient profiles and evaluate risks. It can group similar patients and cases to provide evidence-based patient data. And since data changes over time, the platform continuously monitors the patients' information to tell healthcare professionals what works and what doesn't.

5. Aila by Enterra Solutions

Aila combines advanced mathematics and artificial intelligence to reason and solve problems in a way similar to humans. It can analyze the company's internal and external data, draw inferences and produce valuable insights on-demand. Unlike traditional business analytics, Aila can handle the high-volume, multivariate world of Big Data.

It can answer some of the trickiest questions -

Are there opportunities to shift expenditure next year within market segments to improve ROI?

Are any of our clients' service-level objectives at risk? If yes, how can I mitigate that risk?

6. Ignio Cheetah by TCS

Ignio Cheetah is a cognitive automation product suite for IT operations and workload management. It can instantly detect and predict abnormal IT conditions that need attention, reduce false alerts, gather similar or correlated events, and prioritize alerts based on business impact. And since the system learns from its own experience, it becomes better with time.

Ignio is already being used by many large corporations, including Fortune 500 and Global 2000 companies, which are innovators and leaders in their respective fields. The platform manages more than 1.5 million technology resources autonomously for 50+ clients.

7. Cortex Certifai

Cortex Certifai ensures that the machine learning models are free of algorithmic biases to reduce skewed decision-making and reasoning. It generates a composite trust score, called AI Trust Index, that measures automated models based on data quality, performance, robustness, fairness, explainability, and compliance.

This Trust Index helps businesses evaluate the tradeoffs and usual contention between performance and risk. Stakeholders can dig into every evaluation parameter to find potential improvements. It can be applied to any black-box model, including statistical models, predictive models, and machine learning models.

8. SparkCognition EPP

SparkCognition EPP uses proprietary AI and machine learning algorithms to prevent never-before-seen cyberattacks from disrupting business operations and identify phishing threats from PDFs, documents, and direct-to-memory PowerShell attacks. Its lightweight cognitive agent learns the typical behavioral patterns of all endpoints within a company and alerts the security team about potential attacks.

The system integrates well within existing security architecture, providing seamless operations in the background for up to 100,000 managed endpoints running macOS, Linux, or Windows. It has been proven to outperform conventional and next-gen endpoint protection providers.

Recent Studies

Cognitive computing has attracted a lot of data scientists and researchers over the past decade. The recent studies include:
Detecting psychophysiological parameters for a cognitive-emotional conflict
Russian researchers have developed a platform to identify remotely diagnosed parameters (using video messaging tools such as Skype), which can objectively show the presence of a cognitive-emotional conflict during a dialogue. This platform uses neural networks to automatically mark people's emotional reactions in the video data.
An ACT-R computational cognitive model
Although the mental rotation ability is a crucial spatial reasoning skill in human cognition, very little is known about how it works when playing video games. Italian researchers have modeled mental rotation (via an ACT-R-based cognitive model) to analyze whether and under what conditions such ability is used in the TetrisTM game. The outcomes show significant insights into the activation of mental rotation during game dynamics.
Cognitive Analytics for anomaly detection
The scientists at the Centre for Research and Technology Hellas, one of the largest research centers in Greece, have developed a cognitive analytics platform that can process, analyze, and exploit resourceful machine data to support the growing requirements of the manufacturing industry. It is capable of monitoring complex anomalies and handling unforeseen conditions. Plus, its user interface utilizes advanced visualization techniques to present information in an effective manner.

What's the future of Cognitive Analytics?

According to the Research and Markets report, the cognitive analytics market size will reach $33 billion by 2027, growing at a compounded annual rate (CAGR) of 37%. China is projected to reach a market size of $5.5 billion by 2027. Among the other noteworthy geographic markets are Japan, Canada, and Germany, each forecast to grow at 33%, 32%, and 25% over the 2020-2027 period.

The market's primary growth drivers include the adoption of big data and cloud computing technologies, as well as the introduction of sophisticated analytics techniques that are boosting the demand for efficient cognitive analytics solutions.

Read: Artificial Intelligence vs Machine Learning vs Deep Learning | The Difference

Frequently Asked Questions

How many types of analytics are there?

Modern data analytics can be divided into five categories:

Descriptive analytics: is the simplest form of analytics that describes what happened and what is currently happening.

Diagnostic analytics: takes one step further by answering why did it happen. It compares coexisting trends and determines relationships between variables where possible.

Predictive analytics: forecasts future trends, answering what might happen in the near future. It analyzes past and current data to make predictions about future trends.

Prescriptive analytics: takes various factors into account and suggests actionable takeaways. It helps users make data-driven decisions.

Cognitive analytics: combines the power of statistics, artificial intelligence, and machine learning to process massive amounts of data and make sense of context. It presents new opportunities to mine untapped data sources, discover intricate patterns, and provide personalized services.

Why would market analysts use cognitive analytics?

As more and more devices are generating data every second, it is becoming increasingly challenging to collect, process, and analyze all the information stored across the servers and beyond.

Cognitive analytics addresses these challenges: It utilizes advanced statistics and AI models to identify hidden patterns within massive volumes of diverse data. Unlike conventional analytics, which extracts information from structured data in specific domains, cognitive analytics takes a wider and more flexible approach to data discovery. It operates in a similar way to humans to interpret data, understand its context, and uncover its patterns and trends.

What's the difference between Cognitive Computing and AI?

Artificial Intelligence consists of algorithms that are trained to provide accurate results. These algorithms learn from continuously changing data and self-correction methods to make smart decisions.

Cognitive computing, on the other hand, mimics human behavior and adapts to human reasoning. It tries to solve complex problems the same way humans would solve them. In contrast, an AI model tries to find new ways to solve problems that could be better than humans.

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What Is Cognitive Analytics? [A Simple Overview] - RankRed

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Cognitive Analytics is a smart technology that covers various analytical methods to examine structured and unstructured data. It can search through complex datasets and find answers that make sense for the questions posed.