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How does AI reduce human error | Preventions & Examples

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Read what are human errors and how does AI reduce human error with error prevention examples. Learn how AI may redefine reduce errors.

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Thesis: Ai is a promenint force that can lead to major advantages, all while proposing its own problems.

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Thesis: Ai is a promenint force that can lead to major advantages, all while proposing its own problems.

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Artifical inteligence is "faster and [can] preform better than a human"

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Artifical inteligence is "faster and [can] preform better than a human"

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"once AGI is achieved, technological progress will accelerat rapidly"

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"once AGI is achieved, technological progress will accelerat rapidly"

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prediction

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Ai "has led to major edvances in the field.

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Ai "has led to major edvances in the field.

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Leveraging AI for Streamlined Business Processes

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Find out more about Leveraging AI for Streamlined Business Processes Along with latest news about agile training and more…

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Nowadays, it's hard to scroll through your LinkedIn feed without seeing a post about ChatGPT. How to create sales email templates, blogposts, debug code,... The list of things you can do with generative AI tools seems endless.

How about using AI for data analysis? In this article, we'll explore why AI is great to speed up data analysis, how to automate data analysis each step of the way, and which tools to use. Let's jump in.

What is AI data analysis?

As your data grows, it gets harder and more time-consuming to do data exploration. AI data analysis uses different artificial intelligence techniques to get valuable insights from large amounts of data. Here are just a few examples of these techniques.

Machine learning algorithms: extract patterns or make predictions on large datasets

Deep learning: use neural networks for things like image recognition, time-series analysis and more

Natural language processing (NLP): derives insights from unstructured text data

Imagine you work in a warehouse that stores and distributes thousands of packages daily. To run your warehouse more efficiently, you want to know:

How long does inventory stay in the warehouse on average?

How much space in your warehouse is (un)occupied on average?

Which items are low in stock and need to be replenished?

What's the average time needed to replenish stock items per product type?

Which items have been in the warehouse for longer than 1 month/quarter/year?

AI algorithms will search for patterns in large datasets to find an answer to these business questions. By automating this difficult task, businesses can make better, data-driven decisions quickly. Data scientists have been using machine learning for years to analyze big data. And nowadays, a new wave of generative AI tools empowers anyone to analyze data, even if you don't know anything about data science.

Benefits of using AI for data analytics

AI can feel daunting if you're not familiar with it. But if you consider the benefits, it's more than worth dipping your toes into it.

First off, artificial intelligence can seriously reduce your operational costs. 54% of businesses say implementing AI led to cost savings. Imagine you're paying a data scientist to spend 8 hours on manual data cleaning or processing. What if they can complete that task in less than an hour using machine learning models? They have 7 hours left to analyze other data or interpret the results.

This leads us straight to another huge advantage. Time savings. AI can analyze large volumes of data far quicker than humans. It's much easier to scale data analysis, and get insights in real-time. Especially in industries like manufacturing, construction or finance, real-time analytics have an invaluable impact. Just imagine how many life-threatening accidents you could avoid if machine malfunctions were reported before they happened.

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AI analytics: a threat for data analysts?

With the rise of tools like ChatGPT, people immediately started raising concerns about job security. Think of our data scientist again, who now gets the job done 8 times faster. Should they fear AI will take over their jobs?

If you know that 90% of the world's data was generated in the last 2 years alone, and that data growth is expected to increase by 150% in 2025, there's no cause for concern. Data will only become more important in the next coming years, and the world needs data analysts and data scientists to make sense of it. While AI tools may change their job roles and the way they work, these roles will still play a pivotal role in data-driven companies.

On the contrary, if you familiarize yourself with AI tools now, it can be a huge driver in your career. You'll be able to solve more complex problems faster, which is critical for innovation.

How to use AI for data analysis?

From raw data to decisions, let's look at the role artificial intelligence plays in each stage of the data analytics process.

1. Data collection

If you want to get valuable insights from your data using AI, data collection is the fundamental first step. You'll need to extract data from different data sources to feed your AI algorithm. Otherwise, it won't have input to learn from. You can train AI systems with any type of data, whether it be product analytics, sales transactions, web tracking, or automated data collection through web scraping.

You can also scrape web data using R, a programming language and software environment for statistical computing and graphics. R provides various libraries and packages that allow you to scrape data from websites, extracting the required information for your AI algorithms. This can be particularly useful when you need to gather real-time or updated data from websites for your data analysis.

2. Data cleaning

The cleaner your data, the more valuable your insights will be. However, data cleaning is a tedious process and prone to human error if you need to do it manually. You can use artificial intelligence to do the heavy lifting. For example, use AI to identify outliers, handle empty values, normalize your data, and more.

3. Data analysis

After you've trained AI models with clean, relevant data, you can start analyzing the data and generate insights. AI models can identify patterns, correlations, anomalies, and trends in the data. Doing it manually would cost your data analyst a few days or weeks. Now, they can do it in mere hours!

As with any technology, always be careful about system bias. AI learns from its training data, so if your source data contains biases, it can creep into AI algorithms as well.

4. Data visualization

Once you've found interesting patterns in your data, you want to present them in an easy, understandable format. With the help of business intelligence tools, you can start building visual dashboards to interpret insights. Interactive charts and graphs let you explore your data deeply, and drill down into specific information to improve your workflows.

You can automate data visualization tasks with ChatGPT or other tools. Some business intelligence tools have added AI capabilities to their software. Luzmo's AI-powered dashboard builder is a great example.

5. Predictive analytics

Compared to traditional business analytics, artificial intelligence excels in forecasting. Based on patterns in historical data, it can run predictive models to make accurate predictions about the future. Just think of forecasting inventory based on past stock levels. Or setting sales targets based on past sales and seasonality.

6. Data-driven decision-making

If you've used artificial intelligence in the previous steps, you're bound to get better insights as a result. Leveraging these powerful insights, you'll make better decisions faster and improve your status quo. With powerful predictive analytics, you can even avoid issues before they happen.

The dangers of using AI for data analysis

While AI analytics tools speed up the analysis process significantly, there are some pitfalls too. A tool can streamline the process, but the tool is only as good as the person using it. Here are some of the challenges that may arise with AI.

Data quality

Trash in = trash out. AI data analytics tools use the data you feed into them and give you adequate results. If your data is poorly formatted, has errors and missing fields or has outliers, AI analytics tools won't be able to spot it as many don't have anomaly detection.

In other words, before doing any data analytics, you should invest time and money in data cleaning and formatting.

The decision-making processes only work if the data is accurate and up to date, and this is where a human being comes into play.

Data security and privacy

In April 2023, Samsung employees used OpenAI to help them with writing code. The result? They leaked classified code for measuring superconductor equipment. As OpenAI states on their website, the data you feed into it is used to train the language learning model and teach it more things about the world.

If you ask an AI tool to analyze or summarize data, it means that in most cases, someone else will be able to access that data. Be it the people behind the powerful AI analytics tool or the users wanting to learn, your data is not always safe.

Be mindful of the data you feed into AI analytics tools, especially if you have to comply with strict laws and regulations.

You'll still need personnel on board

Even if you have the best data analysis tools in the world, you're still going to need someone to man the ship.

First, you may need a data scientist to help you with unstructured data, as well as choosing the right ETL and data visualization platform. You may be able to do a lot of the work yourself, by using a tool such as Luzmo's chart generator. However, you'll still need someone to steer you in the right direction.

Best AI data analysis tools

Luzmo

This embedded analytics platform focuses on user-friendly analytics. It helps SaaS products add powerful client-facing reporting to their platform in days, not weeks. With their no-code dashboard editor, anyone can build interactive charts in minutes. Hook up their extensive API to ChatGPT or other AI tools, and use it to autogenerate dashboards with simple prompts.

Tableau

Tableau's AI features are aimed at data scientists, so you need a little more experience to get started. Data analysts can do AI-powered predictions, what-if scenario planning, and other data science techniques. For data scientists, they offer statistical modeling directly in Tableau with R, Python, MATLAB, and more.

Microsoft Power BI

Microsoft has infused AI capabilities directly into Power BI to handle text data. You can now enrich your data through for example sentiment analysis, key phrase extraction, language detection and more.

KNIME

KNIME is an open-source data science platform, and a great entry tool if you want to start experimenting with AI tools. Data experts can use an intuitive, drag-and-drop interface to design, train, and apply machine learning models.

Databricks

Databricks unifies data, analytics and AI all under one platform. It combines elements of data lakes and data warehouses, making it a great infrastructure for developers to run AI applications or machine learning algorithms.

AnswerRocket

AnswerRocket is like an AI assistant for data analysis. You can connect data from different data sources, ask questions in plain English, and get proactive insights and recommendations based on the underlying data.

Getting started with AI data analysis

Do you want to unburden data analysts and data scientists from cumbersome, time-consuming data analysis tasks? Then now is the right time to start exploring the power of AI. It can seriously speed up time to insight, and help your team focus on more complex tasks.

The tools above are a great starting point to start fidgeting with AI data analysis. If you're working on a customer-facing analytics offering for your SaaS product, Luzmo is tailor-built for SaaS use cases. Get in touch with our experts for a product demo, or sign up for a free trial to try it yourself!

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Using AI for Data Analysis: The Ultimate Guide (2024) | Luzmo

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What is AI data analysis, and why should you do it? Explore the best AI tools for data analysis and how to use them in each stage of the data analytics process.

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Data analytics help a business optimize its performance, perform more efficiently, maximize profit, or make more strategically-guided decisions

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Data analytics help a business optimize its performance, perform more efficiently, maximize profit, or make more strategically-guided decisions

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How does AI reduce human error | Preventions & Examples

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Read what are human errors and how does AI reduce human error with error prevention examples. Learn how AI may redefine reduce errors.

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With the increasing prominence of artificial intelligence, a new era of data analytics is unfolding. This development has significant implications for data analysts, as artificial intelligence is not just introducing new tools and techniques but also fundamentally transforming the analytics landscape. Consequently, the skills, roles, and decision-making processes of analysts are evolving.

Decoding AI Terminology

Artificial intelligence (AI) is a branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence.

AI is broad and usually divided into two main types: narrow AI, which is designed to perform a specific task such as voice recognition, and general AI, which can perform any intellectual task that a human can.

Subfields of AI include machine learning, where algorithms learn from data and improve their accuracy over time without being programmed to do so, and natural language processing, which enables machines to understand and respond to human language.

Generative AI is a field of AI that employs machine learning algorithms to produce new data that resembles the patterns and traits of its training data. This includes generating text and images like human creations and intricate data patterns. One of the best-known examples of this type of technology is the GPT-4 model from OpenAI, known for its ability to generate human-like text.

AI for Data Analysis

AI for data analysis allows for processing large volumes of complex data at high speeds, leading to quicker and more accurate business insights. For example, predictive analytics, a form of AI, can analyze historical data to forecast future trends and behaviours.

AI-powered text analytics tools can sift through unstructured data such as customer reviews or social media comments, providing valuable insights into customer sentiment and preferences. Similarly, AI can be used for anomaly detection in financial transactions to identify fraudulent activities.

In addition, generative AI, particularly solutions like ChatGPT, offers a range of capabilities that data analysts can leverage:

1. Code Interpretation and Generation: Imagine a data analyst wrestling with a complex Python script left by a departed colleague. Here, generative AI can be a lifesaver, interpreting the Python code into accessible language. Generative AI can generate tailored boilerplate code in Python or SQL when creating new scripts based on the analyst's guidelines.

2. Learning and Problem-Solving: When an analyst is eager to learn about a novel topic like deep learning, generative AI can curate a personalized learning plan complete with appropriate resources. For complex mathematical problems that might emerge during data analysis, the GPT-4 code interpreter plugin stands ready to solve these challenges.

3. Domain-Specific Support: Picture an analyst working on a healthcare project who encounters a question specific to that domain. Generative AI can provide a relevant answer, bridging the gap between technical knowledge and domain-specific understanding. Be aware, though, that the generative AI will sometimes very confidently make up facts. This is known as "hallucination."

4. Exploratory Data Analysis: Should analysts need a quick overview of a new dataset, they can call on generative AI to provide summary statistics and visualizations, offering an immediate and intuitive understanding of the dataset's key attributes.

5. Automated Analysis and Communication: If routine data analysis tasks take up too much of an analyst's time, generative AI can automate them, freeing up the analyst for higher-order tasks. Generative AI can also translate technical findings into plain English for more effective communication with non-technical stakeholders.

6. Presentation Preparation: Analysts can utilize generative AI to summarize critical data insights and construct compelling recommendations when faced with a presentation. In stakeholder interviews, generative AI can provide answers to practice questions and break down complex concepts, aiding in comprehensive preparation.

7. Synthetic Data Generation: When accurate data is scarce or too sensitive to use, generative AI can produce synthetic data, reflecting the original dataset's characteristics and patterns without compromising privacy. Healthcare data analysts, for example, can use this method to analyze patient information without breaching confidentiality rules.

8. Augmented Predictive Analytics: Generative AI can enhance predictive analytics, giving businesses more precise forecasts. For instance, a logistics company with limited past data can use generative models to simulate various scenarios, improving the accuracy of future supply and demand predictions.

9. Stress-Testing and Risk Analysis: Generative AI can also be used to create challenging situations to test the resilience of business strategies. For example, financial data analysts can generate data replicating extreme market conditions to evaluate the solidity of investment strategies.

10. Humour: Generative AI, especially ChatGPT, can be surprisingly funny and brighten the day of any busy analyst. Type this prompt in ChatGPT, "Marv is a chatbot that reluctantly answers questions with sarcastic responses," and ask why a data analyst should use AI.

An Augmentative Force, Not a Replacement

With the advent of AI, many worry about the job security of data analysts. It is worth noting, however, that AI, particularly generative AI, is not meant to replace people. Instead, it is a helpful tool that takes over tedious and time-consuming tasks, allowing analysts to concentrate on more strategic aspects of their work.

When it comes to understanding the business context, making critical decisions, ensuring ethical data practices, and effectively communicating data insights, human expertise is highly valued. AI is currently limited in these aspects and human analysts can excel in them.

Skill Shift: Adaptability Is Key

As AI continues to gain influence, the skill set required for data analysts is evolving. In addition to core analytical abilities, data analysts must also possess knowledge of AI and machine learning concepts. This includes understanding how AI models are trained, their functioning, and how to interpret the results they produce.

It is becoming increasingly essential for analysts to know programming languages used in AI, such as Python or R. They must also be proficient in using AI tools and platforms, which require technical proficiency. Besides technical skills, soft skills are also gaining importance as AI takes over data processing and visualization tasks. Skills such as critical thinking, creativity, strategic decision-making, and effective communication are more crucial than ever.

Analysts must be able to present data insights and their implications clearly and understandably, making complex data accessible to non-technical audiences.

Embarking on the AI Journey

Beginning your journey into generative AI involves several steps:

1. Foundational Learning: Start with understanding the principles of AI and machine learning, paying particular attention to generative models. Here is an introduction to large language models.

2. Specialized Courses: Pursue dedicated online courses and tutorials on generative AI. Some good examples are: Google Generative AI Learning Path, 9 Best ChatGPT Classes, and Free Resources for Data Scientists.

3. Hands-On Experience: Experiment with simple projects using open-source generative AI models or commercial models such as Open AI's ChatGPT to gain practical exposure.

4. Up-to-Date Knowledge: Regularly review industry articles and research papers and engage in AI forums to keep abreast of the latest developments and advancements.

5. Collaboration: Team up with other data scientists and data analysts in your organization to glean practical insights and applications of generative AI.

To sum up, AI does not mean data analysts will become obsolete. Instead, it gives them powerful tools to enhance and simplify their work.
It is time to embrace the future of data analytics with AI and discover the exciting opportunities it offers.

Note: This blog has been created by applying generative AI. The author used AI to summarize relevant videos, articles, and ideas. ChatGPT was then used to synthesize the ideas and create a first draft blog. Generative AI saved the author considerable time, which supports the promise and premise of this article.

To expand your skill set and optimize your business analysis job opportunities, consider earning a professional certification. IIBA offers an array of professional certifications to help you stand out to employers. Learn about IIBA's certifications.

About the Author:

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How AI Is Rewriting the Rules of Data Analysis | Analyst Catalyst Blog

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This blog explores the transformative influence of generative AI on data analysis, detailing how it augments the work of data analysts, the evolving skill set required, and the steps for integrating it into data analytics.

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Streamlining business processes can improve the day-to-day operations in an organization and make them more efficient. For CIOs who want to turn digital transformation into business transformation, it can eliminate redundancies, establish optimal resource utilization, and improve operational efficiency.

Following the right strategy and tips for streamlining business processes can make all the difference.

Why automate processes in the first place?

Automation is a buzzword these days. Everyone wants to automate processes without knowing what benefits they will get out of it.

Here are four main benefits of business process automation.

1. Increased productivity

All the repetitive laborious tasks can be automated. These tasks are mostly data entry, manual calculations, routing requests based on rules, etc. Any step that operates based on rules can be automated. With repetitive tasks automated, you can spend time on tasks that need actual human attention and decisions.

2. Reduced errors

Most errors are caused by humans. Even if systems make errors, the errors occur repeatedly signaling faulty setup by humans. With automation, errors are almost zero. Even if they occur, the system makes it easy to pinpoint the exact location. Machines can do what they do best-perform repetitive actions with zero errors and maximum efficiency.

3. Better planning

An advantage of automation is data. You get detailed information about how your processes happen throughout each stage. How much time is spent on tasks, where the most errors happen, what kind of requests frequent more, and more. With this kind of data, you can predict, plan, and allocate resources better for the future.

4. Improved transparency and data security

When a process is automated, all the activities happen in the open. Everyone understands the process better than before. Automation also gives the ability to control access to data. You can choose to show or hide specific information to assigned users or user groups. With one shot, you increase transparency and get more control over data access.

What is Streamlining anyway?

Streamlining processes refers to the practice of identifying and eliminating repetitive and unnecessary steps or activities in a process in order to make it more efficient and effective. The goal of streamlining processes is to reduce waste, improve efficiency, and increase productivity. It includes leveraging modern technologies in the place of manual labor.

Top 5 tips to streamline processes at your company:

Find out the top 5 tips to streamline processes.

1. Study the process before you automate it

Too often, companies will dive right into the automation of a process, without thoroughly analyzing it first. Start by mapping out the process visually, and iron out redundancies and inefficient steps. For instance, if your accounts team tends to work significantly harder on a particular day of the month, they may be following a dated procedure that can be improved through automation. Study processes this way from every department.

The key to streamlining business processes effectively is to dissect them before you automate them using software tools.

"The first rule of any technology used in a business is that automation applied to an efficient operation will magnify the efficiency.

The second is that automation applied to an inefficient operation will magnify the inefficiency."

2. Applications first, tech later

Another common pitfall-especially when dreaming about the juicy prospects of automation and streamlining work processes-is that people often create theirworkflow automationapps and then try to fit them to problematic tasks and/or processes.

The right approach is quite the opposite; figure out a process that is bloated or redundant, study it, and then develop the solution specifically for it.

3. Check the ROI of automation

Make sure the time and other valuable resources you spend to streamline business processesdon't actually outweigh the costs and time saved because of automation. Companies can spend anywhere from $250,000 to $500,000 on traditional BPM tools. (Drop off a few zeros if you opt for kissflow!)

If your HR team were to spend thousands of dollars on automating their background verification process and only saved a fraction of the amount by doing so, it would be obviously a waste of money.
The whole point of streamlining a business process is to make it more cost- and time-effective, so ensure that it actually is!

4. Think long term

Checking the ROI is important, but it's crucial that you consider the long-term effect. Complete implementation can take over a year, so start with a 5-year plan. With a large multi-process organization, use the 80-20 rule-changing 20% of your processes can give you 80% of the possible benefits. Put someone in charge of championing the implementation, and convince managers who might be averse to change.

Make sure the way you streamline business processes leaves room for growth and changes in your development process. You don't want to spend 6 months streamlining your purchase order process, and then realize that finance was planning on shifting it to an outsourced model as the company scales.

5. Use readymade solutions whenever available

The business processesin your organization are probably similar to processes that have been automated already in other companies. If you have access to such streamlined business process automations, look into whether you can adopt them for your company. Some workflow automation solutions like Kissflow come preloaded with app librariesthat you can use to create your own process automation apps, without having to start from scratch. Also, with approval workflow software, you can streamline operations with ease.

Automated order processing: An industry example of streamlined business processes

Manual order processing can lead to errors, delays, and unsatisfied customers. Streamlining and automating order processing ensures every order goes through the required series of verifications and checks, which reduces errors and leads to faster order processing.

There is real-time visibility into product availability, preventing out-of-stock product processing and reducing backorder risks. Internal teams and customers can also track the real-time status of orders, reducing common customer inquiries and improving overall customer satisfaction.

Strategies to streamline business processes

Automation: Enterprises can automate repetitive parts of a process to improve efficiency and free up employee's time, allowing them to focus on more complex and important tasks.

Streamlining approval workflows: Approvals can be time-consuming and complex. They can slow down processes and lead to bottlenecks when not designed well. By transforming approvals into structured processes, you can streamline operations, improve visibility, and simplify informed decision-making.

Low-code/no-code development: By using low-code/ no-code platform, you can automate and streamline processes with little to no coding. These tools support collaborative development and allow business users to leverage their domain expertise to build streamlined workflows while allowing IT to maintain governance and control.

Integrating existing platforms: One of the biggest challenges that enterprises face is integrating disparate applications and processes across different departments. Automated and streamlined business processes integrating well with existing platforms can reap huge efficiency dividends.

Improve process efficiency with Kissflow

It can be overwhelming to improve business processes when you don't have the right strategy or tools.

Kissflow is a simple yet powerful low-code platform that allows you to streamline and automate processes without requiring technical expertise. Its drag-and-drop form builder lets you quickly improve processes or add parallel branches to the workflow. Business users can make changes to their processes in real-time and use the intuitive reporting dashboard to measure the efficiency of processes and create built-in reports.

Transform your business processes with Kissflow and experience improved agility.

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5 Easy Tips for Streamlining Business Processes at Your Company

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Streamline processes for: Increased efficiency, agility, happy customers, & reduced costs. Get 5 easy tips!