Generative AI is all the rage. In this modern age, the numerous applications of Generative AI in different sectors, including Business Intelligence and Data Analytics are undeniable. Companies are incorporating AI in their workflow to automate tasks, produce content, scale their profit, and do more in less time. This is a significant increase from 1% of data it accounted for a few years ago, says the same report. Generative AI is getting so widespread that by 2025, it will account for , reports Gartner. 10% of all produced data For product-based businesses, Gen AI can create personalized experiences for customers by checking historical data. Finance companies can analyze market data to propose new trading strategies. Every industry, if it involves data analysis, will benefit from the new and emerging Artificial Intelligence technologies. In this article, we’ll explore 5 main use cases of generative AI in business intelligence and data analytics and how real companies are making use of it. Let’s dive in. 1. Automate Analytics A lot of tasks in business intelligence and data analytics can be repetitive and cumbersome. While you could get them done using automated scripts, again, that requires a high level of coding knowledge. Moreover, you need to write scripts every time you need to . If you decide to change your approach when doing the analysis, you’ll need to change the script. automate an analytical task Data collection, for example, is one of the most time-consuming tasks. You need to find the right data sources, get all your Excel files together, search for the relevant information, and start your analysis. A more innovative use case of generative AI in data analytics is to automate the data extraction and analysis process. With most modern AI chatbots, you can just ask them to create automation scripts for you, fully personalized and tailored to your specific needs. When collecting data, you can ask the AI tool to filter out relevant data by inputting some parameters. You can set up an automatic notification system so that if there is any change in customer behavior or sales, you can use that data immediately and take action. This can save you time, and resources, and in turn, save you from potential revenue loss. Seek.ai is an example of a generative AI tool that can automate data extraction and analytics through AI-powered database queries. It enables you to query your database, , through trained deep-learning models that convert natural human language into precise and executable SQL code, that searches through your business data and retrieves insights. including Microsoft SQL servers This way, you can free up more time for your data science teams, while increasing the ROI of your business data. 2. Data Preparation Data preparation involves many complex steps such as data collection, discovery & profiling, cleansing, structuring, transformation, and validation. This makes the process of preparing data often complicated. When collecting data from different sources, there can be inconsistencies in the quality and accuracy of the data. Not to mention, much of this data can be unusable and irrelevant, so you have to remove them. This can take away a lot of valuable time and resources and have a bad effect on workforce productivity. In fact, data scientists spend labeling, cleaning, and organizing data, reports Crowd Flower, a user data enrichment platform. 51% of their time Imagine how much time and energy your business could save if these tasks could be solved by AI. Generative AI can take care of many such data preparation tasks as tagging, segmentation, classification, enrichment, and anonymization. Some of these models have augmented data preparation abilities. That means you can automate profile data, weed out errors and irrelevant data, and perform data cleansing, transformation, and enrichment. Many Business Intelligence vendors offer generative AI technologies with data preparation automation features. These can streamline how you as a self-service BI user can do advanced data analytics work, even without expert knowledge. The team at Snorkel leveraged Vertex AI to extract important data from complex documents. Snorkel Flow uses to programmatically label the data. These can be PDFs, unstructured text, HTML data, richly formatted documents, or conversational text. data-centric AI workflow They use Vertex AI for monitoring the model performance. In case of a data drift, they can quickly adjust parameters and regenerate training models. 3. Predictive Analysis Predictive analysis involves analyzing your current data to predict the future outcome. Data Analytics can be divided into four types, and predictive analysis deals with the future. It uses , machine learning, decision trees, cluster models, etc., for analytics purposes. statistical models For many businesses, this can be a complicated process. and coming to a future-proof conclusion based on that can be a daunting process. Keeping updated with the trends, collecting relevant data in vast amounts, feeding them to the analysis models, Even if you go through the process, that doesn’t necessarily guarantee an accurate result. On the contrary, there’s more chance of being wrong which results in big losses for companies. Generative AIs are trained on a huge amount of datasets. You can also personalize the data that you train it on, making these tools more effective in analyzing the current situation and making better future decisions. These tools can identify patterns and trends more easily than humans and traditional Data Analytics tools. You can also train and improve the accuracy of your existing predictive models using Generative AI. One way to do that is by generating synthetic data and testing your models more realistically and robustly. With such tools in hand, you can better optimize your business operations and increase efficiency. Google’s AI tool, for example, can by scanning the retina. predict cardiovascular risks The algorithm was presented with patients who suffered from the disease and those who didn’t. It made the correct prediction 70% of the time. This is slightly less than the usual testing method which involves blood tests. 4. Risk Management Evaluating risks is essential for any business to become successful. Not using the correct risk metrics, wrongfully assessing risks, selecting unreliable data sources, or incorrectly communicating the identified risks can make your company face pitfalls. Moreover, companies need to monitor risks 24/7. Traditional tools like spreadsheets are not only limited but also require constant adjustments. This takes up employee time and resources and hurts productivity. Another common problem is risk treatment. You may have identified and assessed any potential risks, but how would you manage them? While regular BI tools can help, you need to be aware of their usage in risk management. So risk management is another one of the best use cases of generative AI in business intelligence. You need to make informed decisions at the correct time to avoid risks. Generative AI can help you deal with risk management situations. AI technologies in business intelligence can locate potential business risks and generate valuable reports outlining them. For example, a financial company can use generative AI to detect any kind of fraud and financial crimes. One of the main use cases of generative AI for risk management is simulation. You can create an imaginary scenario, analyze the risks involved, and build a mitigation strategy in advance. This can also be useful for testing your current strategy and tweaking it to handle any edge cases that you probably didn’t consider. Stripe to offer a better user experience and detect fraud. took advantage of GPT-4 After testing 50 potential uses of this model in their business, they found 15 strong use cases. They improved their support customization, started answering questions about support, summarized documents, and detected any fraudulent activities. 5. Generating Visual Data Data visualization is a prominent use case of generative AI in data analytics. Business intelligence and data analytics tools can be limited when turning data into a visual representation. Some tools can only . They may not output the visual the way you want it to be. The result can’t be further personalized or customized. plot traditional charts and graphs For inexperienced eyes, reading and comprehending can prove to be hard even if it’s visual data. It can also be difficult to use advanced tools for someone who isn’t rather tech-savvy. So even with powerful tools, the end goal isn’t served. Generative AI changes how you generate visualized data, and images, and model your insights. Additionally, unlike traditional tools, you can personalize the output you receive using advanced prompts, making the visualization tailored to your needs. This benefits you as well as your customers. That’s because of the user-friendly nature of these images. On top of that, you can make the visualizations interactive to assess data in real-time. It’s easier to generate visualization and get hidden insights from them when you can input natural language prompts for them. Even if you don’t have coding experience or can’t understand data visualization, generative AI can make you comprehend them easily. Telus, a publicly traded holding company, to improve customer experience and performance analytics. used HEAVY.ai They used interactive visualization features of the tool to find important insights from customer data which enables them to create upsell opportunities. As a result, they were able to build wireless services in profitable locations just by querying, filtering, and visualizing the geo-temporal data. Conclusion Generative AI can completely change your Business Intelligence landscape. From data collection, data analysis, augmenting datasets, and addressing data scarcity, to improving model accuracy, getting reliable insights, generating visualizations, and making business decisions, just to name a few. These AI tools will help you build a business with a personalized experience for both you and your consumers. Interestingly, McKinsey reports that Generative AI has the potential of adding across the 63 business use cases. $2.6 trillion to $4.4 trillion every year By now, you should have a clear idea of the benefits and use cases of Generative AI in business intelligence and data analytics. Adopting AI to your business will not only streamline your whole workflow but also remove any human limitations you had previously. With more accurate data in your hands, you can unlock a world of new opportunities. So now is the time to stop pushing back, and start using these technologies to save more business resources and earn more revenue.

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5 Main Uses of Generative AI in Business Intelligence & Data Analytics

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5 Main Uses of Generative AI in Business Intelligence & Data Analytics

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David Kostya

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