AI Analytics: The Future of BI is Here

ai analytics

In today’s rapidly evolving digital landscape, businesses are constantly striving to gain a competitive edge. One way to do this is by leveraging analytics driven by artificial intelligence (AI) to better extract insights from data and make those insights accessible to more people. Using the power of machine learning, AI analytics is a powerful tool that can help businesses uncover valuable insights so they can make faster data-driven decisions to stay ahead of the curve. Let’s explore strategies and tactics for deploying AI analytics for a competitive advantage.

What is AI analytics?

A subset of decision intelligence, AI analytics refers to the application of artificial intelligence technologies to enhance and automate data analysis. AI analytics algorithms are designed to process large volumes of data, identify patterns, and extract insights that companies can use to improve business performance.

Oftentimes, AI analytics involves a combination of technologies such as machine learning algorithms, natural language processing (NLP), and other features designed to enable companies to analyze data faster, more accurately, and at a larger scale than would be possible with traditional analytics tools. Essentially, AI analytics makes it easier for humans to ask questions and then interpret results, thus making analytics accessible to more business users.

What are the benefits of AI analytics?

Many companies’ analytical processes are far too manual, slow, and subject to interpretation. Traditional tools—like dashboards and spreadsheets—are perfectly fine for observing standard metrics from aggregated data at a high level and with predetermined drill paths. But to answer why things are happening, or how they can effect change in business performance, companies must turn to their data analyst teams, who often rely on manual data analysis techniques like slicing and dicing data via SQL/Python code or visual analysis. 

In the end, it’s easy to miss vital insights when you have to wait for the data experts to prepare and share reports. And it’s nearly impossible to gain a real-time understanding of important metrics. 

Here’s what happens when you bring in AI analytics capabilities:

  • Faster decision-making: Reducing an organization’s analytics backlog, AI analytics helps automate the often time-consuming or tedious aspects of data analysis. Instead of waiting for data scientists to build models or execute the automation of analysis, business users can perform their own analysis using AI.
  • Increased efficiency: Cut down on reporting backlogs and time-consuming handoffs between teams and tools. With AI analytics, organizations can automate the process of data analysis, reducing the effort required to analyze large datasets. Importantly, this can free up the time of data analysts or scientists to focus on other strategic tasks (e.g., interpreting the insights or making business decisions based on those insights).
  • Easier access: The biggest obstacle to the broad use of data is that tools can be difficult to use. Data literacy is a problem, and this deters people from using data even when they have the simplest of questions of the state of the business. Instead, with AI analytics’ NLP capabilities, for example, any user—regardless of data skills—can explore and analyze terabytes of unaggregated enterprise data in plain English. User-friendly AI analytics helps democratize exploration and analysis by making the query process as simple and intuitive as a Google search.
  • Advanced analytics maturity: AI analytics can be used to build and apply predictive models to forecast trends and behaviors, helping organizations make better decisions and take proactive measures to mitigate potential risks. It can also be used to personalize data analysis, providing more relevant insights and recommendations.

AI analytics vs. business intelligence

Business intelligence, a system of processes, tools, and other technologies, is used by organizations to analyze business data and generate insights. BI typically involves gathering data across sources; transforming the data into formats that are suitable for analysis; and using tools like dashboards, reports, and data visualizations to present the results. The end goal is to help businesses understand performance, identify trends, and, ultimately, make more informed decisions—backed by data, of course.  

Though both AI analytics and BI are focused on analyzing data and generating insights to better inform decision-making, AI analytics is typically more automated and advanced, using machine learning algorithms to uncover insights automatically and handle large volumes of data. BI lacks other intelligent automation features such as NLP, automated visualizations, automated insights, automated data prep, AutoML, or proactive intelligence. AI analytics is about making data analysis easier for business users through these capabilities. 

Traditional BI tools can also present a limited or biased view—i.e., you might focus only on subsets of data with which you’re the most familiar. BI tools can only give you a picture of your business that you know to ask. What businesses are missing is the intelligence hidden in their data that they have not explicitly asked about—which is where AI-driven intelligence comes into play. AI analytics can expose significant opportunities hidden in your data to grow revenue and profit.

AI analytics vs. data science tools

AI analytics and data science tools are different mainly in their focus and techniques. Data science tools, typically meant for data scientists and those with advanced training, are suited for analyzing historical data to understand trends and patterns, as well as modeling the future and identifying ways to improve outcomes (predictive and prescriptive analytics). 

AI analytics tools, on the other hand, typically involve a more holistic set of capabilities, making them suited for a broader range of analytics, including descriptive, diagnostic, predictive, and prescriptive. AI tools for analytics are about empowering business users and analysts to do more with their data. Making advanced analytics easier and more accessible for non-data scientists, AI analytics enables users to ask questions in natural language and get data stories generated automatically, for instance.

AI analytics vs. GPT, LLMs, and generative AI

GPT (generative pre-trained transformer) or LLMs (large language models) refer to a specific type of generative AI that excels at generating human-like text and language-based outputs.

In the context of AI in data analytics, generative AI is not a versus but, rather, another subset of AI that is encompassed by AI analytics. This is specifically useful for generating descriptive narratives, summaries, and reports for greater contextual relevance that communicates the insights derived from data analysis in a more human-readable format. This enables data analysts and business users to understand and communicate the findings effectively.

Additionally, generative AI can assist in automating certain parts of the data analysis workflow: e.g., generating data-driven content such as personalized recommendations or performing code review and generation. This integration of generative AI within AI analytics can significantly streamline and enhance the process, enabling faster decision-making and improving customer outcomes and overall business performance.

chat gpt ai analytics

In 2024, generative AI becoming increasingly more accessible as part of organizations’ AI analytics workflows. However, we don’t recommend looking at it as a simple plug-and-play solution. Rather, data and domain experts must still exercise caution when adopting the technology and focus on setting up and fine-tuning GenAI to their specific needs (e.g., use cases, data size, individual expertise).

In other words, an AI-powered analytics platform shouldn’t rely on LLM technology as the backbone of its computational engine. Instead, GenAI should be a means of enriching search with an LLM-based contextual understanding for a given industry or use case. This helps increase the accuracy of search results for a given prompt (i.e., no hallucinations).

How companies are using AI analytics for a competitive advantage

Business users want insights that usually require a good deal of in-depth, complex analysis. Root cause analysis, customer segmentation, or customer churn forecasting are by no means easy tasks with spreadsheets and traditional BI tools, especially when you have lots of data, complex data coming from multiple sources, or many columns or variables in your data.

Here are some common applications of AI analytics:

Pharma and life sciences: AI analytics tools can help market access teams automate repetitive tasks and simplify key pieces of their data analysis, resulting in better-informed access, pricing, and product launch decisions. They can unlock the value of pharma data by easily connecting, blending, querying, and drawing insights from a variety of internal and syndicated data sources via no- and low-code analytics to reveal previously unseen connections.

Consumer goods companies, retailers, and ecommerce brands: For these companies, commercial success depends on their ability to identify and respond to shifting consumer trends, especially as customer behaviors and patterns change so quickly. When brands make use of AI analytics, they can more quickly and easily identify the most important contributors of changes to metrics, understand why metrics changed through root cause analysis, and identify target customers and marketing campaign attributes that will lead to desired outcomes.

Marketing: Using sophisticated machine learning algorithms, marketing analysts can deploy AI analytics to discover personalized customer segments. For example, when customers and prospects can be clustered according to their behavior and socio-demographic patterns, a marketing department can use this information for campaigns to target prospects at a highly granular level. 

Financial services companies: Applying AI analytics enables finserv organizations to detect anomalies in transactions and identify potentially fraudulent activity. By building machine learning models from transactional data and baseline and profile user behavior, they can proactively evaluate incoming transactions in real time, preventing losses before they occur.

Customer lifetime value: A critical KPI in any industry, customer lifetime value helps organizations identify, track, and predict the right customers in order to maximize brand potential and boost profitability and retention, but it’s often a time-consuming process. Using AI analytics, a customer analytics team can go from connecting multiple complex data sources to delivering actionable insights to the business. AI analytics tools can examine millions of data points in a matter of minutes, providing teams with critical insights around customer behavior.

Key capabilities of AI analytics

Natural language search: Data exploration and ad hoc analysis are critical to decision-making, and NLP enables users, regardless of their data skills, to flexibly explore and analyze terabytes of unaggregated enterprise data through a Google-like search, enabling deeper analysis of textual data.

Machine learning and automated analysis: AI analytics tools provide machine learning algorithms that automate the process of model building, simplifying the process for users who may not have extensive ML expertise. Automated analysis accelerates complex data analysis with AI-driven automation to identify the “why” behind the “what” and provide direction as to how to improve outcomes by automating root cause analysis, analyzing key drivers, comparing cohorts, and identifying meaningful segments in data that go beyond first-order facts/drivers.

Predictive analytics: AI analytics tools employ advanced ML algorithms to build predictive models, which take automated insights to the next level by spotting potential opportunities for improving outcomes. These models can forecast future outcomes, identify trends, and detect anomalies, enabling organizations to make data-driven decisions by leveraging historical data and patterns.

Automated data storytelling: Data storytelling streamlines conveying complex analysis and insights to influence decisions. Best-in-class AI analytics tools feature automated data storytelling—concise, decision-centric insights and visualizations generated by natural language narratives, expediting decision-making.

Metrics monitoring: AI analytics tools should include anomaly detection, the generation of auto analysis, and personalized insights, automatically anticipating what users are interested in. They can also provide features like data profiling, data summarization, and data discovery to uncover hidden insights.

Flexibility and ease of use: AI analytics tools should be able to handle diverse and large-scale data from multiple sources (e.g., by leveraging cloud data warehouses like Snowflake), integrate and prepare data for analysis, clean and transform data, and ensure quality and consistency.

Automated data visualization and reporting: AI analytics offers intuitive and interactive visualizations that simplify the communication of complex insights, enabling users to create dashboards, reports, and interactive charts with the click of a button.

Learn more about AI analytics

Tellius is an AI-driven decision intelligence platform that accelerates your understanding of business data to answer critical questions across a variety of analytical use cases. 

See how you can start with data, explore and visualize relevant metrics, generate granular insights to understand why metrics change, and identify the next best action to drive desired business outcomes. Watch our webinar on AI-powered analytics to learn how to unlock your true analytics potential.


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