AI Analytics: Supercharging Decision-Making with Generative AI

In the constantly evolving digital landscape, businesses are in a constant race to gain a competitive edge. Leveraging artificial intelligence (AI) analytics has become a critical strategy for organizations to extract actionable insights from vast amounts of data, making these insights accessible to a broader audience and facilitating faster data-driven decision-making. AI analytics, powered by machine learning (ML) and large language models (LLMs) like GPT, offers businesses the tools they need to stay ahead of the curve.

In this comprehensive guide, we’ll explore how AI analytics works; get into advanced capabilities like embedding, pre-processing, and post-processing with GPT and LLMs; and discuss the benefits and competitive advantages of AI analytics for organizations in 2024 and beyond.

Written by: Betsy Lillian,

Content Marketing Manager at Tellius

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.

The primary goal of AI analytics is to simplify the process of asking complex questions and interpreting results, making advanced analytics accessible to a wider range of business users. By automating and enhancing data analysis, AI analytics empowers organizations to make more informed decisions and respond to changes in the market with agility.

Key capabilities of AI analytics

AI analytics platforms offer a range of capabilities that enable businesses to harness the full potential of their data.

Machine learning and automated analysis: AI data 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.

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.

Learn more: Watch our recent Tellius 101 webinar on how to maximize your analytics value with natural language search.

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 AI 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 the decision-making process and visualizing data for users.

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.

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-driven business analytics capabilities:

Let’s explore these more in-depth:

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 amounts of data. 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.

How companies leverage 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 satisfaction, 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 businesses in the finserv industry 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 (and improving the customer experience).

The role of GPT, LLMs, and generative AI in analytics

GPT (generative pre-trained transformer) or LLMs refer to a specific type of generative AI that excels at generating human-like text and language-based outputs, making them immensely powerful tools for data analysis.

Many of us likely began our generative AI journey through a humble ChatGPT search. Since then, so many more LLMs have emerged, creating a growing ecosystem for training, fine-tuning, deploying, observing, and governing GenAI. Now, in 2024, generative AI is becoming increasingly more accessible as part of organizations’ AI analytics workflows.

In the context of AI in data analytics, we believe 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.

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.

We don’t recommend looking at GenAI plus AI analytics as a simple plug-and-play solution.

Rather, data and domain experts must still exercise caution when adopting the generative AI 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).

Read more—Generative AI for Data Analytics: All Your Questions Answered

Embedding, pre-processing, and post-processing

GenAI offers a tremendous opportunity to help bridge the technical understanding gap for an organization. Previously inaccessible technical activities for some users (e.g., code creation, data analysis, and more) can now be translated with GenAI.

In the context of AI analytics, GPT and LLMs offer several key advantages:

Advanced embedding techniques: A big strength of GPT and LLMs is their ability to create embeddings that capture not just the literal meaning of words, but also their contextual relationships: e.g., “bank” is associated with a financial institution, not the side of a river. GPT and LLMs use advanced embedding techniques to understand these nuances, enabling AI analytics tools to generate more accurate insights.

For example, Tellius’ natural language search function—now enhanced by Kaiya GenAI-powered search—can intelligently understand the context of search queries. Users don’t have to necessarily pre-define synonyms for keywords: e.g., referring to a “profit” column as “financial gain” will be understood, mapping to the correct dataset column.

Pre-processing: Pre-processing is a critical step in any data analysis workflow. GPT and LLMs enhance this process by automating many of the tasks that would otherwise require manual intervention.

For instance, GPT models can automatically identify and correct any misspellings, standardize date formats, and handle missing data. In addition, they can perform more complex tasks like language translation and context-based filtering, thus further improving the quality of the data fed into the analytics pipeline. The result? Businesses can spend more time focusing on getting insights, rather than cleaning up their data.

In the pre-processing phase of Tellius’ AI analytics solution specifically, the query is handled by Tellius, which performs validation (is the query properly formed?), intent detection (what’s the user’s goal?), entity extraction (what are the key elements of the query?), schema retrieval (what’s the relevant business view in Tellius?), tokenization (how can we break it down into smaller units for processing?), and similarity search (how do the embeddings of this query compare to pre-existing embeddings in the vector database?).

Post-processing: Once the analysis is complete, the results still need to be interpreted and communicated to stakeholders. GPT and LLMs can generate summaries, explanations, and visualizations that make it easier for business users to understand and act on the insights.

For example, after performing a complex analysis to identify the factors driving customer churn, GPT can generate a summary that explains the key findings. It might highlight that “customers who interact with customer support less than once a month and have an account balance below $500 are more likely to churn.” This kind of post-processing makes insights more accessible to non-technical users at an organization.

In Tellius specifically, the LLM’s output is processed again by Tellius, which validates whether the LLM’s interpretation is correct and aligned with the user’s intent; enriches the LLM response by adding any missing information; and converts the JSOn into the actual database query.

Turn complex queries into deeper insights

As discussed, GenAI can understand complex data sets and create intuitive, easy-to-understand summaries of the information. These are some of the many reasons why GenAI is being introduced to many AI analytics solutions as a translation layer.

For example, with clear and concise GenAI summaries, CPG organizations can elevate their pricing and promotion strategies by putting more advanced analytics in the hands of frontline category managers. Supply chain analysts can better understand the impacts of potential delays, providing detailed analysis and essential follow-up questions. Field sales teams at life science companies can quickly identify potential opportunities and optimize territory alignment.

Tellius Kaiya’s approach to processing a natural language query combines the power of LLMs with specialized data processing and querying capabilities.

Below you can see how a user asks a granular question in Tellius’ AI analytics solution, and Kaiya GenAI search is able to trigger deep insights from the natural language search:

Learn more about AI analytics

Tellius’ robust AI analytics platform unlocks true self-service AI-native analytics that transforms how customers interact with data.

We believe our secret sauce is our comprehensive GenAI analytics stack, built on our AI-native platform. This combination provides rapid, relevant, and contextualized analytics at a scale critical for enterprises. Since our founding in 2017 with an AI-first approach, we’ve crafted layers to unlock conversational analytics that cater to both business users and technical analysts.

Want to check out Tellius AI-native analytics platform? Get a customized demo for your organization.