Generative AI for data analytics: Turning data into insights

AI advancements are making it easier for anyone to get trustworthy, actionable insights from their data. Generative AI for data analytics brings embedded analytics, grounded in governed data, directly into the applications where people already make decisions.

From asking a question to uncovering an insight, generative AI can shorten the path between data and understanding. And it can change how analytics fits into the products and workflows people use every day, so insights are easier to find, understand, and act on.

What is generative AI for data analytics?

Generative AI creates new content (text, images, video, code, and more) based on conversational prompts and vast stores of training data. In data analytics, these capabilities make it intuitive and approachable for people to explore data, understand what it means, and take action. 

Generative AI in data analytics turns complex data into plain-language explanations, summaries, recommendations, tailored visualizations, and forecasts. It also powers conversational analytics experiences like narrative dashboards, chart annotations, and other ways for end-users to interact with data using simple language. When embedded analytics solutions include generative AI, it’s even easier for people to tap into the power of their data.

The result: a more accessible way to leverage the power of data, helping more people move from questions to understanding, and understanding to action. 

How generative AI turns data into insights

The quality of any generative AI analytics experience depends entirely on what it’s grounded in. When AI queries a governed semantic layer, one that defines what metrics mean, enforces row-level security, and ensures one user’s data never surfaces in another’s view, it can return answers you can actually trust. Without that foundation, you get fast answers that may not be right. 

With the governed semantic layer in place, generative AI enables end-users to use natural language for every interaction with data. Instead of using SQL or navigating through databases, a non-technical end-user can simply ask a question and receive a clear answer based on real, governed business data within an analytics platform.  

For example, someone could type, “Which product drove the biggest increase in revenue last quarter?” The platform translates the question into a query against the underlying data and returns the relevant results. From there, generative AI turns those results into an accessible analytics experience: a plain-language summary, a customized chart, a visualization with key takeaways, and more. 

Generative AI combines several capabilities and approaches to make this possible:

• Natural language querying (NLQ): Lets users ask questions about data in regular, conversational language
• Natural language generation (NLG): Turns data and analytics results into understandable language
• AI-first workflows: Automates and embeds AI-powered analytics directly into products and workflows people already use

NLQ and data exploration

Natural language querying (NLQ) allows end-users to query, explore, and interact with their data in everyday language, without using a specialized query language like SQL. They can ask about a metric, trend, or business outcome and get an answer; no need to know how the underlying data is structured. This makes the initial search more accessible and opens up opportunities for clarifying and follow-up questions.

NLQ essentially acts as a translation tool, taking your plain-language question and translating it into what the software understands.

NLG and generated outputs

NLG transforms an analytics engine’s query results back into outputs that people can easily understand. Think of NLQ as the process of translating human language into data queries, and NLG as the process of translating the results of those queries into plain-language summaries, narratives, and explanations.

Instead of requiring end-users to interpret tables, charts, or complex metrics on their own, NLG generates answers, summaries, and narratives that explain what the data shows. In essence, NLG makes query results understandable and useful. 

For example, after analyzing which product drove the most revenue last quarter, the system might go beyond providing numbers by summarizing insights, highlighting emerging trends, or even explaining which factors contributed to the result. These generated outputs help end-users move beyond seeing what happened to actually understanding the context behind the numbers.

AI-assisted workflows

Generative AI for data analytics reduces friction across the entire workflow, helping users move more easily from exploring data to understanding and communicating insights. Instead of treating AI like a separate destination or disconnected tool, software creators can integrate natural-language interactions directly into the analytics experience. 

These AI-assisted workflows work alongside your underlying data models, queries, and analytical systems, keeping AI grounded in your business context and governance. 

AI-assisted workflows make it easier for end-users to:

• Explore: Ask a question while in a dashboard to identify a trend, metric, or outlier
• Investigate: Ask follow-up questions to drill into the factors contributing to change
• Compare: Ask the system to compare performance across products, regions, customer segments, or time periods
• Interpret: Request an explanation of what a chart or visualization means in plain language
• Summarize: Turn a set of analytical findings into a concise summary for a business audience
• Communicate: Generate a narrative output that helps stakeholders understand key takeaways

This approach keeps the underlying data and analytics infrastructure at the center of the experience without requiring users to understand the technical architecture.

Key applications of generative AI in data analytics

Generative AI applications span the entire analytics experience, so you can turn data into real business momentum.

Data exploration and conversational querying

Generative AI in data analytics = more accessible insights. End-users ask questions about their data, receive answers they understand, and ask follow-up questions to get deeper analysis.

Example: A sales manager could ask, “Which customer segment had the biggest drop in conversions last quarter?” They can then follow that question up with, “How does that compare with the previous quarter?” Generative AI can build them a quick summary paragraph, a comparison chart, or even a whole dashboard.

Insight summarization and interpretation

Dashboards and widgets present data in visual forms. When you add generative AI for data analytics, those visualizations can come with plain-language explanations to make them faster and easier to interpret. AI can highlight key findings, explain trends, and add contextual information. 

Example: A dashboard shows that revenue declined 14% month over month. Rather than asking stakeholders to interpret the chart themselves, AI can summarize the change, identify the specific segments or products contributing to the decline, and provide a concise explanation of key findings. 

Data visualization and storytelling

Another application of generative AI in data analytics is creating, exploring, and explaining data visualizations. End-users can use generative AI to request charts and graphs, refine filters and views, and seek explanations for what a chart shows. Generative AI can even build entire custom dashboards to show an end-user the exact visualizations relevant to them. 

While it can be used for one-off visualizations, users can also leverage generative AI to turn those analytics findings into narratives. These narratives add context to analytical results, helping people understand the key takeaway and why it matters. End-users move from simply viewing a dashboard to fully understanding the story their data is telling.

Example: An end-user asks “Show me monthly revenue by product for the past year.” Generative AI builds the relevant visualizations, compiles them into a dashboard, and then provides a narrative highlighting which products grew, declined, or showed notable changes over the time period. 

Embedded and in-product analytics experiences

Most AI analytics tools are destinations — separate interfaces users have to leave their workflow to visit. Generative AI for data analytics becomes meaningfully more powerful when it lives inside the product itself. Software creators can embed conversational and generative analytics directly into their applications, under their own brand, so end-users get answers where decisions actually happen. 

This approach brings natural-language interactions, summaries, and other AI-powered analytics directly into existing workflows, keeping people inside the application. End-users get better value from their data (and product usage), and software creators create more impactful, engaging experiences.

Example: A customer-facing SaaS platform could let end-users ask “What changed in my account this month?” directly within the application. The embedded AI experience could return a summary of relevant activity and usage data (and even a visualization) and let the user ask follow-up questions without leaving the platform. 

Benefits of generative AI for data analytics

Generative AI offers a profound shift in how people work with analytics, from waiting for answers to exploring questions in the flow of their work. AI can unlock faster growth, more proactive strategies, and sustainable advantages for end-users and in-house teams alike. 

Faster access to insights

Asking complex business questions used to mean formulating a question, submitting a ticket, and waiting for an analyst to get back to you with an answer, a process that could take days. Natural-language AI interfaces reduce many of the steps traditionally required to find and interpret data. Now, business and platform end-users can find answers themselves. 

An AI analytics platform can allow end-users to ask questions about their account activity and receive an immediate summary of relevant trends without leaving the platform. 

• Software creators get their time back because they aren’t answering tickets, allowing them to funnel that attention to core projects.
• End-users get easy access to important insights within moments.

More accessible self-service analytics

AI democratizes data by lowering the technical barrier to insights. People don’t need to know query languages, understand data architecture, or even navigate complex dashboards to get the insights they need. No matter their business function or technical capability, they can ask questions and act on answers. This makes data both more accessible and more actionable. 

• Software creators can give end-users answers they need in-app, keeping people engaged with the product and allowing customer support teams to focus on high-impact engagements
• End-users can receive answers without having to talk to a person or open yet another tool

More productive engineering and analytics teams

Generative AI can support routine querying, summarization, and data exploration. By handling straightforward questions and reporting requests, it frees up analysts to focus on higher-value work, such as developing data strategies, investigating complex business questions, or improving data models. 

• Software creators can use generative AI to assist them in building and embedding analytics experiences, shipping features faster and with less burden on developers and data scientists
• End-users can get fast answers to in-the-moment questions without pulling in a developer or analyst.

Differentiated product experiences for software creators

Don’t just add another dashboard. With generative AI in data analytics, you can put intelligence into your product itself. It makes analytics easier to use while giving software creators new ways to differentiate their product. Instead of simply accessing data, you can build analytics experiences that proactively respond to how end-users ask questions and consume information. 

• Software creators can provide more value and differentiate their product with embedded AI analytics, without having to build new tools from scratch
• End-users benefit from the value of those insights, receiving the most relevant takeaways in moments
  

But differentiation only holds up if the AI is trustworthy. Wrong answers under your brand erode the product experience you worked to build. That’s why the governed semantic layer is vital. 

How Sisense supports generative AI for data analytics

Generative AI makes analytics more conversational, accessible, and actionable. With Sisense Intelligence built into the Sisense platform, end-users can ask questions in natural language, find the right analytics, understand what the data means, and uncover what to do next, all within the analytics experience. 

Sisense Intelligence is built on a platform with more than 20 years of production analytics experience, which means the AI is grounded in how real teams actually use their data, not just how they say they do. The suite of AI features that make up Sisense Intelligence brings together capabilities that help users move from question to insight to action:

• Assistant to turn conversations into charts and dashboards
• Search to describe what you need and find relevant analytics
• Narrative to turn charts into plain-language summaries
• Explanation to surface the drivers behind changes and results
• Forecast to project trends

How to go from data, to insight, to action with Sisense Intelligence 

1. Connect and govern your data. Sisense supports your data infrastructure and governs every experience in the permissions, tenant isolation, and access controls you define.
2. Ground your data and AI stack: The Sisense semantic harness connects to your logic, governance definitions, and LLM (bring your own or use the Sisense-managed model)
3. Build and embed analytics. Use assistant to rapidly create data models, build rich analytics experiences, and embed them directly into your product.
4. Find answers easily. An end-user asks assistant questions in plain language, and the platform surfaces relevant, validated analytics they already have permission to access.
5. Search for the right widget. Instead of navigating dashboards, an end-user simply tells search what they’re looking for. The platform returns a list of the relevant widgets the end-user needs. 
6. Understand more quickly. Narrative summarizes charts in plain language, making the key takeaways easier to understand.
7. Explain what changed. Explanation identifies the key drivers behind the result, helping users understand the “why” behind the numbers.
8. Look ahead. Forecast uses historical data to project future trends, helping end-users understand what could happen next.

Throughout the experience, the Sisense semantic harness works within the data, definitions, permissions, and governance you established in Sisense. End-users enjoy a faster, more natural way to explore analytics, while you keep insights grounded in the data and context they’re already working with. 

Your data has answers. Generative AI helps you find them.

The gap between having data and actually using it has always been the hard part. Generative AI for data analytics closes that gap, turning complex queries into plain-language conversations, raw results into clear narratives, and dashboards into experiences people actually want to use. 

Whether you’re a software creator building smarter products or a business user who just needs an answer, the path from question to insight has never been shorter. The data you already have is more powerful than you think. Now there’s a better way to unlock it.

Frequently asked questions: Generative AI for data analytics