Revolutionizing data analytics With Generative AI

Generative AI has emerged as a groundbreaking technology by engendering some incredible and valuable use cases in the field of data analysis. Gen AI has disrupted the data analytics landscape by introducing unprecedented capabilities in data processing, interpretation, and value generation. Since Generative AI first exploded onto the scene in 2022, organizations have been exploring ways to use Gen AI to enhance customer experiences, reduce costs, and increase efficiency. This post is my take on how Gen AI is shaping how organizations interact with, analyze, and derive value from their data.

The Business challenge around data analytics

Data is the lifeblood of any organization, but generating a meaningful insight from an overwhelming amount of data is like trying to spin hay into gold. It makes me recall the watery irony from Rime of the Ancient Mariner, who said “Water, water everywhere, nor any drop to drink,” except here, I’d change that to “Data, data everywhere, nor any value to see.” Businesses are still scrabbling to generate value from data, as their data becomes increasingly complex and often comes from disparate sources. The sheer volume of data, siloed information across departments, and complexity in data structures and types make it nearly impossible to interpret and create a shared understanding of your data across multiple domains. There is also the fact that 80% of data is unstructured (such as emails, social media posts, videos, audio files, and documents) and not easily accessible for analytics. Non-technical business users often face challenges with complex analytical tools and end up hiring skilled data scientists as budgets skyrocket. Moreover, data evolves over time, and decision-makers cannot afford to miss out on real-time insights to stay ahead of the curve.

Common analytic Generative AI use cases

Now that I have mentioned some of the challenges around organizational data, I’ll share my insight on how Gen AI is coming to our rescue.

Generative AI is powered by large language models (LLMs), which are large-scale deep learning models pre-trained on vast amounts of data. The transformers can generate text, images, videos, graphs, reports, and summaries based on a prompt. A prompt is an instruction that tells a generative AI tool what to do, such as “summarize this blog post in 50 words or fewer.”  Gen AI developers create AI models, which are based on large volumes of data, that business users can use to make decisions, predictions, or recommendations. Unlike traditional rule-based systems where algorithms are explicitly programmed, data-driven generative AI excels at learning patterns, relationships, and behaviors from the data it encounters.

One data-related use case for Gen AI is to automate data cleaning and preprocessing, real-time insight generation, predictive scenario modelling, and automated reporting and visualization. For example, a retail company can use Gen AI to analyze customer purchase patterns, generate personalized marketing recommendations, predict inventory requirements, and even optimize pricing strategies. This is where the magic of Gen AI begins, and the list of use cases is extensive and ever-growing. By using Generative AI, you can transform your data analytics from a complex, time-consuming process that only specialists can understand to an agile, accessible, and intelligent decision-making tool.

But I’m just getting started. The above diagram shows even more applications of Gen AI in data analytics, including: testing to SQL or Python by generating queries or code from natural language prompts, automated metadata generation by asking Gen AI to create descriptions and tags for datasets, creating a chatbot to provide 24/7 customer support based on your organizations policies and documentation, generative business insights (BI) by asking gen AI to create dashboards or visualizations of your data, and even using Gen AI to perform one-time analyses of a particular data set

Getting started with Gen AI in your analytics

Before using Gen AI in your analytics or BI process, ’first determine whether your data is ready. This blog post Is your data ready for Gen AI can help with that process. Text to SQL/Python: bridging the technical divide

One of the capabilities of Gen AI that I find especially exciting is its ability to convert natural language prompts into precise SQL queries. Imagine a marketing manager wanting to understand customer segmentation without deep technical expertise. Instead of relying on data engineers, they can now simply enter "Show me the top 10% of customers by annual spending who made purchases in the last three months" into Gen AI and get an answer!

Generative AI instantly translates this human-readable prompt into a complex SQL query, retrieving the precise information needed. This capability saves time, yes, but what I find especially powerful is that it empowers non-technical users to perform sophisticated data analysis independently. Gen AI can single-handedly break down silos and democratize data access across your organizations.

Beyond SQL generation, Generative AI goes a step further and accelerates data science workflows by converting natural language descriptions into fully functional Python code. Data scientists and analysts can now describe their analytical requirements in plain English, and the system generates the corresponding Python scripts for data manipulation, statistical analysis, and machine learning model development. To find out more on this topic, this blog post, Generative AI in data analytics - how AI is making it easier to access data, might be helpful.

Conversational AI: transforming prompts into meaningful insights

Gen AI has the potential to generate intelligent recommendations and contextual suggestions for real-world impact. Consider these practical scenarios: a healthcare provider uses Gen AI to analyze patient outcomes across multiple departments, or a retail chain identifies cross-selling opportunities by generating complex customer behavior analyses. Gen AI can provide recommendations by identifying potential hidden patterns in data, suggesting relevant additional analyses, highlighting potential risks or correlations that might not be immediately apparent, and offering contextual insights that enhance decision-making.

By using Gen AI, for example, a sales director querying monthly revenue might not only receive the requested data but also get additional insights about potential growth opportunities, seasonal trends, or emerging market segments.

Automated metadata generation

This is one of my favorite uses of Gen AI, because it can take over tasks that are important but that can be tedious for humans to perform. Gen AI can automatically create rich, contextual metadata for data products by analyzing the content and structure of datasets. It can generate comprehensive descriptions and identify and suggest relevant tags for data assets.

Semantic tagging models can create intelligent, context-aware tags and use natural language understanding to identify nuanced relationships between data elements. The auto-tagging features improve data discoverability and data management, boosting efficiency by saving time on manual tags creation.

That rich metadata in turn not only simplifies collaboration by sharing assets with internal or external stakeholders but also facilitates compliance with data governance policies and controls.

Chatbots: Amplifying customer support

Organizations have been using conversational AI to enhance customer services with the help of chatbots. Conversational AI works by using machine learning (ML), which learns from past interactions, and Natural Language Processing (NLP), which responds to human interactions. Chatbots simulate human conversations to improve customer experience by providing instant responses around the clock.

Chatbots can help reduce or eliminate waiting times for basic inquiries and help users find relevant information in real time. Chatbots can be helpful in knowledge base integration and in providing context-aware responses to users. All these benefits go a long way in enhancing user satisfaction, reducing support costs, and providing consistent and personalized assistance to users. While all these benefits seem lucrative, we need to make sure that chatbots are extensively tested with clearly defined constraints and guardrails to ensure accuracy and relevance of responses.

Gen BI: Democratizing data analytics

This is one of my favourite applications of Gen AI, because I believe that most internal users can benefit from having direct access to company data. “Generative BI” is a term I use when business users use conversational AI to access company data to generate customized visualizations and dashboards. So, your Gen AI tool becomes a Gen BI tool.

You can use Gen BI to provide self-service AI analytics, which can integrate NLP, authoring tools to whip up custom dashboards, data integrations, and collaboration for easy consumption of reports.

What Gen BI does, in essence, is democratize access to analytics by taking it out of the hands of the BI team and out directly to users. Gen BI helps more of your non-technical users and stakeholders work directly with data. Integrating AI into BI solutions enables automated generation of dynamic dashboards, intelligent chart and graph creation, automated visualization recommendations, and context-sensitive data representations.

Next steps: Trying Gen AI with your data

In this blog post, I pointed out some of the challenges with data analytics and provided five use cases you can try to start using Gen AI in your analytics today. Gen AI has the power to make all your siloed data more accessible and ultimately more usable for everything from customer chatbots to insightful sales dashboards.

If you want to give it a try, AVRIO is an AI-powered data platform that generates real-time analytics, insights, opportunities, and recommendations from all of your data (structured and unstructured) through intuitive conversations. Reach out for a customized demo here.