Could Conversational AI-Driven Data Analytics Finally Solve the Data Democratization Riddle?
OPENING SCENE: EXEC IN FUTURISTIC OFFICE
A spacious, modern office. Floor-to-ceiling windows offer a panoramic view of the bustling city below. The space is both stark and elegant, with sleek, minimalist furniture and a few carefully chosen art pieces on the walls.
An impeccably dressed, mid-40s executive focuses on her computer monitor and asks "Please show me last month's revenue". Expressionless after a glance, she then asks "Break out revenue by sales region". Now, with a furrowed brow and a quizzical look, she asks "Show me net profit margin, by region". After a long stare, she exhales slowly and reaches for her phone…
Data Democratization –the process of making data accessible to everyone in an organization, regardless of their technical skills.
The democratization of data is a riddle that old-school Ralph Kimball acolytes like myself have been trying to solve for decades. Beginning with the user-friendly data models (data warehouses) and then onto the plethora of highly evolved, user-friendly business intelligence tools now available, we have come a long way.
And yet the ability to derive new insights from data, for the most part, remains the realm of data analysts, data scientists, and business analysts. For the vast majority of others within business organizations, the technical moat around data **** (real or imagined) persists.
A Glimmer of Hope?
In late November 2022, OpenAI's release of ChatGPT enabled the general public (read: non-technical) to interact with a large language model (LLM) by simply typing in a request (prompt) in their natural language. Through this conversational user interface, users could prompt the LLM to answer questions about data it had been ‘trained' on. In the case of ChatGPT, it was trained on, well… the internet.
ChatGPT put incredible data processing power in the hands of anyone who had access to it. As we became aware of this mechanism's possibilities, many of us in the Data Analytics field soon began to ponder its potential impact on our own space.
We didn't have to ponder for long…
A mere four months after the initial release of ChatGPT to the general public, OpenAI released an alpha version of a ChatGPT plugin called Code Interpreter. With it, anyone could load a dataset into ChatGPT, type a few prompts and invoke Python to perform regression analysis, descriptive analysis and even create visualizations. All without having to write any code!
The release of Code Interpreter gave us all a glimpse into how conversational AI-driven data analytics could work. It was mindblowing!
Not long after this, citing ChatGPT's already established ability to write code (SQL, R, and Python, to name a few) along with the nascent capabilities of Code Interpreter, many began to predict the eventual demise of the data analyst role. (At the time, I begged to differ and even wrote an article about it).
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Granted, such a prediction didn't seem like much of a stretch when you considered the possibility of even the least technically inclined in your business organization being able to derive insights from their data by simply typing or even verbally asking their questions.
So could Conversational AI-driven Data Analytics actually be the key to bridging the technical moat between data and its democratization?
Let's take a closer look.
The Current State of Conversational AI-driven Data Analytics
So… it's been almost a year and a half since that alpha version of Code Interpreter was released and how much progress have we made with conversational AI-driven data analytics? Probably not as much as you might have anticipated.
For example: In July 2023, ChatGPT's Code Interpreter was rebadged and rereleased as Advanced Data Analysis. Not only was the name of Code Interpreter changed, but so was… umm… err… Well, at least its new name provides a more accurate description of what it actually does.
