There is currently a lot of hype surrounding generative AI and GPT chatbots. And when playing around with the tools, it’s hard not speculate about their impact on creative text-related work processes.
I noted it three years ago when writing AI for arts, and by now most people have enjoyed asking GPT-3 or Bard to write them a poem in the style of their favorite author – which has led to a deluge of articles and self-proclaimed experts with varying degrees of hyperbole.
A bit less obvious on the surface is how GPT and chatbots might change and complement the more traditional data-based analytics roles. And how the typical toolsets of data lakes/warehouses, reporting tools and Excel will evolve.
It’s still early, but we (CIO:s, CEO:s and world leaders) believe that we’re at a stage where it’s important to explore the possibilities to avoid overhyping new tools, and to prevent us from lagging behind and getting stuck in doing things the traditional way. The evolution is extremely fast with substantial amounts being invested into developing tools, and this process will be user driven with data consumers and analysts requesting to use them.
Equally oversimplified, the new GPT models introduce a new process and tool to work with data that I simplify here as “Ask, get answer” (we’ve seen tools like that before in natural language querying tools, such as Power BI, but it failed to gain much traction and the new GPT tools are far more powerful).
As mentioned, we’re still at an early stage. But to give you an idea of how it works even with the previous generation of tools, I logged on to Google Bard and asked: “Can you please draw a bar chart with the GDP per capita for Sweden from 2013 to 2023?”
Seeing this, both generative AI enthusiasts and scaremongers will sense a revolution. We remove two or three steps of the process. There’s no need to go look for data, no more copy pasting and no need for data engineers or programmers to build anything – as you notice Bard tries to write code to generate the graphs.
This opens up more interesting applications. Let’s ask it to create a cash flow model.
And we can then use this definition to quickly ask for information, without having to go collect the data, build excel formulas and create a layout.
In general, my argument is that GPT will bring ease of use and a reduction of work and speed in the analysis. But the counterargument is that data will have a trust issue.
Anyone working with data in analytics or engineering knows this is an age-old question. The current challenges with data lakes- and warehouses often comes down to issues of data quality, definitions and lineage. The main challenge with data platforms, or even with Excel sheets, is often how stable/correct a model must be compared to its building cost. The new opportunities of generative AI will make these questions even more urgent.
But in practice, these new toolsets will bring different opportunities to varying companies and roles. Bloomberg, as a financial data provider, will have a different challenge than a stock market analyst giving advice to investors, or a company trying to understand the connection between customer communications and sales.
A great way to avoid getting surprised by the competition is to try and learn about the opportunities and challenges that comes with integrating new tools into traditional data flows, systems and business processes. This also prevents you from being oversold on expensive miracle solutions.
In the next blog we’ll look into the issue of using proprietary data in a chat solution. We’ll also discuss how building a ChatGPT-based solution compares to traditional data flows.
Författare: Daniel Hedblom, BI Konsult, Random Forest.