AI-Powered Data Analysis pt. 2

This is a continuation of the tech showcase that began on August 2^nd.

AI, and generative AI in particular, has been touted as a transformer of the research process from end to end, from design to delivery. For most research activities, it can be argued that AI speeds up mechanical processes but adds little value other than speed and cost savings. Reducing time and money are, of course, nothing to sneeze at, and perhaps characterizing AI-enabled research tools as speedy plow horses is a gross simplification. However, these horses pale in comparison to AI-powered analytical tools because the application of AI during analysis delivers an entirely new benefit: the ability to know why.

Traditional qualitative and quantitative methods make the what clear and give you good leads into why the audience thinks as it does, but it does not entirely bridge the gap between guessing and knowing, at least within reasonable doubt.

Quantitative data is voluminous, and even Big Data can be analyzed relatively quickly using pre-GenAI tools, but quant data doesn’t quite tell you why someone has a particular behavior or attitude. If qual and quant are the two horses pulling your cart, the cart can’t move any faster than the slower horse, which is qual. Historically, it is prohibitively expensive to interview enough people to reach the same level of confidence you have in quant samples, and, if analysis time was weight, the data cart the qual horse would pull would be extremely heavy.

Yet, you need the qualitative perspective because, as many believe, that is where you learn why. Regardless of how much or how little qual data you have, AI-enabled tools can analyze text, images, and video more quickly and consistently than you can. (It might even be less biased, but that could depend on the bias that you trained or coached into it, so it’s not necessarily one to chalk up for humans.) When you add the ability to conduct qual at the scale of quant to AI’s ability to analyze a large volume of data (and to integrate it with quant), you get an unprecedented opportunity to narrow the gap between guessing and knowing why.

The recent GRIT Insights Practice Report leads us to dub AI-Analytics-Automation as the “Axis of Insights.” At least 40% of buyer-side insights professionals, full-service researchers, strategic consultants, technology providers, and data and analytics providers are using AI-powered text analytics, and text analytics is the most common application of AI in each of the eight GRIT segments. Analytics is running on the inside track in the AI adoption race, and there are reasons why.

This is the conclusion of a two-part showcase that began on August 2^nd. It’s for anyone who has doubts about AI-powered analytics and wants to see proof, anyone who’s used or dabbled in AI-powered analytics tools and wants to see what it can do in a larger context, and anyone already using AI for end-to-end research and wants to know what’s new. You’ve no doubt heard a lot about AI-powered analytics – now you can see for yourself!