Before May, I had little to no knowledge of what big data had to do with AI. I’d never heard of “data chaos”, and I certainly didn’t know that AI is not in a place where it doesn’t still need human assistance. Isn’t the whole point of AI to replace humans?
AI doesn’t always work?
Pramata, the company that lives and breathes extracting useful data for big enterprises, takes a rather controversial stance. AI, especially when it’s applied to big data, can miss what enterprises are looking for in the first place—critical, accurate and useful information. Hmmmmm? I would never have guessed AI alone doesn’t always work.
I was asked to research problems financial executives and their teams are facing when they try to harness the data they need to do their jobs. To that end, we started asking what finance executives, managers, and particularly controllers in big enterprise care about, what keeps them up at night, and how they look at the data that is most relevant to them. I can’t wait to see the results, but as our survey gets underway, I started talking to coworkers who have helped companies with this “data” stuff for years.
“Can you help us?”
They told me that finance folks often look to apply AI to big data so they can find solutions for increasing company revenue or finding revenue leaks. The passionate Pramata team told me how companies complain that big data initiatives take too long and even when they are working, the critical information they need never surfaces. These same big companies show up on Pramata’s doorstep when they find AI and big data don’t work.
Data chaos, what’s that?
What Pramata has discovered through the years of unearthing critical data is that, in order for financial executives to analyze, and ultimately uncover revenue opportunities, it’s critical that they address their data chaos. They need clean, accurate, quality data versus just harnessing and curating massive quantities of data. In other words, AI applied to big data can provide important information, but in order to accurately predict and increase revenue, they need precise data.
We have data gaps? Where?
Praful recently packed rooms with CFOs in New York and Silicon Valley by taking on their assumptions that big data approaches work. He explained to them that his experience with big enterprise shows that struggling with AI and big data is common and needs a completely different approach. That’s because big data solutions too-often leave companies with gaping holes in their critical data.
I was thrown into a confusing whirlwind of big data and AI. Though these are powerful when applied to the right problems, our CEO, Praful Saklani will explain why they aren’t nearly as effective as they should be, and they rarely work as promised.