A Ferrari Without Fuel?

While data is exploding and dancing like never before, AI is still not able to convert this ocean into the juice of actionable intelligence as fast as, we hoped it to. What could be holding AI back?

- Pratima Harigunani

Data feeds, it nurtures and it helps the data-lakes grow. But is too much of any good thing always a good thing?

With breakthroughs that brought affordability, scalability and simplicity for collecting and analyzing more data; it was easy to presume that Artificial Intelligence is all set for explosion and exuberance. But it turns out that just having cheaper and better compute technology or storage or databases was not the answer yet. The real answer is still elusive.

Data Scarcity in this age!

As counter-intuitive and preposterous as it may sound, we may be surrounded by mountains of data and still be starting at a serious dearth of data.

Data is crucial for algorithms to work in an AI context, remarks Arup Roy, Analyst, Gartner.

The volume of data enterprises accumulate today has grown tremendously due to the increased sources of data and customer touch-points, agrees Faisal Husain, Co founder and Ceo, Synechron. But the success of an AI-enabled program depends on the quality and quantity of data transmitted through the data pipeline, as he underlines next.

Deficit in AI is not in the start; but scale-ups, argues Vishal Vasu, CTo, Dev Information Technology. “Though a lot of enterprises are taking a plunge in, there are very few that stand the test of time to grow and scale. Access to data is the key. once you have the data, it has to be cleaned, de-structured and again re-structured. Without right data you cannot train your AI models. And if your data is not of sufficient quality, you need a lot of resources to fix it which can be time and capital intensive.”

Data Half-baked, Half-Squeezed