How AI Makes Big Data Smarter Posted on : Mar 23 - 2020

The artificial intelligence (AI) road from unlimited (yet largely nonspecific) potential to concrete, specific business benefits, is like taking a long road trip with kids — palpable excitement alternated with restless tension and cries of “Are we there yet?”

So, are we “there” yet? Well, no, but we are certainly closer than we have been, and by further examining the data that underlies these systems, we can progress closer to "there" by recognizing measurable ROI from the ability to make better decisions with AI-powered analytics technologies.

We’re at that inflection point in the AI hype cycle. Most companies that say they are using AI have yet to gain any value from their investment, according to the 2019 Artificial Intelligence Global Executive Study and Research Report from MIT Sloan Management Review and Boston Consulting Group (BCG). They continue to plug away, however, even though the payoff — new products, increased revenues and optimized efficiencies — is likely further out than previously imagined.

I’ve outlined the tremendous agility and precision that AI is poised to bring to the supply chain — as well as the importance of building in domain-specific intelligence that maps industry and function-specific capabilities with data to solve business problems.

Big Data That’s Getting Bigger

Just as Waze and other “smart” GPS systems use data to optimize the family road trip, I believe the road to AI returns will also be built on data. There’s clearly no shortage of it: The concept of big data — the large volume of structured and unstructured data collected by businesses on a regular basis — has been around for nearly 15 years, and in that time, it’s only continued to get bigger.

This data is coming from a variety of places — internally, it’s business transactions, back-office information, customer and prospect data, IoT sensors providing machine information, etc. The supply chain is a particularly rich source of data — each hop from raw materials through shipping to the end user provides valuable historical information. The real power comes from matching internal and external data with third-party data.

Companies realize that their operational data leads to valuable insights. Yet this internal information is biased and has gaps. So, organizations are increasingly looking to add additional sources of data to their analysis — anything from weather and demographic information to satellite information. While transactional data remains a foundational data asset, a 2018 Gartner survey showed that nearly half of organizations are using external sources. The most common of these are weather data — for example, by correlating sales with a weather stream, retailers can project demand for snow shovels. View More