By Anand Mahurkar, Founder and CEO of Findability Sciences
Enterprise artificial intelligence is a game changer in today’s data-driven world, and as such, manufacturing organizations are increasingly adopting AI to help improve business processes, transform products and business models, resulting in revenue growth, reducing costs and improving customer service. In other words, we are living in a time of AI-led digital transformation.
However, to make this all happen, organizations require data—not just a huge volume of data, but rather a variety of data from disparate internal and external sources.
Manufacturing organizations require “wide data.”
The concept of big data has been around for a long time. Manufacturers have long tapped into big-data analytics to gain an edge over their competitors. But with today’s machine learning applications, big data simply isn’t enough. To provide meaningful training data to ML applications—think predictive analytis for optimal decision making—manufacturers must adopt the concept of wide data.
Whereas big data focuses on analytics that can only tell you what happened in your organization (honing in on volume, velocity and variety), wide data narrows its focus on variety.