In the current technology landscape, nothing elicits quite as much curiosity and excitement as artificial intelligence (AI). And we are only beginning to see the potential benefits of AI applications within the enterprise.
The growth of AI in the enterprise, however, has been hampered because data scientists too often have limited access to the relevant data they need to build effective AI models. These data specialists are frequently forced rely solely on a few known sources, like existing data warehouses, rather than being able to tap into all the real-time, real-life data they need. In addition, many companies have great difficulty efficiently and affordably determining the business context and quality of massive amounts of data instantly. Given these difficulties, it’s easy to understand some of the historical barriers to AI acceleration and adoption.
At the end of the day, data only becomes useful for AI—or for any other purpose—when you understand it. Specifically, this means understanding its context and relevance. Only then can you use it confidently and securely to train AI models. The only way to achieve this is with a foundation of “intelligent data.”
Over the years, we’ve moved beyond the collection and aggregation of data to drive specific business applications (data 1.0), and organizations have been able to create well-defined processes that allow anyone to access data as its volume, variety and velocity continue to explode (data 2.0). But this simply isn’t enough. We’ve now reached a point where intelligent datais needed to truly power enterprise-wide transformation (data 3.0).
As an example, consider the challenges a company would face trying to redefine its traditional relationship with its customer base. Let’s say that you’re a company that makes razor blades and the goal is to sell them by subscription rather than over the counter. Guiding such a disruptive change requires input from a multitude of data sources (databases, data warehouses, applications, big data systems, IoT, social media and more); a variety of data types (structured, semi-structured and unstructured) and a variety of locations (on-premises, cloud, hybrid, and big data).
