Along with poor data quality, these issues combine to deprive organizations' data platforms--and the machine learning and analytics models they support--of the speed and scale needed to deliver the desired business results. To understand how data management and the technologies it relies on are evolving amid such challenges, MIT Technology Review Insights surveyed 351 CDOs, chief analytics officers, chief information officers (CIOs), chief technology officers (CTOs), and other senior technology leaders. They are succeeding thanks to their attention to the foundations of sound data management and architecture, which enable them to "democratize" data and derive value from machine learning. Pushing these to the edge with advanced data technologies will help end-users to make more informed business decisions -- the hallmarks of a strong data culture. Organizations' top data priorities over the next two years fall into three areas, all supported by wider adoption of cloud platforms: improving data management, enhancing data analytics and ML, and expanding the use of all types of enterprise data, including streaming and unstructured data.

In this context, effective data management is one of the foundations of a data-driven organization. But managing data in an enterprise is highly complex. As new data technologies come on stream, the burden of legacy systems and data silos grows, unless they can be integrated or ring-fenced. Fragmentation of architecture is a headache for many a chief data officer (CDO), due not just to silos but also to the variety of on-premise and cloud-based tools many organizations use. Along with poor data quality, these issues combine to deprive organizations' data platforms--and the machine learning and analytics models they support--of the speed and scale needed to deliver the desired business results.

JPMorgan Chase sees artificial intelligence (AI) as critical to its future success. And the mega-bank has a big advantage over many of its smaller rivals: the massive amount of data it gathers from sources such as the 50% of U.S. households with which it has some form of relationship and the $6 trillion worth of payment flows it handles daily. But until recently, identifying and pulling in relevant data to train AI models was taking up around 60% of the time of the bank's growing army of data scientists. That was an inefficient use of an expensive and relatively scarce resource. Now a new data platform the bank has developed, called OmniAI, is helping it to get relevant data into its models much faster.