At Bristol-Myers Squibb, I have the privilege of working for a company singularly focused on our mission to discover, develop, and deliver innovative medicines that help patients prevail over serious diseases. Accurate, high quality, and trustworthy data is central to our work in R&D, manufacturing, sales and marketing, and corporate functions. In IT, we strive to make sure the right data is available to the right audience at the right time with the right quality and controls to advance our company’s mission. With the digital data and analytic transformation that is pervasive across the health care industry, as an IT and a data professional, there has never been a more exciting time than now to transform how we manage, protect, and consume data to help patients prevail over serious diseases.      

The digital data and analytic transformation is not unique to health care. Everywhere you turn, in industry after industry, the focus is on digital and analytic transformation with companies in a race to become the digital enterprise powered by machine learning and AI. This transformation thirsts for trusted good quality data. Yet the one common theme, in my conversations with IT, analytics, and business leaders across industries, is the persistent dissatisfaction on the state of data in the modern enterprise. There is no disagreement on the aspirations of treating data as an asset and a fuel for the modern enterprise. Yet almost all enterprises suffer from the weight of legacy data infrastructure, dysfunctional data stewardship and poor rate of return on organizational investments in data management. 

So what is my solution?

I believe it is data governance 2.0, a pragmatic, relentless, self-sustaining data governance aided by machine-assisted data stewardship. I define data governance 2.0 as the combination of people, process, and technology that precisely articulates the data domains and assets that are critical to the enterprise (high risk and/or high value), defines the baseline of where the enterprise is today in managing the data (data ownership, data quality, data readiness), defines the target state of where the organization needs to be, orchestrates pragmatic ownership and asset management processes that efficiently fits in the organizational structure and culture, relentlessly monitors utilization and value, and course corrects without dogma when needed. This data governance 2.0 should use algorithmic automations, machine learning, and AI to reduce the organizational burden and bureaucracy so that human involvement in data governance shifts from mundane data stewardship tasks to qualitative action directed by the “machine.”