Data governance at the speed of business

IoT devices, wearables, SaaS applications, and social media channels are but a few of the sources from which data enters organizations today.  When thoughtfully combined and analyzed, data from across those channels can deliver new insights and unlock new opportunities. Organizations that institutionalize and scale those insights across the enterprise can make informed decisions faster and ensure no lesson needs to be learned twice.

Converting siloed information into enterprise-wide insights requires a commitment to data governance, and doing it right is more than a passing endeavor. At its best, data governance can adapt and scale as a company’s strategy evolves, accommodate growing troves of data and, not least of all, provide a common nomenclature and trust that eases communication across business units and functions.

If data is the new oil and speed is the currency of business, then data governance is the link that fuses the two. It’s the set of systems, policies, and procedures an organization uses to ensure teams have the right data at the right time in order to enhance and automate processes, products, and experiences. It’s an exciting and valuable function in today’s competitive landscape, but getting there takes significant work. In this article, we lay out a three-step process to develop and mobilize a data governance program that moves at the speed of business.

Step 1: Establish foundational components

In many organizations, data governance is often limited to compliance, privacy, and security. These are critical domains, no doubt, but widening the scope and diversifying the representatives who oversee it can usher in more business value through faster, more informed decision-making and operational efficiency. Any data governance program should include four primary components: a data-governance steering committee, data owners, data stewards, and a data management team.

First, take stock of your data governance steering committee. If you don’t have one, assemble one. Include leaders from all business units and functions. If you have one, but it lacks cross-functional representation, augment it. Every business unit and function should have a representative on the committee. Depending on the size and scope of the committee, this could be a C-level executive or someone who works closely with the BU’s core data and IT systems.

Representatives must first articulate the committee’s objectives, which should include a set of both business- and compliance-driven objectives. Articulating these objectives will help illuminate the data governance objectives the steering committee is best suited to carry out.   As an example, consider a healthcare organization that manages administrative processes on behalf of large hospital systems. The steering committee identified an objective to drive more automation into reporting processes. To accomplish this objective, they determined they would first have to drive common data definitions across their client base.

Once you’ve assembled a steering committee and defined its objectives, it is time to assign roles. Each BU and function represented should have a data owner, who will establish and uphold the policies and procedures that will, through an iterative process, alleviate the worst data-quality issues in their respective domains. Sticking with the healthcare example, consider that each business unit defined claim denials slightly differently, which impeded the organization’s broader adoption of solutions that would allow them to automate claims reporting.