Data governance is the orchestration of a company's staff, technologies and processes to transform data into an enterprise asset that yields business value for the organization. Data governance engages the policies and procedures specifying how decisions are made in handling data, how information resources are allocated and how accountability for results is tracked.

The analogy between governing the data in an enterprise and a system of checks and balances in a federal system of government is a useful one. Customers vote with their dollars, and those managers and executives whose messages, products and services win those dollars stay in office - keep their jobs. That is the ultimate check on the forward motion and balance of the enterprise. An executive function provides the leadership in setting priorities as to which projects, products and data initiatives have the best chance of resonating with the market. A resource allocation function determines the hurdle - prospective ROI - that must be surmounted by the executive initiative(s) to gain funding. At a high level, a compliance, monitoring, and auditing function (with a whole host of external regulatory agencies as well as Sarbanes-Oxley, HIPAA and Basel II) interpret the business practices as being consistent with the integrity and well-being of both the enterprise and customers. At an implementation level, project management keeps the initiative in line with the vision embodied in the governance model by surfacing issues and obstacles, working to resolve them or escalating to request executive intervention to stop losses. All of this, of course, is easier said than done.Because this is a short column, let's go straight to the heart of the matter. The weak link in data governance is between policy formulation and implementation. A data governance roadmap is the key to connecting the dots between the business and the technology in the IT department, which is an essential part of implementing every business process. The roadmap winds it way through policies, standards, success criteria, key performance indicators, accountability and authority, and onto business results. The best data governance roadmaps trace a route that resembles a capability maturity model (CMM) - with a couple of differences.1 The roadmap leads from the current state of enterprise data management capabilities in the direction of implementation. In contrast, a CMM leads from the heroics of the professional staff to the ideal state by means of a defined, repeatable, measurable, process of continuous improvement. Fewer heroics are good. Governance and CMM interact iteratively by means of incremental advances in capabilities enabling corresponding improvements in best practices in the implementation cycle. The roadmap moves from the cow path to the autobahn as implementation activities advance from readiness and engagement through integration to mastery. The goal is information availability - preferably with a low latency that maps to the requirements of the information demands of the business. Examples of policies that form the backbone for data governance look like this:

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