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February 21, 2024
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Data Management
Data governance has been a hot topic for at least several years. It has been discussed in social gatherings of data professionals, asked about in meetings, and too often casually included in requests for proposals by adding "and Data Governance" at the end of a requirement. As if it was something as self-explanatory as a burger and fries on a fast-food restaurant's menu.
What is Data Governance?
First, let's look at what data governance is. According to the Data Management Body of Knowledge (DAMA – DMBOK2), Data Governance is defined as the exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets. It provides direction and oversight for data management by establishing a system of decision rights over data that accounts for the needs of the enterprise.
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Data Governance is one of eleven domains of Data Management. It does not include important processes such as data security management, data architecture development, and data quality management. Data Security, Data Architecture, and Data Quality are all separate domains of Data Management that are very important to the organization. Data Governance is not a substitute term for Data Management. It is important to establish a common vocabulary before engaging in further discussions.
Implementation of Data Governance
Implementation of Data Governance is a very challenging endeavor for an organization. Data is growing in volume and variety at a speed never seen before. The ever-changing business environment requires reorganization much more frequently. These factors greatly influence the approach to how Data Governance needs to be implemented. Trying to implement company-wide Data Governance in one go will be far too slow and expensive. Therefore, I would advise using the Agile approach and getting inspiration from principles based on the Agile Manifesto. Most importantly, set priorities, move in small steps, show progress frequently, be flexible and ready to adapt in late implementation stages, work closely with data owners and users, and measure your success based on the benefit created. You might also want to explore DataOps, a DevOps-inspired practice, processes, and technologies used to create business value from data. Just a reminder - using the Agile approach does not mean neglecting the final goal – company-wide Data Governance. It has to be the unifying element of all implementation activities.
Responsibility for Data Governance
Although Data Governance implementation is often initiated by the IT department or by the Business Intelligence Competency Centre, governing data is not the responsibility of these units. Data Governance needs to be embedded in business processes where data is actually created and within the units where data owners reside. It is based on the organization's strategy and cannot be performed without strong support from the organization's top management. In many cases, the education of an organization comes before Data Governance implementation.
Data Governance is not a "one-size-fits-all" product. Its implementation requires thorough self-assessment on the organization side and careful scoping of the implementation program. Data Governance principles, policies, and procedures depend highly on the goals an organization wants to achieve and are unique in each case.
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