Data governance usually invokes anxiety in users and decision-makers because they commonly view it as an organization monster deliberately put in place to interfere with their day-to-day work. On the contrary, a data governance strategy attempts to eliminate barriers to becoming a more data-driven organization.
Microsoft describes data governance as the collection of processes, policies, roles, metrics, and standards that ensure effective and efficient information use. To take it one step further, The National Institute of Standards and Technology adds that the model establishes authority, management, and decision-making parameters related to the data produced or managed by the enterprise.
More and more, business leaders are stating that their organization is data-driven. Many are, some are still in the middle of their transformation, and some may be data-driven but aren't working as efficiently as they could be. Do you know into which category you fall?
Developing a data governance strategy is the first step to implementing a transformation to become a data-driven organization. It should answer some of the following questions:
Ultimately, it ensures that data is audited, evaluated, documented, managed, protected, trustworthy, valuable, and will run smoothly. Taking the time to plan your strategy, your data will be more accessible and accurate.
High-quality data: Build user and decision-makers confidence in data knowing it is safe, complete, and consistent. Confidence in data builds trust in the decisions made with that data.
Improve cost controls: Reduce software, hardware, and people by reducing data duplication introduced by information silos. Data duplication increases up-front and long-term maintenance costs for software and hardware. It adds an unnecessary burden for users and decision-makers.
Single source of truth: Improve user and decision-maker efficiency and productivity by eliminating time spent reviewing redundant, overlapping data sets.
The Cloud Data Management Capabilities (CDMC), created by a global industry council, prescribes a framework for what data governance should encapsulate.
Some key capabilities are:
Data cataloging and discovery: Identifying, preferably automatically, and recording data assets in a unified manner to enable data search and discovery.
Data classification: Tagging data with sensitivity classifications to secure use and protection.
Data ownership: Owning data for cataloging, classification, security, and quality by users within the organization.
Data lineage: Pinpointing the data's origin, any transformation it's undertaken in the past, and current uses.
Data quality: Ensuring data is high-quality based on accuracy, completeness, consistency, validity, relevance, and timeliness measures.
Define data governance goals and objectives: Review the organization’s long-term and short-term business goals and define data governance goals and objectives that align with the organization’s business goals. Then prioritize data governance goals and objectives to guide the development of the organization’s data governance strategy and keep from being overwhelmed.
Secure executive sponsor and key stakeholders: Recruit an executive sponsor who understands the significance of a data governance strategy, recognizes the value of a data governance strategy, and who communicates this information to the organization’s leadership team. Recruit key stakeholders to fill additional roles of the data governance team, such as data owners, data stewards, and general users and decision-makers.
Assess existing data governance program: Identify the strengths and weaknesses of the organization’s existing data governance program. EDM Council’s CDMC, for example, provides organizations with a method to assess an organization’s existing program against industry best practices. Then prioritize areas of improvement based on the organization’s business and data governance goals.
Document data policies and processes: Document data assets guidelines for data origination, storage, quality metrics, and security. Data policies provide a means for ensuring data is used acceptably. Processes implement data policies by providing a formal framework for users to follow.
Implement, evaluate, and adjust: Implement new technologies or processes or modify existing ones to fit the organization’s data governance strategy. Review the data governance strategy regularly to measure the effectiveness of the organization’s data governance goals. Adjust technologies or processes to change with the changing needs of the organization.
Think big but start small: If you are new to data governance, try to wait to do everything right away. Instead, review your data governance goals and priorities to identify a pilot engagement. A pilot engagement enables an organization to test ideas and understanding in a limited scope, develop skills, and validate the approach before committing to extensive arrangements.
Appoint an executive sponsor: An executive sponsor is crucial to communicate a data governance strategy’s business value and success to executive leadership — additionally, the executive sponsor advocates for adopting a data governance strategy for the broader organization.
Build a business case: Build a business case by identifying the benefits and opportunities a data governance strategy will bring to the organization. Then, align data governance benefits to the organization’s long-term and short-term goals to better demonstrate these benefits.
Develop meaningful metrics: Too many or too few metrics make it difficult to understand whether the data governance strategy meets the organization’s data governance goals and delivers business value.
Communicate with all levels: Involve individuals likely to be impacted by a formal data governance strategy throughout the planning and implementation of the data governance strategy early and often.
An organization could create and implement a data governance strategy independently. However, implementing a data governance strategy will run more efficiently with experienced help. An outside firm offers:
A firm with data governance experience will guide an organization through industry-standard processes providing a smooth and successful implementation. Data governance is not simply hiring a new employee or purchasing new software; it is a strategy that blends an organization’s people, policies, and technology.
If you want to partner with a highly experienced firm in data governance, contact us through the form below.