When speaking of data governance, the term data governance framework crops up. In this respect, a company's data governance refers to their management of data governance, while a data governance framework is essentially a document. Unique to each organization based on their goals, needs, and features, a data governance framework describes the rules, responsibilities, and processes used to structure their data governance program. It should be referenced by many different actors. It’s meant to provide standardization across the entire organization, covering data standards, data privacy, business strategy, and the responsibilities of key individuals.
A data governance framework begins with a data governance strategy which is the process of coming up with the framework. Along this journey, several data stakeholders, leadership, and end user perspectives are gathered to develop a fitting data scope, organizational structure, data standards and policies, and oversight and metrics.
The need for a process came into being because historically data management was left to IT departments who were not outfitted for the pitfalls of the meaning of data, such as the privacy issues. The onslaught of several data breaches brought to light that leadership and other stakeholders must have input into data governance and therefore data management from the beginning of policy planning. Essentially, because of the potentially sensitive nature of data, its handling has been elevated from just a function of IT.
Because rules and policies play the central part in data governance, a well thought through data governance strategy that specifies appropriate policies is essential. The Data Governance Institute (DGI) defines data governance as “a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.”
DGI’s Rules of Engagement helps guide companies when developing their data governance strategy.
- Mission and vision — Define outcome measures, this forms a baseline for collaboration, standardization, and identifying areas of improvement.
- Goals, governance metrics and success measures — Clear measures of success..
- Data rules and definitions — Defined value sets and algorithms.
- Decision rights — Data must be put under the purview of a data steward responsible for collaborating with other data stewards.
- Accountabilities — A data stewards is accountable for their content.
- Controls — Use tools for data entry and data harmony.
- People and organizational bodies — Three general tiers of data users must be classified: those interested in using data, data stakeholders; data governance officers; data stewards.
- Processes: proactive, reactive, and ongoing data governance — Data procedures must be mapped and defined for reference and standardization.