Data governance defines how data should be gathered and used within an organization. It address core questions, such as:

  • How does the business define data?
  • Where does it live?
  • How accurate must the data be for use?
  • Who can use it?
  • How can they use it?

Such questions inform the governance rules and principles, which seek to maintain the highest quality of data possible throughout its life cycle.

Gartner defines information governance as, “the specification of decision rights and an accountability framework to ensure appropriate behavior in the valuation, creation, storage, use, archiving and deletion of information. It includes the processes, roles and policies, standards and metrics that ensure the effective and efficient use of information in enabling an organization to achieve its goals.”

Organizations have much to gain from learning about and implementing a data governance framework. Though data governance has different names and may be described in many ways, this is what we’ll be focusing on in this article:

Why is Data Governance Important?

As data collection and storage grow, so too does the need for data governance. Businesses must learn the most efficient methods to gather, organize, and analyze data, and data governance creates a foundation to improve data quality and accuracy. Where data governance once focused primarily on compliance, the age of big data has broadened its applications.

Data governance ensures that data users follow the same guidelines to achieve higher quality and accuracy; it also provides a flexible framework that responds to new laws and regulations around data usage. Chances are, if your organization is heavily focused on data, then regulations like GDPR, CCPA, and HIPAA have crossed your radar. Without a solution that classifies data assets and communicates policies to front-line users, organizations are exposed to higher data risks.

Data governance establishes similar data-use processes across an organization, as dissimilar processes lead to inconsistent data quality and accuracy. If implemented properly, a data governance framework not only solves this problem, but also helps data users understand the guidelines.

Data Governance Roles

Data governance enforces data quality and fosters a healthy data culture, but that isn’t enough. A governance platform must be built with all data users in mind, from leaders to end-users, to ensure organizational buy-in and trust. This inclusivity is vital for creating a data culture. Some of the data roles that benefit from a successful data governance framework include:

Data governance, done properly, benefits everyone responsible for data at different points of its life cycle. For example, data stewards may define what constitutes “quality data.” This definition, in turn, informs the actions and responsibilities of the data custodian. Governance solutions can either make the custodian’s life more efficient or derail their process completely. It all comes down to the philosophy behind the data governance framework.

3 Major Forms of Data Governance

The philosophy and implementation of data governance depends on the form. There are three approaches to consider for your organization. The philosophies behind these forms are as follows:

Command & Control Traditional Active
People must be assigned to data stewardship roles Path is set and users are expected to take it Non-invasive
Forced governance of data Passive governance Embeds governance in users’ day-to-day activities
Very invasive One-size fits all approach Assumes people are already governing data informally
Demanding tone Innovation through governance is not a priority Distributes decision rights
Early purchase of tools Formal decisions are not fully understood Believes people can be more efficient and effective if they are recognized and rewarded
    Establishes common trust model
    Achieves business outcomes while managing risks.

A successful data governance implementation will take cues from your organization’s culture. Bob Seiner, an expert in data governance, explains that “the best approach is the approach that will activate your program, engage the data stewards, and lead to the demonstration of business value.” The benefits all boil down to the data governance approach an organization takes.

Some of the direct business benefits include:

  • Improved data quality – As a business implements data governance, data quality will improve. This leads to more accurate business processes and higher customer trust.
  • Better analysis – Because data governance enhances data quality, analysts have an easier time finding and analyzing the data they need.
  • Easily defined goals – Data governance sets out a clear method to achieve business goals.
  • Consistent compliance – Data governance helps data users stay compliant with regulations, reducing the risk of fees and reputational damage.
  • Improved data management – Improved data management minimizes duplicative efforts, which boosts operational efficiency.
  • Standardized systems and data policies – Standardizing systems and policies across the organization instills users with ethics and awareness.

Democratizing Data

Quality data is only powerful if people can use it. In the past, many companies adopted a “bunker mentality” around data, locking it away for fear of misuse. Nothing could be worse for creating a data-driven culture! In an enterprise, many people make business decisions each day, and those decisions need the support of good data. Democratizing data means making it available to those who need it. A data governance model that emphasizes data democratization, with the support of a data catalog, will unlock value and empower better decision making.

Data catalogs implement data governance in various ways. A catalog that takes an active, non-invasive approach will track user behavior to spot governance patterns at the source. By spotting patterns in user behavior with machine-learning and AI, a data catalog can formalize those patterns into a framework everyone shares and sees. Minds align around shared processes, and trust between people grows.

How Alation Activates Data Governance

The magic behind Alation’s data governance framework is the people-first approach. This approach uses an iterative process of continuous management and improvement. Not only does this improve internal communication, but it also allows the governance strategy to evolve over time into its most adoptable form, while being as minimally invasive as possible.

scaling data governance

When implementing a governance strategy, organizations need a way to track progress. Alation’s Stewardship Dashboard helps stewards communicate as they track, measure, and report impact related to their efforts. This dashboard simplifies data governance through automation and behavioral insights. To get a better idea of how non-invasive data governance works, learn how Alation helped implement a governance solution for Riot Games.

Data governance sets clear rules around data usage. At its best, when implemented intelligently, it achieves a determined business outcome. This ranges from improving data quality to better operating efficiency. With a proper framework in place, any organization can start leveraging data governance to enhance storage, operations, security, and integration.

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