The Core Data Governance Pillars Every Organization Should Get Right

Data governance is the process of overseeing and regulating an organization's data assets, and it is widely recognized as essential, yet many organizations struggle to move from theory to execution. That's partly because the foundational pillars of data governance are either poorly defined or inconsistently applied. When companies gain a clearer picture of what constitutes proper data governance, they can begin to build a sustainable data governance framework that aligns with their business goals and empowers them for future growth.

While the exact number and nature of the data governance pillars that organizations will found their frameworks upon may vary slightly, the core components of each pillar remain largely the same. The main building blocks focus on data quality and integrity, data ownership and accountability, data privacy and security, and metadata management and data transparency, while components such as data management, data architecture, data literacy, and continuous improvement are critical pieces of the puzzle as well.

Data Ownership and Accountability

One of the first steps in developing data governance frameworks is clearly defining which parties have authority over, access to, and responsibility for your data assets. This is called data ownership. The main players on a data ownership team are:

Data owners, who bear the responsibility for specific data sets
Data committees, who oversee the implementation of data governance policies
Data stewards, who create processes and policies that maximize data quality and security, ensuring that an organization's data management protocols align with current data governance best practices

Another component of this data governance pillar is data accountability. Data accountability requires that data assets be generated, used, stored, shared, and deleted according to the rules established within the framework and enforces the appropriate consequences when data owners, stewards, or users fail to do so.

Assigning clear data roles and responsibilities within your organization reduces confusion and ambiguity regarding the appropriate use of your data, and data accountability helps ensure that all data is operated upon as intended. For example, designating specific data assets to certain data owners lets you implement identity and access management (IAM) principles and access controls so that only authorized data owners or stewards can handle certain data artifacts. This minimizes the risk of a data breach or compliance violation, helping keep your data secure.

Data Quality and Integrity

Poor data quality undermines analytics, leads to AI errors or bias, and can cause you to fall short of your business goals. Maintaining the highest possible data quality and integrity is therefore essential for excelling in your broader business operations.

Data integrity refers to the usability of your data assets and pertains to their authenticity, structure, and protection from any corruption. It's a subset of data quality which consists of:

Accuracy, which measures the degree to which the data rightly describes the information it's intended to capture
Completeness, which determines if the data contains all necessary values and is devoid of gaps, null sets, or invalid values
Consistency, which ensures that a specific data value is the same in every application in which it's used, reducing errors or mishandling
Timeliness, which looks at the time gap between the creation and availability of a data set, so that it's current enough to yield accurate insights

While data stewards are often the primary parties responsible for creating the processes and policies needed for high-quality data, maximizing your data quality and integrity is a team-wide endeavor. For example, all stakeholders involved must adhere to the appropriate standards and regulations applicable to your data governance framework. The right tools must also be put in place to automatically monitor, clean, store, share, and delete your data to enhance your operational efficiency, as well as to effectively manage the vast amounts of data at your organization's disposal.

Data Security, Privacy, and Access Control

Data privacy and security form another data governance pillar, one that's particularly critical for maintaining compliance. Failure to secure sensitive data and protect it from unauthorized access is likely to result in a compliance violation, damaging profitability and tarnishing company trust.

Maintaining proper data security and privacy requires the implementation of several tools and processes to keep your data from falling into the wrong hands.

Encryption for data at rest and in transit
Access controls such as multifactor authentication (MFA) to prevent unauthorized access
Data anonymization, to prevent any party associated with a given data set from being identified
Data lifecycle management, ensuring the data is properly stored and maintained during its use and deleted once it's no longer needed

Many organizations have their data scattered across both cloud and hybrid infrastructures, so applying their data protection processes consistently across all silos and environments can be a challenge. Consulting frameworks or data security practices of industry leaders can help you navigate the complexities of end-to-end data protection, so refer to resources such as Dell's privacy policy to see what tools and protocols work best when keeping your data secure.

Metadata Management and Data Transparency

Sometimes referred to as "the data about your data," metadata plays a crucial role in maintaining data quality. It gives users easier access to their data, improves context surrounding the data so that users can better understand its validity, and increases transparency regarding the sourcing and use of your data, which fosters better compliance.

Some components of proper metadata management include data classification, data tagging, cataloging and documentation, and data warehousing for business intelligence. With these components in play, your data governance processes can better support analytics initiatives, foster a culture of data literacy, and facilitate the innovation that takes your business to the next level.

Enforcing Policies and Building a Scalable Governance Framework

Just as the pillars of a building uphold its structure, the pillars of data governance support your organization's broader data environment so that you can leverage your data to its fullest potential. Each of the data governance pillars work together to help you create a comprehensive data infrastructure, minimizing the possibility of gaps that could result in incorrect data analysis, compliance violations, or greater business risk. Clearly defined and enforced data policies, a data management framework that maximizes data quality, and a culture of continuous improvement can cover the gaps in your data governance processes, enabling your data infrastructure to evolve from a patchwork solution into a cohesive, scalable governance strategy.

The Elevate User Community creates a space where IT and data professionals can share their data governance best practices and learn from each other as they develop a framework that aligns with their unique applications and business goals. To become a part of that space and glean insights from today's leaders in data governance, join the Elevate User Community today.