Data Governance Pillars: Empowering Your Data-Driven Journey

Updated February 27th, 2024
Data governance pillars

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Data governance pillars are the foundational principles that guide the implementation of an effective data governance framework. They encompass various aspects such as data quality, data privacy, data security, and data compliance. Together, they help ensure the effective and responsible management of data within an organization.

In this article, we will explore:

  1. What are the pillars of data governance?
  2. The various popular data governance frameworks that use the data governance pillars
  3. Interrelationship between these pillars

Ready? Let’s dive in!

Table of contents

  1. What are the pillars of data governance?
  2. Popular data governance frameworks that use data governance pillars
  3. The interrelationship between data governance pillars
  4. Data governance pillars: A case study perspective
  5. Rounding it all up
  6. Related reads

What are the pillars of data governance?

Data governance is a fundamental framework and set of practices that organizations implement to ensure the effective management, quality, security, and compliance of their data assets. At its core, data governance rests on several key pillars, each playing a crucial role in the successful management of data within an organization.

These pillars include data stewardship, data quality, data security, data privacy, and data management. Together, they form the foundation upon which data governance strategies are built, helping organizations maximize the value of their data while minimizing risks and ensuring regulatory compliance.

In this section, we will identify the 8 key components or “pillars” that form the basis of any robust data governance program. These pillars are:

  1. Data quality
  2. Data security and privacy
  3. Data architecture and integration
  4. Metadata management
  5. Data lifecycle management
  6. Regulatory compliance
  7. Data stewardship
  8. Data literacy

Now, let us look into each of the above pillars of data governance in brief:

1. Data quality


Data quality involves maintaining the accuracy, completeness, consistency, and reliability of data. Techniques and tools for data cleaning, validation, and monitoring are essential to maintaining data quality.

In practice, this might involve implementing rigorous processes around data entry and collection to minimize errors, using automated tools to clean and validate data, and regularly reviewing and auditing data to identify and correct any inaccuracies.

For example, a company might implement real-time data validation checks to flag and correct erroneous data as it’s being entered into a system.

2. Data security and privacy


Protecting data from unauthorized access, ensuring compliance with privacy laws and regulations, and managing user access and authentication form the foundation of this pillar. Security measures must be in place to protect sensitive and critical data.

This encompasses all measures taken to prevent unauthorized access to data and to protect the privacy of personal data.

For instance, an organization might use encryption to protect data in transit and at rest, implement robust access controls to restrict who can access certain data, and regularly conduct security audits and risk assessments. In terms of privacy, an e-commerce company might implement processes to anonymize customer data and ensure that all data collection practices are in line with privacy laws like GDPR.

3. Data architecture and integration


This pillar focuses on how data is structured, stored, and integrated across the organization. It involves making sure that the organization’s data is properly classified, inventoried, and stored in a way that facilitates easy access and use.

The data architecture and integration pillar is about how data is structured, stored, and linked across different systems and databases.

For instance, a company might use a data warehouse to consolidate data from different sources. They may implement a standardized naming and classification scheme for data, and use APIs or ETL processes to facilitate data integration.

An example could be a multinational company that creates a unified data architecture to allow seamless data flow between its different regional branches.

4. Metadata management


Metadata or data about data (like data source, creation date, last update, owner, etc.) should be properly managed and accessible to help users understand the data they’re working with.

Metadata management is about managing data about data. Metadata includes information like the source of the data when it was last updated, who owns it, and how it should be used. It might involve creating a centralized repository where metadata can be stored and accessed, and implementing policies around metadata creation and update.

For example, a research organization might keep detailed metadata about its datasets to help researchers understand the context, quality, and appropriateness of the data for their studies.

5. Data lifecycle management


This involves managing data throughout its entire lifecycle, from creation and collection, through use and archiving, to eventual disposal. It’s important to ensure data remains useful and accessible as long as it’s needed, and that it’s properly disposed of when it’s not.

This refers to managing data from its creation or acquisition through to its deletion. This might involve setting policies around

  • Data retention (how long data should be kept before it’s deleted)
  • Data archiving (how data should be stored over the long term)
  • Data disposal (how to securely delete data).

For instance, a hospital might implement a data lifecycle management policy that specifies how patient data should be handled after the patient’s death, including how long it should be kept, when it should be archived, and how it should be securely deleted.

6. Regulatory compliance


Given the number of regulations related to data (like GDPR, CCPA, etc.), it’s critical for organizations to ensure that they’re in compliance. This involves processes and policies to manage data according to legal and contractual requirements.

This involves ensuring that data management practices comply with all relevant laws and regulations. This might involve conducting regular audits, keeping abreast of changes in data-related laws, and implementing processes to ensure compliance.

For example, a financial institution might have a dedicated team responsible for ensuring that all data practices are in compliance with regulations like GDPR and the Sarbanes-Oxley Act.

7. Data stewardship


Data stewards play a crucial role in governance efforts by acting as liaisons between different departments and ensuring that the policies and procedures are implemented correctly. They help define the data, ensure its quality, and promote its use across the organization.

Data stewards are responsible for ensuring the proper management and usage of data within an organization. They might be involved in :

  • Defining data elements
  • Establishing data quality standards
  • Resolving data issues
  • Promoting data sharing and usage.

For instance, in a manufacturing company, a data steward might work with both the production and sales teams to ensure that they’re using the same data and definitions when discussing product outputs.

8. Data literacy


Data literacy refers to the ability of organization members to understand, use, and communicate data effectively. It involves training and education to ensure that all users understand the importance of data governance and their role in it.

This refers to the skills and understanding of data that members of an organization possess. A data literate individual is able to understand, interpret, and question data. Improving data literacy might involve providing training and education to staff, or creating resources and tools to help staff better understand and use data.

For example, a city government might offer workshops on data literacy to its staff to ensure that they can effectively use data in decision.

Each of the above pillars supports a comprehensive data governance program. The emphasis on each might vary depending on the specific needs and context of the organization, but all play a crucial role in ensuring effective data governance.

While the phrase “data governance pillars” is not universally standardized, it is commonly used in the field of data governance to describe foundational elements or principles necessary for a successful data governance program.

It is not so much attributed to a single popular framework, but rather, it’s a way of organizing the multifaceted components of data governance. Different organizations and authors may define slightly different pillars, but most would agree on several key areas of focus.

For example, the Data Governance Institute (DGI) uses the term “components” instead of “pillars”, and describes 8 major areas:

  1. Data governance organization: An organizational body or entity assigned to manage the company’s data assets.
  2. Data stewardship: The people and processes that ensure the effective use, maintenance, and protection of data assets.
  3. Data quality: Measures and processes to ensure the accuracy, completeness, timeliness, and consistency of data.
  4. Data privacy & compliance: Ensuring that all data is used and managed in compliance with laws, regulations, and industry standards.
  5. Data architecture: The structure of data as stored in databases and systems.
  6. Metadata management: The collection, management, and usage of data about data.
  7. Document & content management: Management of unstructured data and information resources.
  8. Master & reference data: Defining, using, and managing the organization’s shared data to reduce redundancy and ensure better data quality.

Another popular framework is DAMA-DMBOK (Data Management Body of Knowledge) by DAMA International. The DMBOK identifies 11 major areas of focus, including data governance and data stewardship as specific disciplines within data management. Other areas include data quality, metadata, data privacy & security, and data architecture, among others.

In summary, the term “pillars” in the context of data governance often refers to key components, principles, or areas of focus that together provide a comprehensive approach to managing and maximizing the value of an organization’s data assets. While different models might use different terminologies, the overarching principles remain quite consistent.

The interrelationship between data governance pillars

While each of the data governance may seem independent of each other, in reality they’re closely interrelated. Here’s a simple example in text format:

                       ------------ Data Governance ------------
                      |                                        |
                      |                                        |
                      V                                        V
             Data Quality <----> Data Strategy   <----> Data Privacy & Compliance
                      |                                        |
                      |                                        |
                      V                                        V
             Data Architecture <----> Metadata Management <----> Data Operations & Technology
                      |                                        |
                      |                                        |
                      V                                        V
             Data Literacy <----> Data Culture & Change Management

The above diagram is a simplistic representation, and the lines between each pillar suggest interaction and dependency. For example, to ensure data quality, your data architecture must be robust and well-organized. Metadata management is integral to understanding your data architecture and also plays a role in ensuring data quality.

All the above components are guided by the overarching data strategy and need to adhere to data privacy and compliance norms. Data literacy and strong modern data culture, underpinned by effective change management, are crucial for maintaining and improving all these areas.

In a real-world scenario, the interrelationships would be much more complex, involving feedback loops and additional dependencies. A visual mapping tool or software would provide a much clearer and more detailed representation.

Data governance pillars: A case study perspective

Now, let us understand about data governance pillars based on a deployment we did for a customer - Contentsquare. It is a leading digital experience platform, sought to launch a data governance program after years of significant growth.

Choosing Atlan, Contentsquare launched their program with a single source of context, transparency, and interaction across a diverse range of users

Having successfully launched data governance, Contentsquare now benefits from a fully mapped data estate, supports crucial KPIs with single owners and shared definitions, has streamlined collaboration, and has propagated knowledge and standards of data quality across its assets

Here’s how their data governance pillars looked:

  1. Data quality: Contentsquare is a platform that helps businesses analyze user behavior on websites and applications. The quality of data it captures is vital for its ability to provide reliable insights. This pillar may involve ensuring the accuracy of the data collected, validating it, and ensuring it’s free from duplicates or errors.
  2. Data strategy: As a data-centric company, Contentsquare needed to have a clear strategy on how to use data for business growth. This included defining their key performance metrics, deciding how data will be used to drive product development, or determining how to leverage their data for competitive advantage.
  3. Data privacy and compliance: Given that Contentsquare collects user behavior data, they have to deal with various privacy regulations like GDPR, CCPA, etc. This pillar would involve setting up policies and procedures to ensure compliance with these regulations.
  4. Data architecture: This refers to how Contentsquare’s data is organized, stored, and accessed. They would have to consider things like where the data will be stored (on-premise or cloud), the format of the data (structured or unstructured), and how to ensure it can be easily accessed by those who need it.
  5. Metadata management: For Contentsquare, metadata included information about where and when the user behavior data was collected, what actions were taken, etc. Managing this metadata is important to provide context to the data and enable better analysis.
  6. Data operations and technology: This involves the technical aspects of handling data, such as ETL (Extract, Transform, Load) processes, data pipeline management, data warehousing, and so on. Contentsquare would need to ensure these operations run smoothly to deliver their services effectively.
  7. Data literacy: As a data-centric organization, it’s crucial for Contentsquare employees to be data literate. This includes not only the data team but also marketers, product managers, and executives, all of whom need to be able to understand and use data effectively in their roles.
  8. Data culture & change management: This refers to the creation of a culture where data is highly valued and used for decision-making across the organization. For Contentsquare, this could involve fostering a culture of data-driven decision-making and managing change as the company evolves its data practices.

Again, these are speculative examples based on the nature of Contentsquare’s business. The specifics of how they handle each pillar may vary.

Rounding it all up

While the phrase “data governance pillars” is not universally standardized, it is commonly used in the field of data governance to describe foundational elements or principles necessary for a successful data governance program.

The specific definitions and importance of each pillar can vary depending on the organization, its industry, and its specific needs and goals. The examples we discussed were in the context of a data-centric company like Contentsquare, but the principles can be applied to any organization aiming to establish a robust data governance program.

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