What is Data Governance?

Data governance is a collection of strategic practices used to organize and manage data to ensure quality and accessibility throughout the data lifecycle. With data governance processes in place, data is considered across an organization rather than housed in silos, to increase availability and improve collaboration, as well as protect it in accordance with corporate and regulatory compliance rules.

Data governance is not a project. It is an overarching strategy for how organizations work with and consider data on an ongoing basis.

Data Governance can help align stakeholders’ needs and provide checks-and-balances between those who create / collect information, those who consume / analyze it, and all other stakeholder groups.

Data Governance Institute

Why is Data Governance Important?

As organizations consider data governance, tension quickly surfaces between access to data across people, teams, and business functions and the protection of data from threats and misuse. Done correctly, data governance enables security and compliance and allows users to quickly and easily access the data they need to be productive.

Organizations’ accelerated shift from centralized to distributed workplaces puts an unprecedented strain on data management. Weak or nonexistent data governance exacerbates the related risks as an already porous perimeter has become nonexistent in many cases. Data exposure continues to grow, as does the risk, with more remote workers and hybrid workplaces along with more devices, clouds, and applications.

Benefits of Data Governance

Data governance benefits are driven by the improved analytics made possible by better data quality and access:

1. More informed decisions
2. Improved operational efficiency
3. Greater accountability
4. Better data quality
5. Increased value of data
6. Standardization of data
7. Faster and better decision-making
8. Revenue growth and reduced costs
9. Higher customer satisfaction
10. Democratization of data

Data Governance and Compliance

Data is at the heart of most regulatory compliance requirements. Legal, finance, and operations teams depend on data to affirm their adherence to various regulations from HIPAA and GDPR to GLBA and PCI–DSS. Data governance not only assures them of the quality of data, but also ensures that they know where it all resides—both critical to meeting compliance requirements.

Data Governance Goals

The end goal of data governance is to protect organizations’ most important assets while enabling the consistent and reliable flow of data across people, teams, and business functions. The policies and processes set forth ensure that data is secured, trustworthy, documented, managed, accessible based on roles and responsibilities, and audited.

Setting data governance goals ensures that organizations can achieve and measure success by helping to:

1. Drive data-driven decisions
2. Break down data silos
3. Gain visibility into the location of all data
4. Understand how all data is related
5. Maintain compliance with regulatory requirements—internal and external
6. Establish rules to ensure the consistency, quality, and veracity of data
7. Protect data from misuse or exploitation—accidental or malicious
8. Implement effective security systems and processes to safeguard data
9. Eliminate data redundancy
10. Mitigate risk

Roles in Data Governance

Data governance succeeds when proper teams are in place and integrated. A data governance team is responsible for gathering the right people in the right groups, then bringing them together and ensuring cross-functional collaboration. The team also defines processes and terms for the working group and, of course, the data.

A data governance committee includes:

• Executive representatives
• Chief data officer
• Data managers
• Data steward (responsible for ensuring that data policies and standards become practice within their domain)
• Training and education lead

Data specialists who support the execution of data governance include:

• Data scientists
• Data architects
• Data modelers
• Data quality analysts

Data owners and users are from across the company, including:

• Finance
• Marketing
• Sales
• Operations
• Legal
• IT

In addition to producing and using data, IT implements, supports, and maintains the technology required for data governance. This includes helping with workflows, monitoring, security, and deploying new technology.

Data Governance Frameworks

A data governance framework establishes rules and processes for the collection, storage, and use of data. With a data framework, teams are able to:

• Define and document data standards and quality rules
• Establish accountability
• Assign data ownership
• Set roles and responsibilities
• Determine criteria for metrics
  • Key quality indicators (KQIs)
  • Key data elements (KDEs)
  • Key performance indicators (KPIs)
  • Data risk
• Develop policies and processes
• Create a glossary to standardize related definitions across the organization

Questions Addressed by a Data Governance Framework

• What data exists?
• Where is sensitive data exposed?
• Are there mechanisms to identify sensitive data?
• How is access to data granted and managed?
• Are there areas where there is a lack of compliance?
• How are the multitude of folders and files organized and protected?
• Is there data that can potentially be archived or eliminated?

Three Pillars of a Successful Data Governance Program

1. Data transparency to provide a clear understanding of where data resides, what data is available, and how data is used.
2. Accountability for data that includes policies which reinforce how data should be handled and repercussions for its misuse. Data governance policies related to accountability can include the assignment of roles and responsibilities across the data lifecycle—i.e., creation, use, storage, movement, changes, destruction. They also include restricting access to sensitive data based on users’ needs.
3. Commitment to data governance by all stakeholders is only achieved when they can see value in the program and not be hindered by it. Support for embedding data governance into existing workflows increases engagement and increases the likelihood of the program succeeding and the organization realizing the benefits.

Essential Components of a Data Governance Framework

A data governance framework includes policies, procedures, processes, and people, which are wrapped up in a strategy. Technology, while vital, plays a tactical role in data governance.

To provide a complete and unified view of data across an organization, exhaustive discovery is critical. Discovery goes beyond identifying the location of all data and must include:

• Relationships and lineage
• Metadata
• Profiling
• Certification
• Classification

Process components of a data governance program include:

• Data quality management
• Issue resolution
• Catalog development and maintenance
• Management of reference and master data
• Data privacy
• Enforcement and management of policies
• Ongoing data literacy development
• Provisioning and delivery of data
• Plans for data retention and destruction

Data Governance for Establishing Acceptable Data Terms

Data governance defines rules that articulate what constitutes acceptable data, including constraints, cascades, triggers, or a combination of these to ensure the accuracy of data sets.

Considerations for acceptable data include:

• What data should be collected?
• Where is the data collected from?
• How is the data used?
• Who can use the data?
• What rules should be applied to the data?
• What is the lifecycle of the data?
• Who is involved in the lifecycle of the data?

Using a Data Governance Framework

A well-developed data governance framework provides the ability to:

• Measure and monitor data quality and proliferation
• Track processes
• Achieve regulatory compliance
• Identify and remediate risks associated with sensitive data
• Detect anomalies
• Create an audit trail
• Follow data use policies for internal and external flows

Data Governance Best Practices

While organizations differ, most of the following data governance best practices will facilitate the creation of a successful program. Considering these best practices will also help orient and align team members as they embark on the journey.

1. Build a business case for the data governance program that focuses on productivity, efficiency, cost savings, risk mitigation, and compliance.
2. Establish clear goals and set metrics to measure progress and efficacy—remember to call out the successes.
3. Secure buy-in from all stakeholders by educating them about how the program works and what the benefits are.
4. Define ownership, at all levels, with roles and responsibilities clearly explained and monitored.
5. Ensure that the data governance framework aligns and integrates with the organization’s structure, processes, and goals.
6. Engage IT at the outset to ensure that the proper tools and infrastructure are in place or can be deployed to support the data governance program.
7. Develop and use a glossary to ensure that everyone is speaking the same language.
8. Focus on critical data elements (e.g., sensitive information) first, then expand to incorporate all data into the program—start with small and manageable. Within the first data domain, select that which will yield the highest impact for the effort.
9. Document all policies and processes.
10. Continuously communicate with all stakeholders.

Data Governance Challenges

Aside from the challenges related to dealing with data, organizations face other obstacles and roadblocks when creating and rolling out a data governance program. These include:

• Internal politics
• Limited funding
• Resistance to change
• Lack of IT support and data-related expertise
• Misperceptions about data governance
• Ineffective security practices

On the data side, there are many challenges, but the ones that stand out are:

• Complexity of dataflows
• Data sources outside the control of the organization
• Limited access to data
• Siloed data
• Traceability
• Unstructured data
• Unsanctioned apps

Data Governance Tools

Questions to Consider when Selecting a Tool

Key questions that need to be addressed while selecting a data governance tool include:

1. What are the data governance use cases?
2. How extensive will the program be?
3. How much detail is required in terms of how data governance is adopted and followed?
4. What data search capabilities are needed?
5. Will the tool need to support on-prem, cloud, or hybrid deployment?
6. Is data lineage a priority?
7. How will the tool connect with underlying data systems?

Capabilities Checklist for Selecting a Tool

• Integrated data catalog—expedites data discovery
• Data assessment—evaluates the quality of data throughout its lifecycle
• Data ownership and stewardship—facilitates data access, accuracy, completeness, consistency, and maintenance
• Automated data lineage—provides visibility and error traceability
• Code parsing—supports ETL, SQL, or AI

Cloud Data Governance

A data governance solution needs to provide support to data in cloud environments. Cloud data governance includes the regulation of and reporting on the integrity, usage, availability, and security of data in cloud computing environments.

The primary objectives of cloud data governance include providing the ability to:

• Enhance security
• Ensure privacy
• Regulate and monitor access to sensitive information
• Deliver data analytics
• Maintain compliance with regulatory requirements
• Help safeguard against data breaches and other cyber threats

Steps to Achieve Effective Cloud Data Governance

Considerations for supporting a successful cloud data governance program include:

• Design the program in a way that both cloud data and on-prem data are centrally managed.
• Consider scalability when designing global policies for cloud data governance.
• Include guidelines for providing data consumers with self-service access.
• Ensure that the program includes processes to manage the automation of sensitive information discovery.
• Build processes into the program that streamline the certification of sensitive information workflows.
• Define rules for setting up fine-grained access controls, including purpose-built ones for compliance.
• Establish auditing processes in the cloud data governance program that include monitoring and logging usage.
• Embed transparency by using the program to define processes to ensure timely delivery of robust reports.

Turn People into Data Guardians

The development, adoption, and adherence to data governance plans and protocols increase productivity, minimize risks, and reduce operational expenses with improved data quality, decreased data management costs, and increased access to data for all stakeholders. Organizations with data governance see better decision-making, business outcomes, and protection from threats.

It is critical to remember that data governance is not a one-time project. Behind effective data governance is a cultural shift that requires not just a plan, but the right tools and systems.

Data governance needs to be built into an organizations’ culture and technical backbone. Complement a culture of governance with systems that support the way organizations run and people work. Given a chance, tools, and process to follow, people can and will step up to be the data guardians who will allow data governance programs to deliver their promised benefits.

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