Data governance entails the strategies and rules created to maintain corporate data and its security. Effective data governance preserves the usability and availability of data but also ensures that it cannot be misused, tampered with, or stolen. Most organizations implement data governance infrastructure and controls based on compliance regulations. Good governance reduces the risk of a compromise and ensures that the organization avoids data privacy violations and hefty fines.

Why Does Data Governance Matter?

The main purpose of governance is data integrity and compliance. Data integrity is necessary for consistency across an organization, business productivity, and revenue. For example, suppose that data integrity fails and sales, customer service and shipping have two different addresses for the same customer. If a package were delivered to the wrong address after a sale, customer service would not see the correct address when the customer calls to complain.

Without data governance to oversee data integrity, an organization could have inconsistent data across databases, platforms, and departments. This issue could interfere with sales, revenue, customer satisfaction and daily operations.

Another reason for data governance is compliance. Depending on the organization, compliance regulations require data integrity infrastructure to ensure customer data is properly monitored and maintained. Two major compliance regulations overseeing consumer data integrity include the European Union’s GDPR and the California Consumer Privacy Act (CCPA). Violations can cost millions if an organization is found to be non-compliant, so having good data governance strategies benefits an organization’s business continuity.

Data Governance Goals

Data governance isn’t just about compliance; it also helps organizations better manage their data. Every organization has its own requirements and standards, so a data governance plan can be tailored to support an organization’s unique needs. When designing a strategy, data governance goals should include:

  • Enable better decision-making for data storage, authorized access, and management.
  • Reduce integrity issues by ensuring data is consistent across all storage locations.
  • Protect the interests of data stakeholders.
  • Educate employees, vendors, and other data users on the proper data integrity and management approaches.
  • Establish data management standards so that strategies can be successfully repeated.
  • Coordinated efforts to reduce costs and effectiveness.
  • Create transparent processes.

Data Governance Benefits

The cost of designing and implementing data governance has its benefits. These benefits can improve data processes and protect private data from misuse. A few examples of data governance benefits include:

  • Fewer inconsistencies across reports and applications reliant on data.
  • Fewer data entry errors and changes to data.
  • Consistency between performance metrics used to determine future performance strategies.
  • Better monitoring and oversight for sensitive staff and consumer data.
  • Accuracy in data storage performance and availability.
  • Increase in your data’s value.

Data Governance Use Cases

In an age where one organization could store millions of consumer records, data governance helps with the privacy and integrity of these records. Data governance benefits consumers and the organization and ensures that data procedures are compliant. Every organization should have a data governance strategy, but certain industries benefit more due to the type of data stored.

  • Medical: HIPAA highly regulates patient information. Prescriptions, images, contact information and sensitive services must be protected from misuse and unauthorized access.
  • Risk management: Big data in risk management analysis must be protected and properly managed to ensure the accuracy of results so that consultants can make effective decisions.
  • Banking: Financial data cannot contain errors, or it could affect consumer livelihood and ruin banks. Data governance ensures that transactions and balances are correct across all platforms and consumer information is protected.
  • Agriculture: Many agriculture organizations have legacy systems that fall under the umbrella of data protection and governance. A governance plan protects current and legacy systems that store data.

Who is Responsible for Data Governance?

Because data governance is a significant factor in compliance and integrity, larger organizations have a Chief Data Officer (CDO) to manage all strategies and procedures. The CDO hires a data governance manager to oversee a team of people who plan procedures, develop automation, and determine policies.

Other parties might be involved with data governance. For example, a committee might decide on standards and policies, and committee staff members must vote on any changes to these procedures. Staff members carry out the committee’s regulations and are responsible for ensuring that standards are followed.

What is a Data Governance Framework?

A good strategy includes a data governance framework. The data governance framework includes all the processes, policies and people involved in data management and maintaining its integrity. A data governance framework covers:

  • Consistency across all data views while allowing organizations to update and add data.
  • A plan that highlights all the policies and keeps procedures consistent.
  • A “single point of truth” that covers every question and helps staff determine the right way to handle particular challenges.
  • Methodologies and practices for end-users to follow to establish quality.
  • Accessibility to data for users required to access it.

How to Implement Data Governance

Planning and implementing a data governance strategy usually happens in phases. The way you implement data governance depends on your internal infrastructure, industry, internal procedures, technology, and location of data.

  • Phase 1: You know that you need data governance and become aware of regulations and policies. If you don’t have someone on staff who understands data governance, you might need help from outside consultants.
  • Phase 2: With the help of consultants or internal staff, audit data for its location, usability, availability and access permissions.
  • Phase 3: Identify data ownership and determine roles and responsibilities for governance.
  • Phase 4: Develop data definitions and determine if data is stored and maintained in the best location.
  • Phase 5: Educate users and stakeholders on new standards, policies and the importance of data governance.
  • Phase 6: Monitor data and review metrics to determine if standards should be modified and improved.

Best Practices for Data Governance

Several other organizations already perform data governance. You can learn from others’ experiences to improve your own procedures and policy rollouts. A few best practices you can follow to help reduce downtime and frustration:

  • Start small and design achievable goals to continuously improve.
  • Designate ownership of procedures so that everyone can be a part of the process to success.
  • Assign roles and responsibilities to each data owner and manager.
  • Educate users so that they understand the importance of data governance.
  • Map tools and infrastructure with data to get a clear picture of where it’s used.
  • Focus on the most critical data first to ensure changes significantly impact governance maturity.
  • Develop control procedures and policies that provide the availability to those that need it.
  • Use metrics to identify weaknesses and improvement opportunities.
  • All people involved in data governance should communicate frequently.

Common Data Governance Challenges

As with any new initiative, implementing a data governance strategy has its challenges. Proper solutions can overcome some challenges, but others require outside help from consultants. Before you start your data governance journey, consider these common challenges:

  • Limited resources: Small-to-midsize organizations struggle with finding on-site staff with the knowledge and skills to implement a data governance plan. Current administrators are already overworked and don’t have the time to take on another responsibility. Automation still requires the right people to design and deploy it, so many organizations need outside help to get started.
  • Data silos: For small businesses, changes in technology, the addition of technology, communication barriers and constant staff turnover lead to scattered data within a specific location.
  • No leadership: Even staff familiar with data governance needs direction and leadership to deploy it. A good leader will educate users and see a data governance strategy from start to finish.
  • Defined business requirements: To define data policies, the first step is to understand business requirements. This requires creating use cases and understanding how data is used throughout the organization.
  • Data quality: Poor quality data makes it difficult to improve integrity and determine ownership. It may be necessary to organize and improve the data before a data governance plan can be created.
  • Lack of control: For small organizations, data might be mismanaged and scattered throughout the organization. This leads to a lack of control of all data, potentially resulting in missed data during an audit.

Data Governance Pillars

Data governance is built on pillars. Every pillar plays a role in a successful strategy. When designing a data governance strategy, note that these pillars are crucial to its success:

  • People: The people who take ownership of data make governance successful. A successful strategy requires that everyone on-board understands the importance of governance and what they can do to ensure the protection and integrity of corporate data.
  • Processes: Take every action necessary to ensure data governance and integrity is effective and thoroughly tested. People carry out processes, so processes are built on people.
  • Contributors: Experts who know how to implement a data governance plan contribute to a strategy’s success. They ensure that processes are effective and pass down procedures for people to use.
  • Technology: Organizations don’t always need the latest and greatest technology, but they need effective infrastructure to monitor and implement policies. People and contributors must know how to work with technology for it to be effective.

How Vendors and Tools Can Help

Compliance is difficult to follow, and an organization that doesn’t have on-site staff yet must follow multiple compliance regulations usually needs help. Outside consultants with expertise in specific compliance regulations can start an organization on a journey towards effective data governance planning and practices.

The right technology is also necessary for good data governance, and outside consultants can help you choose the right platforms, tools, and automation procedures. Platforms help with organization, monitoring, and help administrators with making decisions.

How Proofpoint Can Help

Proofpoint has the consultant experts and the tools to help organizations manage their data better, from archiving solutions and policies to discovery. We also have the tools necessary to help administrators properly oversee and manage data discovery and governance. Because data governance requires everyone in the organization to understand its importance and policies, Proofpoint helps organizations from beginning to end by educating users so that they remain productive while following compliance standards to protect data.