Exploring Data Governance

Data governance (DG) is the process of managing the availability, usage, integrity, and security of data in an enterprise system based on internal data policies and rules. Effective data governance ensures that data is consistent and reliable and is not misused. As organizations face new data privacy regulations and increasingly rely on data analytics to help optimize operations and drive business decisions, this becomes increasingly important.

A well-designed data governance plan typically includes a governance team, a steering committee as a management organization, and a group of data stewards. They jointly develop standards and policies for data management, as well as implement and execute procedures performed primarily by data administrators. In addition to the IT and data management teams, executives and other representatives who organize business operations are also involved.

Gartner analyst Andrew White wrote in a blog post that although data governance is a core component of an overall data management strategy, organizations should focus on the expected business outcomes of the governance plan, not the data itself. Data governance explains in more detail what it is, how it works, the business benefits it provides, and the challenges of data governance.

Why Bother?  

Data is becoming the main corporate asset that determines the success of a company. Digital transformation is everywhere. If you can control your data, you can use your data assets to successfully carry out digital transformation. This means implementing a data governance framework that suits your organization, i.e., your future goals and business model. The framework should control the data standards required for this journey and delegate the necessary roles and responsibilities within your organization related to the business ecosystem your company operates.

A well-managed data governance framework will support the transformation of enterprises to digital platform operations at multiple levels within the organization:

Management: For senior managers, this ensures the impact on the company’s data assets, their value, and their changing business

Finance: Ensures consistent and accurate reporting

Sales: Provides reliable viewing of customer preferences and behavior

Purchasing & Supply Chain Management: Strengthens cost reduction and operational efficiency planning based on data mining and business ecosystem collaboration

Production: It’s essential for implementing automation

Legality and Compliance: It will be the only way to satisfy growing regulatory requirements 

Benefits of Data Governance

An effective data governance strategy can bring many benefits to an organization, including:

  • A common understanding of data: Data governance provides consistent data views and common terminology, while maintaining sufficient flexibility across business units.
  • Enhanced data quality: Data governance creates a plan to ensure data accuracy, completeness, and consistency.
  • Data Map-Data governance provides an advanced capability to understand the location of all data related to key entities, which is necessary for data integration. Just as GPS can present physical landscapes and help people find their way in unfamiliar landscapes, data governance makes data assets available and easier to connect to business outcomes.
  • A 360-degree view of each customer and other business entities: Data governance establishes a framework for organizations to agree on a “single version of truth” for key business entities and establish the appropriate level of consistency between entities and business activities.
  • Ongoing compliance – Data governance provides a platform to comply with the requirements of government regulations, such as the EU General Data Protection Regulation (GDPR), HIPAA (Health Insurance Portability and Accountability Act) and industry requirements like PCI DSS (payment cards) and industry data security standards.
  • Enhanced Data Management: Data governance brings the human dimension to a highly automated and data-driven world. It has established codes of conduct and best practices for data management to ensure that problems and needs outside of traditional data and technical fields (including legal, security and compliance fields) are addressed consistently.

Data Governance Is Not the Following

Data governance is often confused with other closely related terms and concepts, including data management and master data management.

  • Data governance is not data management. Data management refers to managing all the requirements of an organization’s data life cycle. Data governance is the core component of data management, bringing together nine other disciplines, such as data quality, master data and reference data management, data security, database operations, metadata management, and data storage.
  • Data governance is not master data management. Master data management (MDM) focuses on identifying the key entities of an organization and then improving the quality of this data. It ensures that you get the most complete and accurate information about key entities such as customers, suppliers, and medical service providers. Since these entities are shared across the organization, master data management is the integration of fragmented views of these entities into a single view, a discipline that goes beyond data governance. However, there is no successful MDM without proper governance. For example, the data governance plan will define the master data model (what is the definition of customers, products, etc.), specify the data retention policies, and define the roles and responsibilities of creating, managing, and accessing data.
  • Data Governance is not data stewardship. Data governance ensures that the right data responsibilities are assigned to the right people. Data management refers to the activities necessary to ensure that data is accurate, controllable, and easy for related parties to discover and process. Data governance is primarily about strategy, roles, organization, and policies, while data management is about execution and operations.

The data manager is accountable for data assets, ensuring that actual data is consistent with the data governance plan, is associated with other data assets, and is controlled for data quality, compliance, or security.

Final Word

The world is full of data, nuances, and features as diverse as languages and dialects. Data is everywhere, embedded in the “structure of our daily life” in an imperceptible and conspicuous way. Technology is changing the way data is generated, collected, maintained, and used. In the era of big data, people tend to attach importance to the speed, quantity, and type of data captured, stored, and generated. Unfortunately, deriving meaningful value from data is much more complicated than speed, capacity, or the data itself.