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How To Formulate Your Data Governance Strategy in 5 Steps

By and Posted in Data Integration December 22, 2022

Data governance refers to the policies and procedures governing how data is created, processed, and distributed. It’s used throughout the data lifecycle to ensure organizations have access to trustworthy data and comply with privacy and data safety laws. 

In this article, we’ll share actionable steps to help you and your organization build a data governance strategy that best serves your business needs.

The Five Pillars of a Data Governance Strategy

Applying proper data governance across your organizations requires a well-defined strategy that makes implementing policies and data management easier. The five fundamentals when developing a data governance strategy are:

  1. Identify and know your data: Before formulating a data governance strategy, you need proper knowledge of the data available. Understanding your entire data landscape, and data types, structures, movement, locations, and data transformation processes will help you design the best data governance strategy for your needs.
  2. Create a data governance organization: The data governance organization acts as a central body and coordinates with teams and departments to create consistent policies that align with business goals. How your governance organization is structured depends on your resources and goals. But the key element is an executive sponsor who supports the groups administering governance programs.
  3. Ensure privacy by design through all data processes: Data governance strategies should enforce the “privacy by design” approach. This approach helps protect sensitive data and ensures organizations comply with data privacy regulations. This privacy-first method may involve only collecting necessary data, obfuscating potentially identifiable information, acquiring customer approval before data usage, and using data only for the intended purposes.
  4. Practice proper metadata management to ensure effective communication and ease operational friction: Metadata, or data about your data, is essential to any data governance initiative. When metadata is properly managed, it’s much easier to track data changes, control data access, understand relationships between data, and more. Without metadata, it’s impossible to tell whether your data flow follows the “rules” created by your data governance initiatives. Metadata is also the foundation of a data catalog that provides data consumers with self-service access to approved data. 
  5. Data quality management: Apart from causing bad decisions, poor data quality wastes resources. Proper data quality management ensures data accuracy, completeness, consistency, and validity throughout the data lifecycle. An effective data governance strategy will establish criteria and scoring to ensure data delivered to consumers is high quality and reliable for analytics and AI/ML

Four Steps To Follow To Build an Effective Data Governance Strategy

Here are five actionable steps to build your data governance strategy

  1. Design a data governance approach that revolves around your business: Data governance should result in faster delivery of high-quality data.  For data governance to accelerate instead of hinder your data delivery speed, you need a data governance approach designed with your organization’s unique goals and resource constraints in mind. Three ways you can organize your data governance framework are top-down or bottom-up:
  • The top-down approach: Involves setting the rules and principles according to business goals at the executive level. Although this is a centralized approach and applicable to a data governance vision long-term, this process poses a higher risk of failure as governance policies may need to be more specific for finer-grained operations.
  • The bottom-up approach: In this method, daily data activities and processes set the policies and principles of data governance. This method is more targeted and makes it easier to identify challenges. However, this approach may not unify data governance efforts since departments are more likely to pursue their own goals instead of a unified vision.
  • The hybrid approach: Through a hybrid approach, long-term data governance vision and standards are set at the executive level, but there is flexibility for teams to develop their own data governance in adherence to the higher-level standards and in alignment with the long-term vision.
  1. Identify resources and document all processes and tools for data governance: Data governance applies to all aspects of data processes including, architecture design, cleansing, storage, security, metadata management, integration, and others. The nature of these processes at your organization influences the choice of policies for your data governance strategy. For example, governance policies for the data storage process depend on the type of data, where it’s coming from, and how it’s processed.  
  2. Make and enforce policies: Once you’re familiar with how data moves within your organization, you can design a data governance framework that fits. To enforce the policies, you’ll need efficient communication and collaboration among the data organization members to help engage and educate users on the new approaches.
  3. Monitor and audit existing policies for governance-related data processes: Implementing a data governance strategy doesn’t end with making policies. Those monitoring data governance efforts must track previous policies and measure performance. Plus, business goals and needs change. For instance, data protection rules for a new project may be tighter than those previously set. Hence, policies must undergo constant reevaluation.

Data Governance Strategy Example: City of Philadelphia

The Philadelphia Department of Behavioral Health and Intellectual Disability Services (DBHIDS) faced challenges in its data governance strategy, which included:  

  1. Data wasn’t available when needed, causing data and business teams delays.
  2. Data quality was poor or inconsistent, impacting data trust and reliability.
  3. There was no defined process for dealing with data, so every new data request required excess time for cleaning and standardizing data.

At the root of the problem was an assumption that data analysts and researchers were the best qualified for implementing processes and rules to manage the data flow within the organization. This thinking implied a bottom-up approach, but the researchers still needed to create standard data governance policies. 

Additionally, individual departments pushed the responsibility for data quality around, further exacerbating the lack of governance policies.

Proposed Solution: Implementing a Data Governance Framework by Leveraging Organization Information Architecture

After rigorous interviews with members of its data and business management teams, the team made targeted updates to its data governance strategy:

  1. Create a centralized information architecture to help manage data across the organization. The information architecture documented everything about the organization’s data flow.
  2. Introduce data reusability to improve data access and sharing between groups and improve productivity. 
  3. Manage data as an organizational resource to enhance easy accessibility.
  4. Clearly define data governance roles and responsibilities with an enterprise-level focus and with active participation by both business management and technology professionals. This approach is a mix of the bottom-down and top-level strategy, a more robust approach to data governance.

StreamSets and Your Data Governance Strategy

Adequate knowledge of data that exists in an organization and relationships between these data through good metadata management helps inform data governance strategies. 

StreamSets helps inform your organization’s data governance strategies by creating intelligent data pipelines that leverage the power of metadata to help organizations monitor and maximize their data. In addition, the open metadata system adopted by StreamSets feeds into data governance tools, giving organizations visibility and knowledge of their data and processes as data moves between locations like data lakes and warehouses.

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