Data is unarguably the most important organizational asset. With that said, it is vital to ensure that Data is managed and maintained through its entire life-cycle, from creation to archival or deletion. Here is where Data Governance comes into the picture. Data governance ensures the delivery of trusted information through proactive management of data quality, policies, and business processes. The term “governance” sounds daunting, but with the right mindset and approach it can be successfully implemented in an organization. This involves Organizational and Functional Strategies that encompass the cultural and technological aspects involved in data governance. Outlined below are two main components of a successful approach to implement and deploy Data Governance in an organization:
Gaining alignment at the highest level of organizational leadership is paramount to successful implementation of data governance policies and processes. Hence, the first step in the approach to enabling data governance is the creation of a Data Governance Council. The Governance Council consists of key representatives of the organizational leadership from all aspect of the business, typically at the Executive level or C-level Management. The Governance Council will set and communicate business priorities and provide high-level strategic direction.
The next step is to create a Data Governance Steering Committee that actively manages the Data governance initiatives and strategies set by the Data Governance Council. The Steering Committee consists of IT and business managers and is tightly integrated if not a part of the Project Management Office (PMO) to ensure that all existing and future applications and processes align and follow the governance guidelines set for the organization.
After the creation of the Data Governance Council and Steering Committee, we now need to identify Data Stewards to serve as representatives of key subject matter areas. These Data Stewards will be responsible and accountable for the quality of data for different subject areas like Customers, Products, and other transactional data elements. The Data Stewards would be responsible for defining policies and business rules that establish the framework for implementation of data quality across all applications in the organization for the entity they own.
Additionally, we need to address three key areas - Data Quality, Master Data Management, and Metadata Management - during the execution of the functional strategy.
Data Quality is core to the success of Data Governance and not only enables good business processes, but also is a key enabler to data-driven decision making. The Data Stewards will define the data quality policies and rules that the different source system applications will implement. When identifying the rules, one should consider all organizational entities, including transactions, that affect the business. It is important to consider the business-related and technical aspects of the entity being considered. Defining business rules this way leads to successful implementation of data governance policies. Every application and/or system in the organization needs to implement these rules to best support data quality. The next step would be to create a road map with appropriate prioritization by working with the Governance council and Steering committee.
Master Data Management is essential for the key data elements used throughout the organization, especially for Customers and Products. It is crucial to have one customer or product identification across the organization. Being the subject matter experts, the ultimate responsibility and accountability of creating and managing master data of the organization lies with the Data Stewards. The Data Stewards will not only identify and manage the master data but also define the security and compliance requirements around each. Once the Data Stewards defines the Master Data, they will establish an approval workflow. The workflow will include the steps for creating Master Data, verifying the Business and IT related data quality rules, and routing them for approval by the Lead Data Stewards before the Master Data is cascaded down to be used by the rest of the organization. Similar to Data Quality, Master Data should tightly integrate with the rest of the organization to avoid inconsistent and duplicate master data.
In my experience, Metadata Management is the most ignored aspect of data management. When implementing a Metadata Management strategy, it is important to define good business and technical metadata.
Business metadata provides a common language to which people across the organization can relate to, which is also a huge proponent in data analytics. Business metadata for each term used in the organization is defined in collaboration with the data stewards, application subject matter experts, and organization leadership. Once the business metadata is defined, it is important to gain approval and alignment across the leadership community for the different business functions.
Technical metadata is very application-centric and helps organizations understand data lineage across different applications. This is a primary enabler for understanding the impact of change. One of the first steps to implementing technical metadata in an organization is to create a repository of applications, tools, and technologies in the organization. The next step is to identify the application priority of the implementation. This is typically done with the help of the Data Governance and Steering committee. Once the priority is set, we will have to evaluate the feasibility of implementing an integrated Technical Metadata solution within the applications or build a Metadata manager for the organization.
Once the functional aspects of metadata, including the process for entering and maintaining the Business metadata are finalized, the next step would be to leverage off-the-shelf technical solutions available to implement Metadata Management across the organization.
Data Governance drives better information management at every level in an organization leading to a strong enterprise data foundation. While Data governance is a Business Driven initiative, a successful implementation and deployment cannot happen without Business and IT alignment and collaboration.