Data stewardship and data lineage are two essential aspects of data governance, especially in the context of master data management (MDM). In this article, you will learn what these terms mean, why they are important, and how they relate to some common data governance frameworks and standards for MDM.
Data stewardship is the role and responsibility of ensuring the quality, availability, security, and usability of data within an organization. Data stewards are the custodians of data assets, who define and enforce data policies, standards, and rules. They also collaborate with data owners, data users, and data managers to resolve data issues, improve data processes, and support data initiatives. Data stewardship is a key function of data governance, as it helps to establish and maintain trust, accountability, and value of data.
Data stewards do not usually define data policies. Since this role is usually federated across an organization and embedded within business functions responsible for curating, collecting and maintaining the data they are the custodians for. Policy definition is usually left up to enterprise risk and compliance functions such as information management risk partners or data steward partners.
Business data stewards also helps standardizing definitions across different business areas or departments. Technical data stewards helps mapping logical data assets to physical implementation as part of lineage mapping.
Data lineage is the documentation and visualization of the data flow and transformation across different systems, applications, and processes. Data lineage helps to track the origin, movement, and changes of data over time, as well as the dependencies and impacts of data on other data elements. Data lineage is a vital component of data governance, as it enables data quality assessment, data analysis, data compliance, and data audit.
Data lineage is vital component of data governance but it is very manual and error prone often. For successful implementation, it must be capture programmatically .
MDM is the process and technology of creating and maintaining a single, consistent, and accurate view of master data across an organization. Master data are the core business entities, such as customers, products, suppliers, or locations, that are shared and used by multiple systems and processes. MDM helps to eliminate data silos, reduce data errors, and enhance data integration and interoperability. MDM is a subset of data governance, which is the overarching framework and strategy of managing data as a strategic asset.
Master Data Domain like Customer or Products is one of the key and business critical data domains and data assets that should be covered and governed by the existing data governance practice. Similar to any other data domains, master data needs to have business and technical accountability/ownership defined, data quality rules applied , business glossary define, data stewardship process/workflows established, integration and data sharing mechanism defined…etc Having Data Governance applied for Master data will enable and sustain the success and effectiveness of the Master data management program.
Data governance frameworks and standards are the best practices and guidelines that help to define and implement data governance within an organization. They provide a common language, structure, and methodology for data governance activities, roles, and responsibilities. For example, the Data Management Body of Knowledge (DAMA-DMBOK) is a comprehensive reference model that covers 11 knowledge areas of data management, including data governance and MDM. The Data Management Capability Assessment Model (DCAM) is a framework that measures the effectiveness and efficiency of data management across eight core capabilities, including data governance and MDM. Additionally, the International Organization for Standardization (ISO) 8000 is a series of standards that specify the requirements and methods for data quality and MDM, such as data quality management, data provenance, data exchange, and master data registration.
In my experience, establishing this framework for your enterprise is THE foundational step of a successful Data Governance Program. It's a step that is often pre-empted by the choice of Data Governance / Data Quality tools and technology. Starting here and implementing a few key low-tech proof of concepts based on your chosen framework can drive long-term success.
Data stewardship and data lineage are crucial for achieving and maintaining high-quality master data in MDM. Data stewards are responsible for defining and enforcing the rules and standards for master data creation, update, deletion, and validation. They also coordinate with other data stakeholders to ensure the consistency and alignment of master data across different sources and systems. Data lineage helps to trace and monitor the origin, movement, and changes of master data throughout the MDM lifecycle. It also supports data quality analysis, data impact analysis, and data compliance reporting.
Data stewardship and data lineage in MDM can bring many advantages to an organization, such as improved data quality, enhanced data integration, and increased data value. Data stewardship and data lineage can help identify and resolve data issues, like duplicates or inaccuracies, as well as ensure compliance and validity of master data. They can also facilitate integration and interoperability of master data across different systems, reduce complexity and cost of data migration, and increase trust, confidence, and usability for decision making. In addition, they can demonstrate the return on investment and business impact of MDM initiatives.
Data stewardship and lineage foster accountability and transparency, crucial for reliable MDM. By tracking data and ensuring compliance with standards, they improve data accuracy and business decisions. Prioritize these elements to maximize MDM's impact on your organization.