Master Data Management Decisions Made by the Data Governance Organization A Whitepaper by First San Francisco Partners
Master Data Management Decisions Made by the Data Governance Organization Master data management (MDM) is about people and process; it is not about technology. Implementing MDM technology alone will not address operational and business process challenges. Rather, mastering data involves people taking action through established data policies and processes. Data Governance ensures that data in the MDM hub is of high quality and can be trusted by business users. Lacking Data Governance, organizations do not have consistent data definitions or know what constitutes a data problem, who is accountable, what decisions need to be made, or how to escalate and resolve issues. In this way, Data Governance plays a vital role in an MDM implementation. The MDM hub provides the requisite data cleansing, duplicate detection, survivorship, hierarchy management, and merge/unmerge capabilities. While Data Governance provides the processes, policies, organization and technology guidance required to manage and ensure the availability, usability, integrity, consistency, auditability, quality and security of the data in the MDM hub. Data Governance creates a culture of accountability and ownership around the quality of data and provides escalation mechanisms to manage data quality. The Data Governance Organization s (DGO) role is to understand and outline data requirements Page 2
from the business for the MDM hub. Once these requirements are understood, the DGO facilitates the creation and agreement of foundational elements such as data models and data dictionaries to support the requirements. The goal of the DGO is to ensure the right resources, policies and processes are in place; and that the data is available, usable and secure in the MDM hub. The DGO manages confidence in the data by ensuring that the data stays clean over time, it is monitored and measured, and data quality is continuously improved. In order to deliver on its mandate, the DGO is responsible to guide and make decisions concerning an MDM implementation. What MDM decisions does the DGO need to make? This paper will reveal and discuss the important data governance decisions that need to be made by the DGO for a successful MDM implementation. Data Governance Organization Decisions for MDM Success During the requirements gathering phase of an MDM implementation, the DGO is involved in understanding and defining the scope and requirements for data that will be managed in the MDM hub. Several categories need to be considered, including: Entity Entity Ownership and Accountability Policies, Processes and Standards Data Integration (Inbound and Outbound) Service Level Agreements Data Quality Match & Merge (Survivorship) User Interface and Security General Maintenance The entity types that are in the initial scope to be mastered need to be defined. For example, client, product, supplier, legal entity etc. Hierarchies, relationships and associations that need to be mastered must also be defined (client, account and product hierarchies), as well as the association of individual to company, and party to address, etc. Additional entities, hierarchies, relations and associations can be added as needed. Page 3
Ownership and Accountability Ownership and Accountability ensures that there are people in place to drive decisionmaking and execution on data related matters. A Responsible, Accountable, Consulted and Informed (RACI) matrix outlining data owners (by data element), data custodians, who can create, view, update/change and delete the data should be developed and agreed upon. Policies, Processes and Standards Policies are business rules or guidelines that need to be in place in order to manage and govern the core set of data elements in the MDM hub. Policies ensure that consistency exists around how data is managed. Policies, processes and standards should be clearly defined, followed and enforced by the DGO. Policies are business rules used to manage the data. Business rules fall in the categories of data management, data integrity, data lifecycle, data access and retention. Processes are workflow processes that define how the business rules will be implemented. Workflow processes can be integrated into a data governance workflow tool. Foundational processes include: Issues identification, escalation and resolution Data changes, change control, new elements Data quality management approach Standard Operating Procedures (SOPs) Performance baselines Data reconciliation and synchronization Standards define a means of maintaining consistency. Standards are created to help mitigate risks of multiple data definitions and usage resulting in financial, operational, and compliance related inefficiencies. Agreed upon definitions of each entity type, purpose, and usage of each data element, authoritative source for each data element must clearly be articulated. Data Integration (Inbound and Outbound) The DGO should define the type of data to be integrated into the MDM hub and where the data is expected to come from (which internal source systems will supply the data); including defining the authoritative and most trusted sources, frequency and timeliness of the data. These definitions inform the development of service level agreements between the producers of data, consumers of data and other business groups: Page 4
Inbound Data Sources What type of data does each source supply? Why is this needed? At what frequency should they supply it? Who owns the quality of the data? Outbound Data Sources Which applications will receive master data directly from the MDM hub? Service Level Agreements Service Level Agreements guide the standards and set expectations as it pertains to the quality of service provided by the DGO. SLAs are identified and developed to explain the level of service the organization is to expect from the DGO. SLAs are typically defined between: The DGO and the business For example, how are data quality exceptions handled? What is the duration to fix it? An SLA would state a response will be received from the DGO in 2 business days with a remediation plan and time line to fix the issue. Producers of data and consumers of data For example, to address data integrity, how will changes to data in upstream systems be addressed? The SLA would state that producers of data must perform an impact analysis within a given time period and present the recommendations to the DGO before implementation. The DGO and IT For example, in regards to serviceability and the ease in which a service may be performed and completed on a system by the IT group, an SLA may state that 80% of service failures are recovered in less than 30 minutes. Page 5
Data Quality The required data quality targets per entity type and each data element that is to be measured should be defined. The DGO should monitor data issues and track progress over time to show the value of the MDM hub: How good does the data have to be? How is the data to be monitored? What are the data quality measurements, metrics and key performance indicators (KPIs)? What scorecards need to be created? Match & Merge (Survivorship) Survivorship rules attempt to create the best version of integrated data in cases where multiple systems can create and/or change a record that refers to the same record in production applications. They also serve to understand the required process when master data is deleted in a contributing source system. The DGO must define survivorship rules to detect duplicate entities based on specific match rules. For example, Find duplicate contacts; Exact match on Full Name and Organization and Email Address, Fuzzy on Full Name, Fuzzy on Organization and exact Email Address, etc. The MDM hub must be able to automatically merge duplicates or set them aside for manual verification by the DGO based on the configuration of the match rules. User Interface and Security The extent of the MDM user community (those users who will be using the MDM tool) and all associated security rights need to be defined and understood. The data stewards need access to the MDM hub. However, not all data stewards have user access rights to all the data in the MDM hub. Some data stewards can see and work on all the data in MDM and others can only see and work on certain types of data within a certain subject area. The following questions should be addressed: What type of security is required around the data? What user access rights and privileges are required by user type? How is data security monitored and improved? Page 6
General Maintenance The DGO also needs to define the requirements for job and system monitoring, maintenance, backup and recovery, and system support. Conclusion Many decisions need to be made in the course of an MDM implementation, and those related to data management in particular should be made by the DGO. An MDM implementation has a high likelihood of failure without an effective decision-making structure and process. That is the purpose and value of a well-defined Data Governance process and DGO. Consequently, one of the most important factors in preparing for an MDM implementation is setting up the DGO to facilitate these critical decisions. The need for the DGO arises from the fact that data is now being shared at an enterprise level rather than used at the application level. With proper data governance practices in place, the MDM hub will deliver trusted data to the organization and the organization will realize the full benefits of mastering data. Author: Kelle O Neal As Founder and Managing Partner of First San Francisco Partners, Kelle O Neal manages specialist data governance and data management consulting services to complex organizations that deliver faster time-to-results. Kelle can be reached at kelle@firstsanfranciscopartners.com or through First San Francisco Partners Website (www.firstsanfranciscopartners.com). Page 7