Master Data Management Defining & Measuring MDM Maturity, A Continuous Improvement Approach

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1 Master Data Management Defining & Measuring MDM Maturity, A Continuous Improvement Approach DEFINE IMPROVE MEASURE Presentation by Mark Allen 1

2 About the Author Mark Allen has over 25 years of data management and project management experience including extensive planning, deployment, and management experience with master data and data governance. Mark is co-author of two books: Master Data Management in Practice, Achieving True Customer MDM (2011) and Multi-Domain Master Data Management, Advanced MDM and Data Governance in Practice (2015). Mark manages the Enterprise Data Governance program at Anthem Inc. Prior to Anthem, Mark was a senior program manager in customer operations groups at both Sun Microsystems and Oracle Corporation. At Sun Microsystems, Mark served as the manager and lead data steward throughout the planning and implementation of the company s enterprise customer master data hub. Throughout his career Mark has championed and implemented many data management programs establishing data governance, data quality management, data stewardship, and change management practices. Mark has been a speaker at Data Governance and Information Quality conferences, and has served on various customer advisory boards focused on sharing and enhancing MDM and data governance practices. Mark can be contacted at or please visit for additional reference. Presentation by Mark Allen 2

3 Defining a MDM Maturity Model Presentation by Mark Allen 3

4 MDM: A Highly Managed & Controlled State Common to any MDM strategy is the need to transition master data from an insufficiently managed state to a highly managed and controlled state necessary to achieve the goals identified in the MDM program plan. During the planning of an MDM program, a maturity model should be defined to serve as a high level, multi-year, measureable roadmap to the desired end-state. Achieving MDM goals will require a strong continuous improvement commitment with program performance measurements focused on maturity milestones associated to various data management practices and capabilities. The maturity model should reflect where data management practices and capabilities need improvement to enable master data quality and control. Presentation by Mark Allen 4

5 MDM Maturity Models MDM and Data Governance maturity models tend to reflect either a behavioral or a functional based approach for measuring maturity: Behavioral Approach: Expressing maturity from a behavioral perspective using maturity phase descriptions such as Undisciplined, Disciplined, Reactive, Proactive, Advanced, Controlled. Functional Approach: Expressing maturity from a functional perspective using maturity phase descriptions such as Unstructured, Structured, Defined, Repeatable, Managed, Optimized. Reactive Undisciplined Structured Unstructured Controlled Proactive Optimized Managed The number of maturity phases can vary. Four or five phases are commonly used in a maturity model. Presentation by Mark Allen 5

6 The Functional Approach Is Most Effective The functional approach is most effective for measuring the initiation and maturity of MDM practices and capabilities, particularly with a multi-domain MDM strategy where progress needs to be tracked across the various domains in scope. The functional approach assumes that behavioral conditions such as reactive and proactive are conditions that continue to exist throughout the functional phases and across domains. There is never a purely proactive MDM state absent of reactive situations. Unplanned or unpredictable conditions will always exist creating reactive situations that must be addressed at any point in an MDM program, even when the program has reached a highly managed and optimized state. Presentation by Mark Allen 6

7 Functional Approach Phases Be sure to have clear definitions for what these maturity phases represent, such as: Unstructured: A key practice or capability area has not been sufficiently organized and enabled to support the MDM program goals and objectives. Structured: The practice or capability area has been sufficiently organized, enabled, and has initiated key efforts in support of MDM goals and objectives Managed: The practice or capability area is being effectively managed and is achieving key milestones that are driving MDM practices and quality improvement objectives Optimized: The practice or capability area has reached a highly functional, fully mature state continually able to meet its objectives and interacts effectively with other program practices and capability areas Regardless of the actual phase names and definitions chosen, they should reflect phases and milestones that are: Relevant to the MDM program goals and objectives Consistent and measureable across the MDM practices and capability areas Meaningful to the intended audiences Presentation by Mark Allen 7

8 Examples Of MDM Maturity Milestones Maturity Milestones Maturity State Measurement or Evidence Data Governance Governance team and charter has been identified Structured Charter ratification and audit verification Master data has been identified Structured Governance approved master data list per domain Data management policies and standards are enforced Managed Monitoring and auditing of policy requirements Metrics and dashboards are driving master data control Optimized MDM program metrics and quality dashboards Data Stewardship Processes, tools, and training needs are defined Structured Handbook and operating procedures exist Master data control points and practices are defined Structured Control points in data flow and life cycle models Data stewards identified and assigned to MDM practices Managed Names, roles, and responsibilities are communicated Data stewards are meeting quality control requirements Optimized Performance reviews with job requirements and goals Data Integration Data integration projects reviewed by data governance Structured Project reviews with data governance signoff Data stewards engaged in data integration project teams Structured Data stewards identified and active in project teams Agreement on ETL rules and data acceptance criteria Managed Business acceptance signoff by data governance Integration and quality rules are standardized and reusable Optimized Rules repository and usage tracking exists Data Quality The data quality team is aligned with data governance Structured Engagement rules are defined and adhered to Analysis of key data quality issues has occurred Structured Tracking and prioritization of data quality issues Data quality management policies and standards are enforced Managed Policies and standards exist with regular auditing Data quality is maintained within control targets Optimized Monitoring and remediation is maintaining quality Metadata Management Metadata management scope and approach identified Structured Governance approved metadata management plan Metadata management priorities have been established Structured Metadata management roadmap exists Metadata management policies and standards are enforced Managed Policies and standards exist with regular auditing Key data definitions are distinct with data steward control Optimized Data steward gated change control processes exist Presentation by Mark Allen 8

9 Maturity Dashboard Example Indicates maturity level of data management practices that are the key drivers for achieving the MDM program goals and milestones MDM Maturity Dashboard Level of Management and Control Unstructured Structured Managed Optimized Data Governance: Data Stewardship: Data Integration: Data Quality Management: Metadata Management: Green Green Yellow Red Yellow Presentation by Mark Allen 9

10 What The Maturity Dashboard Is Saying The Data Governance component (Green): Data Governance is firmly established, being managed effectively and functioning as planned in support of the MDM program. There are some data management areas where policies, standards, and measurement are still needed in order for data governance to achieve the optimized phase. The Data Stewardship component (Green): Data stewardship roles, responsibilities, and MDM assignments are developing as planned. The company has recognized need for establishing formal data steward job roles and is on target for fulfilling these positions. There are a few areas of master data management that still need data stewards assigned. The Data Integration component (Yellow): In some system areas, the plans, architecture, and capability for integrating with the master data hub is on track and progressing well. In other areas the integration plans are lagging due to delays with the project readiness and implementation plans. The Data Quality Component (Red): Key data quality improvement projects are on hold while a consulting partnership and license agreements for a third party vendor application are being renegotiated to improve capabilities needed for master data quality management. The Metadata Management Component (Yellow): Where data governance and data stewardship practices are maturing well the metadata management practices are also improving. But due to the data integration and data quality plans lagging behind, this has impacted the overall progress and maturity of metadata management. Presentation by Mark Allen 10

11 Performance Measurment Presentation by Mark Allen 11

12 Performance Measurement: Key Points Measurement of MDM program performance and maturity should be defined across Strategic, Tactical, and Operational levels. Be sure performance measurements have purpose, track important aspects of the program aligned to the key goals and objectives, and will influence decisions that will improve the program s ability to manage and control master data. Over the long haul, a MDM program will risk failure if there are insufficient performance and maturity measurements to navigate and manage the program at the strategic, tactical, and operational levels. Continuous improvement will be directly influenced by what the program is able to measure and where improvement can be best focused. Presentation by Mark Allen 12

13 Measurement Model Example Level Types of Measurements Audiences Strategic Tactical Operational MDM Roadmap and Maturity Key Performance Indicators Program Financials Audit & Compliance Tracking MDM Roadmap and Maturity Program Activity Metrics Quality Dashboards Partner/Vendor Performance Data Quality Monitoring Issue Resolution Tracking Data Steward Performance Service Level Agreements Steering Committees Data Governance Council Stakeholder Reviews Consulting Partners Program Management Office Data Governance Teams Partners and Vendors Project Managers/Leads Data Stewards Data Administrators IT Support 3rd Party Support Presentation by Mark Allen 13

14 Strategic Level Measurement At the strategic level, executive sponsors and steering committees will need to know how the MDM program is performing against its goals and how this is benefiting the company and reducing business risk. Reporting how data governance and data quality are improving to reduce risk and improve business efficiency will command the most interest at the strategic level. Key Performance Indicators (KPIs) should be defined and used to report these conditions. Keep in mind that as data governance practices mature, less effort will be needed for data quality management because as there is better governed data there will be less need for data correction. Data Quality Index 12 Month Trend Data Quality Dimension Completeness Validity Consistency Duplication Accuracy Data Definition Quality Status Detailed Reports Presentation by Mark Allen 14

15 Strategic Level Measurement: KPIs Key Performance Indicators (KPIs) should be used to focus on the primary performance factors that drive the master data management program, improve master data quality, improve business efficiency, and reduce business risk. KPIs should report on factors such as: Progress toward the achievement of the overall program goals and Return on Investment (ROI). Program budget versus program spend Improvement of key capabilities targeted in the program roadmap and maturity model Reduction of business risk factors associated to the MDM program Operational improvement associated to MDM program efforts Improvement in MDM data quality associated to data governance and data stewardship efforts. Status of any MDM remediation efforts associated to corporate audit or regulatory compliance requirements Incidences of breaches related to master data security and privacy Presentation by Mark Allen 15

16 Tactical Level Measurement At the tactical level, performance measurements should be focused more widely and deeply on the program s key initiatives and processes to produce a more granular set of end-to-end program measurements. These measurements will allow the program management office to have a clear picture of what s driving the program, what s impacting the program goals and objectives, and where adjustments are needed. The results and observations from many of the tactical level measurements will contribute to the KPIs reported at the strategic level. Data Quality Index 12 Month Trend Data Quality Dimension Completeness Validity Consistency Duplication Accuracy Data Definition Quality Status Detailed Reports Presentation by Mark Allen 16

17 Key Types of Tactical Measurement MDM Roadmap and Maturity: Close measurement of program, resource, and budget conditions that directly enable or impact the MDM capabilities, maturity, and delivery plans. Program Planning Activity & Decisions: Tracking of important, on-going program planning activities, meetings, and decisions that support the fundamental foundation, processes, and disciplines of the MDM program. Data Quality Improvement: Measuring various aspects of master data quality management. Measuring not only the quality of the master data elements themselves, but also the quality of the business metadata and reference data associated to master data. Partner/Vendor Performance: Many aspects of a company s master data program can involve external partnerships and thirdparty vendors. The partner and vendor performance along with the budgets and costs associated with these contracts should be regularly tracked and evaluated. Presentation by Mark Allen 17

18 Operational Level Measurement Performance measurement at the operational level of MDM will focus more on the processes, applications, and data steward activities in each functional area or MDM domain associated to master data touch points, quality control, and issue management. The operational level of performance measurement reflects the degree to which that the operational areas and domain teams have a handle on the day-to-day management and quality control of the master data. Presentation by Mark Allen 18

19 Key Types of Operational Measurement Data Management Processes and Controls: At the heart of MDM are the processes and controls established to manage the quality and integrity of the master data. There should be regular tracking and reporting of these process conditions and controls. Issue Resolution Tracking: Monitoring of the MDM issues and defects being logged. Being able to characterize master data management issues and the actions taken to resolve these will provide valuable insight about how well the company is handling master data issues. Data Steward Performance: Measuring how data stewards are performing in the key touch point areas where there is master data entry, usage, management, and quality control. Service Level Agreements (SLAs): Measuring where SLA performance is most important to the quality, support, and timely availability of master data. Presentation by Mark Allen 19

20 Continuous Improvement Presentation by Mark Allen 20

21 Continuous Improvement: Key Points For the MDM program to mature, continuous improvement is assumed but not a given. Continuous improvement requires many enabling factors involving people, process, and technology, These factors need to be examined across the MDM program to determine what improvement opportunities can most benefit the program and where lack of improvement will be an inhibiting factor. A continuous improvement plan needs to define an ongoing focus representing reasonable and applicable improvement targets that will support the program s sustainability and maturity. Achieving many of the program maturity milestones will require specific process and functionality improvements that should be clearly identified in the program plan and roadmap. Presentation by Mark Allen 21

22 Improving Key Practices And Capabilities Each of the key practice and capability areas should be examined to identify where master data management improvement is needed to achieve program maturity goals. Examples: The data governance and data stewardship practices need to mature before the MDM data quality and metadata management objectives can be achieved. The ability for the program to reach the Optimized maturity phase requires advanced data monitoring capabilities. The planning and improvement of this capability area should be factored into the program plan and identified as a key dependency in the maturity model. Presentation by Mark Allen 22

23 Where MDM Programs Struggle The Most MDM programs struggle the most with conditions such as: Lack of sufficient data governance and data stewardship Lack of cross-functional cooperation and alignment with business processes Coordination across disciplines such as data quality and data integration Poor data quality Improvement opportunities will vary with each practice and capability area. Take advantage of the opportunities when and where possible. Many companies already have specific solution design and continuous improvement methodologies employed to constantly evaluate and improve their processes, services, and products. Such methodologies should be leveraged to help support the MDM program improvement needs. Presentation by Mark Allen 23

24 Master Data Management Stay Focused! DEFINE IMPROVE MEASURE Presentation by Mark Allen 24

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