Master Data Management (MDM) programs are inherently risky because they are long running and span several business functions. Business thinks Master Data quality is an IT problem and IT knows MDM won’t succeed without Business ownership. Applying Six Sigma methodologies helps you manage a challenging program using well understood management techniques that serve to remove risk and improve communication.
The quality of any useful set of Master Data will never approach Six Sigma perfection of no more than 3.4 Defects Per Million Opportunities (DPMO) because of the human tolerance for poor quality data. People have a wonderful built in, ‘error correction’, mechanism of understanding context and the rules of language games. The Missing Letter Effect from behavioral psychology (Healy 1976) demonstrates how humans use context to supplement poor quality data.
In spite of our tolerance for poor quality data we can still use the Six Sigma DMAIC methodology of Defining, Measuring, Analyzing, Improving and Controlling (DMAIC) to manage our MDM program and mitigate project risk.
DMAIC – Define
In the Define phase we list our business opportunities that MDM will address. For example, you might define the MDM as an opportunity to consolidate four regional customer masters into one central customer master.
The results of the Define phase go into the project charter as the goals, objectives and deliverables of the project.
DMAIC – Measure
In the Measure phase of an MDM project we develop metrics that track the severity of the data quality problem. It is critical that the metric be Real, Reliable and Repeatable. ‘Real’, because it must be relevant to the business. The metric must address a real business problem and measure it in business terms. The metric must be ‘Reliable’, in the sense that it leaves no room for doubt and includes a drill-down to any underlying facts. Lastly, the metric must be, ‘Repeatable’, because you will need to show historical trends in order to show the progress of the MDM program.
An example of a good metric that addresses a business issue might be an audit item found in a Sarbanes-Oxley audit; Immediate Suspension of Terminated User Accounts (ISTUA). This metric would track the time between when an employee is terminated and when all of the employees user accounts have been suspended. Consolidating the various employee and user accounts into a Person master would help reduce this metric.
David Loshin (Loshin 2005) defined eight criteria that make up a good metric, including Clarity of definition, Measurability, Business relevance, Controllability, Representation, Reportability, Tractability and Drill-down capability. Loshin emphasizes there is no reason for creating a metric which can’t be controlled or improved.
DMAIC – Analyze
During the Analyze phase we might use a fishbone cause-effect diagram (Figure 1) to analyze the causes of disintegrated master data. We begin the fishbone by showing the undesirable effect of, ‘Duplicate Disintegrated Customer Data’, in a box on the right side of the diagram. Then we list the various causes that produce this effect including Architecture causes, Governance causes, Organization causes and Process causes along arrows pointing into the Effect.
Figure 1 – Fishbone Cause & Effect Diagram
DMAIC - Improve
During the Improve phase we develop strategies to change the architecture, processes, organizations and governance that will help achieve the goal of integrated master data. We can use SIPOC (Supplier Input Process Output Customer) in the Improve phase to brainstorm improvements to the process.
The SIPOC diagram in Figure 2 depicts the new improved process, ‘Unique ID Service’, and lists Order Management as the supplier function. They supply the input of customer name that is matched in the, ‘Unique ID Service’, into the output, ‘Matched Customer’. And Strategic Procurement might be the customer of this process.
Likewise we use the same SIPOC to depict how we could summarize spend with suppliers and receivables from customers into a new Key Performance Indicator (KPI) called, ‘Trade Balance’.
Figure 2 – SIPOC Diagram
DMAIC – Control
In the Control phase of Six Sigma DMAIC methodology we use data quality metrics to control the quality of master data so that it does not degrade over time. We also develop metrics that show the progress of the overall MDM project.
All MDM solutions should deliver the following components or capabilities:
Governance: Master Data Governance is the authority that decides how master data is maintained, what it contains, how long it is kept and how changes are authorized and audited.
Consistent Unique ID Service: Each MDM system assigns a common Master ID that is mapped to every source master record. This cross-reference is maintained in the Master Data Registry.
Attribute Management of Factual Master Data: The consolidated master contains factual information that is common across all sources in the enterprise, including data values, relationships and hierarchies.
Hierarchy Management: MDM includes the capability to manage hierarchies or structures of master data; for example customers and their sub-units and sites and products and their component BOM’s.
Data Quality Service: MDM includes the capability of measuring and improving the quality of master data specified by the data stewards and owners.
Master Data Registry: The MDM Registry is where the Common Master ID is assigned and maintained. The registry should have the capability to compare, merge & de-duplicate master records in order to normalize redundant data records.
Data Stewardship: Stewards are appointed by the owners of each master data source; they have knowledge of the current source data and the ability to recommend how to transform the source into the master data format.
Business Rules: Master Data Stewards establish common business rules for updating and maintaining master data in each domain.
Data Management Workflow: Master Data Stewards define a Workflow for creating and updating their master data according to the Business Rules for that domain.
Process Integration Services: Master data for each domain is integrated into the business processes of each business unit using a common workflow subscription.
Master Data Integration Services: Each Master Data domain exposes common data integration services for creating, updating and synchronizing master data for that domain with other operational and analytic systems.
Figure 3 – MDM Stoplight Chart
Figure 3 shows a simple stoplight chart that illustrates the progress of each of these required MDM capabilities across the various master data domains. In the case of Consumer Master, you may not have an active project and this chart clearly shows the gap.
Conclusions for Applying Six Sigma Principles to MDM
While Master Data may never approach Six Sigma quality due to our inherent tolerance for error, we can still benefit from using Six Sigma methodologies and tools to manage our MDM projects and programs. MDM establishes and documents the data quality thresholds so repeatable processes can be developed to improve how information supports real business goals.
1. Healy, A. F. (1976). Detection errors on the word ‘the’: evidence for reading units larger than letters. Journal of Experimental Psychology: Human Perception and Performance 2 (2): 235–42.
2.Loshin, David. Developing Information Quality Metrics.DMReview. May 2005
ABOUT THE AUTHOR
Joe Danielewicz spent 28 years at various business units of Motorola in IT data architecture and enterprise architecture. Mr. Danielewicz is now an independent consultant in Enterprise Architecture and ERP Integration Planning.