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Performance Management and MDM Convergence

Enterprise performance management (performance management for short) and master data management (MDM) are two of the most talked-about topics in business intelligence (BI) today. Although the industry treats them as two distinct disciplines with their own methodologies, tools and implementations, in reality, they are two dimensions of an enterprise information management strategy. Without the key performance indicators (KPIs) of performance management, MDM becomes an exercise in data integration, and without MDM, performance management cannot achieve the promised enterprise-level impact. This convergence will become one of the driving forces of our industry for the next couple of years. The convergence will be both logical, in how to apply various methods and techniques, and technical, in how to make the various technologies work together.

Performance Management Simplified

It goes by many names in the industry: corporate performance management (CPM), enterprise performance management, business performance management, etc. Performance management can take many forms, such as reporting systems, analytic applications, dashboards, scorecards, balanced scorecards and strategy maps. In the end, though, all are concerned with a similar issue: providing the organization with visible, actionable metrics to illustrate and guide its performance.

Let’s examine the two concepts of visibility and actionability.

Visibility is not as simple a concept as the name would imply. Many times, the performance data that really matters is locked away in an obscure system from which it cannot be easily teased out without some technical programming. The data has to become readily available, or visible, to the decision-makers so that they can use the data.

Actionability refers to the ability to take some form of corrective action based on the data presented. Whether the presentation is in a computer system is irrelevant. What is key is that the data being presented is something that can be controlled and manipulated by business users.

By tying together actionable and visible metrics into a cause-and-effect relationship, you get an analysis chain. Figure 1 illustrates a simple example of metrics that have varying degrees of visibility. Strategic accounts attainment has direct impact on change in customers, which has a direct impact on financial health.

An organization will have hundreds of these analysis chains, but there are normally a dozen or so that ultimately drive the business. We will use this model to tie in MDM shortly.

Figure 1: Example of an Analysis Chain


MDM Simplified

The best way to start with master data is with logical understanding. Great technical tools exist to build the physical master data architecture, but without the right logical model, they will fail.

Because master data, whether product, customer, employee, vendor or others, could contain hundreds of elements, segmentation is needed to follow a divide-and-conquer analysis. If not, the analytic effort to understand hundreds of attributes making up the customer or product in one big group will become an endless cycle of analysis paralysis.

Segmentation should first be broken down into four levels.

Level 1: Identification. Identification of the elements that determine uniqueness is much easier said than done. One would think a universal ID like Social Security number would work, but it is almost never the right answer. Uniqueness is almost always determined by a concatenation of keys that are either naturally occurring (Social Security number, tax ID) or system owned (customer number, vendor number).

Level 2: Common elements. Level 2 is reserved for elements that are commonly queried but are not required for uniqueness. Common elements should be used by a large group of users, a large amount of eventual data queries or both. The common level can contain attributive information to complement the identification level, such as when the ZIP code is in level 1 and the town name is in level 2.

Level 3: Extended internal. Level 3 is where the large group of internal profile and attributive data elements will go. If elements are not used by a large group and not queried heavily, they end up in level 3 if they are sourced from internal systems. Internal systems are any systems that your enterprise has complete control over without dependencies or codevelopment by other organizations.

Level 4: Extended external. Level 4 is for the outside data brought in house. The outside data may be purchased data, such as credit ratings, product rankings, address correction files, or shared data, such as co-owned systems with business partners, customers or vendors. Two classic examples of extended external elements are market share data and external shipper (FedEx, UPS, etc.) data.

With this four-level understanding and the analysis chain, we can now combine them to show how performance management and MDM converge on a logical level.

Figure 2: Four Levels of MDM Segmentation


Using the Same Yardstick

The analysis chain normally spans multiple departments and moves into both system-captured data (enterprise resource planning, customer relationship management [CRM], etc.) and nonsystem data (spreadsheets, paper, etc.). MDM ensures that regardless of where on the analysis chain or from which department the data is being sourced, the same yardstick is used.

In most organizations without MDM, the definition of a customer is fragmented. Imagine going to a football game where each official has their own yardstick to measure the movement of the ball. It would be anarchy.

Without MDM, performance management ends up using many yardsticks, such as the identification of customer (perhaps it is the shipping address, the billing address, the legal entity, the tax ID, etc.), and the results often do resemble data anarchy.

If we use the analysis chain to vet out the concepts of MDM, the anarchy starts to disappear.

Financial metrics and level 1 master data form the basis for convergence. Why? Financial metrics are almost always the highest quality data, and level 1 master data is the basis upon which all other levels are built. If you accept this premise, then working backward in the analysis chain becomes a validation and augmentation exercise. Although in practice it is much more complex, there are four major steps in ensuring alignment:

Step 1: Formulate a basis four-layer MDM model by examining systems and conducting business workshops. The baseline MDM model will then need to be more fully vetted in the next three steps.

Step 2: Confirm MDM model alignment with financial metrics needs. Using the four-layer model of identification, common, internal and external, vet for validity against financial metrics.

If the four-layer model does not hold for the financial metrics, it needs to be revisited or augmented. Keep in mind that this does not confirm the four-layer model is complete yet.

Step 3: Vet the MDM model against other systems and metrics. Based on the available systems in the analysis chain, formulate the complete four-layer model. In accessing the available systems, the large number of common, internal and external attributes will be exposed. Based on this population set, assign the attributes to the appropriate layer in the model.

Step 4: Verify the MDM model against nonsystem data. Even though the previous steps create a complete model of available data, the logical data that people may need, which may be locked up in spreadsheets or file cabinets, must be used to validate the completeness of the four- layer model. It is quite common to find internal and external attributive data that is important to the business but is not in the core CRM or enterprise resource planning (ERP) system (i.e., it is not easily visible). This step will ensure that the four-layer model will contain both automated and nonautomated data.

Figure 3: Start with a Cornerstone and Build up


This four-step process is then repeated for the major dozen or so analysis chains to ensure functional breadth of coverage. At this point, performance management and MDM are aligned at a logical level, which provides a foundation for any technical efforts.

Although this is just one of many different methods for bringing together performance management and MDM, it exemplifies the convergence at a logical level, which is unavoidable in today’s BI environment.


Technical Convergence

With the consolidation of the pure-play BI software vendors into larger organizations, the technical landscape is changing rapidly. In the preconsolidation world, an organization would need to find its MDM vendor of choice and its CPM vendor of choice and then try to make them work together. The software may all come from one vendor but may not be integrated, thereby still missing the mark on the consolidated architecture. In the end, the integration of the technical components and metadata layers is mainly left up to the customer. Key to the technical convergence is the metadata convergence.



In the consolidated world, large software vendors have a vested interest in making the products work together both logically and technically. This will help them to capture higher market share and increase successful enterprise-level-impacting projects. The reality is that pure-play BI vendors did not have the R&D dollars to invest in making the products work well together. The new large software vendors do, and the marketplace will benefit from this increased R&D.

One may argue that with fewer vendors, there is a risk of stifled innovation. In actuality, the greater access to R&D dollars and the larger customer base and multidisciplinary field teams that the large vendors bring have the possibility of bringing the converged performance management and MDM product landscape well beyond the fragmented R&D investments in the smaller pure-play vendors.

Performance management and MDM are two major areas that are already on a collision course. Organizations that understand how to bring the two together in a logical and technical fashion stand to gain competitive advantage and economies of scale not possible in a fragmented approach. This likens back to the early days of data warehousing where nobody really bought an extract, transform and load (ETL) or reporting tool. Instead, they bought a data warehouse solution that needed ETL and reporting tools as line items in the overall solution. The same can be said for today’s BI solutions that will have performance management and MDM as two of the major line items. Convergence is inevitable; get on board!


Anthony Politano has more than 20 years of experience in IT and is the author of Chief Performance Officer. Read his blog at

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Source:  Performance Management and MDM Convergence, Ensuring Alignment, By Anthony Politano, DM Review Magazine, March 2008

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