Daniel Teachey, DataFlux 

According to recent analyst research, many companies see master data management (MDM) as a cure for integration or data management mistakes. The issue with approaching MDM in that way is the risk of missing the forest for all the trees. While many individuals naively consider MDM to be a technology issue, others consider the way that MDM processes and policies can address, and ultimately “fix”, both data and integration challenges.

The way we perceive our approach to MDM is that it is not about technology, and in fact, is not really about the data either. Yet this concept is often difficult for people to grasp, especially folks in IT. But the bottom line is clear: MDM is about using key data to improve the business, and if the program is not approached in that way, it increases the risk of failure.

If you step back from your MDM program and examine why it’s important and how it can enrich your business, you’ll see that every aspect of a successful MDM initiative revolves around the concept of business improvement. For example, you can’t strengthen customer relationships if you don’t know who your customers are. It’s unwise to create and produce innovative products without a way to efficiently get the products in the hands of your customers. The examples go on and on. Data is at the heart of business. And good data is the engine that drives successful businesses.

We have identified three areas of business problems that good data can help combat: risk mitigation, cost control and revenue optimization. Using data to see the big picture – whether it’s due diligence in an acquisition or assuring regulatory compliance reporting – can greatly reduce risk exposure. Data can also be used to control costs. Properly managed data can help companies unearth the tiny areas where money is leaking out of the organization – ways that could never be tracked manually. And, with a diligent data quality approach, you can deliver significant revenue gains for your business.

Still not convinced MDM is a business issue? Consider the fact that businesses have thousands of business processes to execute each day. A recent Forrester Research study estimated only 5 to 15 percent of those processes are automated, which means the overwhelming majority of companies rely on human involvement to execute these processes. MDM-as-technology apologists would argue this only shows the white space that exists in getting more data automatically processed. Our approach is that MDM creates an environment where companies can automate those business processes, get consistent execution on them and optimize them based on factual data that comes from the organization. It’s more than just automating data; it’s doing something useful with it that drives business and increases profits.

Look at how data can be stored in an organization – in independent silos. Let’s say marketing is using a database to generate leads for sales. Finance has another database with customer history and corporate P&L. Sales and manufacturing have yet another database to manage order history and product inventory. A successful campaign by the marketing team is deemed a success because the featured product’s sales are up. However, the financial reality is that those sales came at the expense of another product’s sales, so it wasn’t really a success. Meanwhile, manufacturing didn’t know about the campaign, so it didn’t adjust inventory levels. The company must now deal with an inventory surplus, customer dissatisfaction because orders aren’t being shipped and a financial hit to the bottom line. All because data isn’t being shared.

It’s true that part of a successful MDM initiative involves cleaning up and sorting through this data that has traditionally been kept in silos. However, it isn’t meant to cause finger-pointing within an organization. After all, it was just the way things were done. And while cleaning up databases and correcting the problems of the past is part of effective MDM, it shouldn’t be the main driver of the program.

The bottom line is this: If the goal of your MDM program is just to have immaculate data, you’re missing the point. The data must be driving a business goal. Otherwise, the return on your MDM investment will never materialize, because your business process won’t improve. In today’s competitive environments, with the customer, employee and regulatory demands – it is essential to run your business as efficiently as possible. The key to better business is better data, managing and funding your data infrastructure like you would your other corporate assets. This is only achievable if you build data management and data governance processes based on business requirements.

Check out these white papers:

Populating a Data Quality Scorecard with Relevant Metrics

Too often, data governance teams rely on existing measurements as the metrics used to populate a data quality scorecard. But without a defined understanding of the relationship between specific measurement scores and the business’s success criteria, it is difficult to determine how to react to emergent data quality issues - and determine whether their fixing these problems has any measurable business value. This white paper by David Loshin explores ways to qualify data control and measures to support the governance program.

Building a Data Quality Scorecard for Operational Data Governance

Operational data governance is the manifestation of the processes and protocols necessary to ensure that an acceptable level of confidence in the data effectively satisfies the organization’s business needs. In this white paper, David Loshin from Knowledge Integrity examines how a data governance program defines the roles, responsibilities, and accountabilities associated with managing data quality, and how a data quality scorecard provides an effective management tool for monitoring organizational performance with respect to data quality control.

by Ravi Shankar , Siperian, Inc. 

Critical master data management (MDM) functionality can be easily overlooked when request for proposals (RFP) are narrowly focused on a single business data type—such as customer (Customer Data Integration) or product (Product Information Management) — or on near-term requirements within a single business function.  Consequently, IT teams and systems integrators alike run the risk of selecting and investing in technologies that may be difficult to extend to other data types or difficult to scale across the organization.  Worse, such solutions will likely require costly and extensive custom coding in order to add additional business data entities or data sources, or to extend the system to other lines of business or geographies.  In order to avoid these costly pitfalls, bolster the return on investment, and reduce the over-all project risk, it is important that your RFP include key business data requirements across several critical business functions including sales, marketing, customer support and compliance.

To avoid the common mistakes made by MDM software evaluation teams and ensure long term success, you should make sure that key components are built into your master data management RFP.  By including these ten critical MDM requirements in your RFP, you will be well on your way to laying the foundation for a complete and flexible MDM solution that addresses your current requirements, and is also able to evolve to address unforeseen future data integration requirements across the organization.

Ten Costly RFP Mistakes to Avoid  

Mistake #1:  Failing to ensure multiple business data entities can be managed within a single MDM platform

When you select and deploy an MDM platform make sure it is capable of managing multiple business data entities such as customers, products, and organizations all within the same software platform.  By doing so, system maintenance is simplified and more cost effective which results in lower total cost of ownership.  A less favorable alternative is to deploy and manage separate master data solutions that each manages a different business data entity.  However, this approach would result in additional system maintenance and integration efforts and a higher total cost of ownership.  Another advantage of an MDM platform which can handle multiple data types is that implementation can begin with a single business data entity like customer, and can later be extended to accommodate other master data types—resulting in rapid return on investment.  

Mistake #2:  Ignoring data governance needs at the project- or enterprise-level

Data governance is unique to each and every organization since it is based on the company’s business processes, culture, and IT environment.  However, companies typically select an MDM platform without much thought to their enterprise data governance needs.  It is critical that the underlying MDM platform is able to support the data governance policies and processes defined by your organization.  In contrast, your data governance design could be compromised and forced to adapt to the limitations of some MDM software platforms with fixed or rigid data models and functionality.  Controls and auditing capabilities are also important data governance components.  In order to properly support this functionality, your RFP should require the MDM platform to integrate with your security and reporting tools to provide fine-grained access to data and reliable data quality metrics.

Mistake #3:  Failing to ensure the MDM platform can work with your standard workflow tool

Workflow is an important component of both MDM and data governance, as it can be used to approve the creation of a master data entity definition and to determine, in real-time, which conflicting data entities survive.  Workflow can also be used to automatically alert the data steward of any data quality issues.  So in preparing a master data management RFP, it is important to raise the question of how the MDM platform will integrate with the standard workflow tool that you have selected.  Several MDM vendors bundle their own workflow tool and may not offer integration with your standard workflow tool.

Mistake #4:  Failing to ensure the solution supports complex relationships and hierarchies

With a single entity master data hub, such as customer, hierarchies and relationships are relatively straightforward.  For example, organizational relationships are depicted as legal hierarchies of parent and child organizations, while consumer relationships are those belonging to a common household.  On the other hand, hierarchies among multiple data entities can be highly complex.  Examples include: retail locations in the Eastern region stocking only certain products; complex counterparty legal hierarchies determining credit risk exposure; or an account holder’s spouse being a high net-worth individual.  Make sure your MDM request for proposal requires the solution to be capable of modeling complex business-to-business (B2B) and business-to-consumer (B2C) hierarchies, along with the definitions of those master data entities within the same MDM platform.

Mistake #5:  Relying on fixed Service Oriented Architecture (SOA) services

Reliable data is a prerequisite to supporting SOA applications— applications that automate business processes by coordinating enterprise SOA services.  Since MDM is the foundation technology that provides reliable data, any changes made to the MDM environment will ultimately result in changes to the dependent SOA services, and consequently to the SOA applications.  IT professionals need to ensure the MDM platform can automatically generate changes to the SOA services whenever its data model is updated with new attributes, entities, or sources.  This key requirement will protect the higher-level SOA applications from any changes made to the underlying MDM system.  In comparison, MDM solutions with fixed SOA services that are built on a fixed data model will require custom coding in order to accommodate any underlying changes to the data model.

Mistake #6:  Cleansing data outside of the MDM platform

Data cleansing includes name corrections, address standardizations, and data transformations.  Typically the number of source applications that provide reference data to departmental level Customer Data Integration (CDI) or Product Information Management (PIM) solutions is relatively small.  In these cases, the data can be efficiently cleansed at the source using commonly available data quality tools.  In contrast, the number of sources for an enterprise MDM deployment spans multiple departments and typically comprises tens or hundreds of systems.  In this scenario, cleansing the data at the source systems is not viable.  Rather, data cleansing needs to be centralized within the MDM system.  If your company has already standardized on a cleansing tool, then it is important to ensure the MDM solution provides out-of-the-box integration with the cleansing tool in order to leverage your existing investments.

Mistake #7:  Thinking probabilistic matching is adequate

There are several types of matching techniques commonly in use—deterministic, probabilistic, heuristic, phonetic, linguistic, empirical, etc.  The fact is no single technique is capable of compensating for all of the possible classes of data errors and variations in the master data.  In order to achieve the most reliable and consolidated view of master data, the MDM platform should support a combination of these matching techniques with each able to address a particular class of data matching.  A single technique, such as probabilistic, will not likely be able to find all valid match candidates, or worse may generate false matches.

Mistake #8:  Underestimating the importance of creating a golden record

For MDM to be successful within an organization, it is not enough to simply link identical data with a registry style because this will not resolve inconsistencies among the data.  Rather, master data from different sources need to be reconciled and centrally stored within a master data hub.  Given the potential number of sources across the organization and the volume of master data, it is important that the MDM system is able to automatically create a golden record for any master data type such as customer, product, asset, etc.  In addition, the MDM system should provide a robust unmerge functionality in order to rollback any manual errors or exceptions—a typical activity in large organization where several data stewards are involved with managing master data.

Mistake #9:  Overlooking the need for history and lineage to support regulatory compliance

Today, business users not only demand reliable data, but they also require validation that the data is in fact reliable.  This is a challenging and daunting undertaking considering that master data is continually changing with updates from source systems taking place in real-time as business is being transacted, and while master data is merged with other similar data within the master data hub.  The history of all changes to master data and the lineage of how the data has changed needs to be captured as metadata.  In fact, metadata forms the foundation for auditing and is a critical part of data governance and regulatory compliance reporting initiatives.  As a result, and because metadata is such an essential component of MDM, it is important that your RFP defines the need for history and lineage.

Mistake #10:  Implementing MDM for only a single mode of operation: analytical or operational

An enterprise MDM platform needs to synchronize master data with both operational and analytical applications in order to adequately support real-time business processes and compliance reporting across multiple departments.  In contrast, CDI and PIM solutions are most often implemented at the departmental level with the objective of solving a single defined IT initiative such as a customer relationship management migration or a data warehouse rollout.  These deployments will typically only synchronize data back to either operational or analytical applications but not both.  Without the ability to synchronize master data with both operational and analytical applications, your ability to extend the MDM platform across the organization will be limited.

MDM Success Begins with Selecting an Integrated and Flexible MDM Platform

Once your organization starts to make its departmental master data management projects operational, you will find that your larger enterprise requirements will expand to include other business data types and other lines of business or geographies.  Therefore, it is important to first seek out and evaluate an MDM solution that adequately addresses these ten essential MDM capabilities.  It is also important to assess the MDM platform’s ability to support these ten core capabilities out-of-the-box, as they should be integrated components of a complete enterprise-wide MDM platform.  In this way, you will be able to mitigate technology risk and improve your return on investment since additional integration and customization will not be necessary in order to make the system operational.  Another benefit gained by having these ten MDM components integrated within the same MDM platform is that software deployment is much faster and easier to migrate over time.  Finally, it is wise to check customer references to evaluate their enterprise-wide deployment and to ensure that the vendor’s MDM solution is both proven and includes all ten enterprise MDM platform capabilities.

By including these critical MDM requirements in your RFP you will achieve greater success with your MDM initiative along with a more rapid deployment and faster time to value.  Not to mention, a well thought out RFP will allow you to quickly reap the returns from selecting an integrated and flexible MDM platform that is able to address both your current and future business requirements.

About the Author

Ravi Shankar is Director of Product Marketing at Siperian, Inc., an innovative provider of the most flexible master data management platform.  For more information, contact the author at rshankar@siperian.com or visit www.siperian.com.

Join Gwen Thomas, Founder and President of The Data Governance Institute, and and Cliff Longman, CTO of Kalido, for an insightful live Web seminar to review the growing issue of “bad data” within organizations, hosted by Computerworld.

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