Master data management RFPs: 10 mistakes to avoid

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 request for proposal (RFP).

By Ravi Shankar, Siperian

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 organisation.

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 overall 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 10 critical MDM requirements in your RFP, you will be well on you way to laying the foundation for a comprehensive MDM project that addresses your current requirements, and is also able to evolve to address unforeseen future data integration requirements across the organisation.

Mistake #1: ignoring data governance needs at the project- or enterprise-level

Data governance is unique to each and every organisation 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 organisation. 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 #2: 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 organisations 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 favourable 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.

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, organisational relationships are depicted as legal hierarchies of parent and child organisations, 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 standardisations 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 centralised within the MDM system. If your company has already standardised 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 organisation, 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 organisation 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 organisation 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 synchronise 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 synchronise data back to either operational or analytical applications but not both. Without the ability to synchronise master data with both operational and analytical applications, your ability to extend the MDM platform across the organisation will be limited.

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.

Ravi Shankar is director of product marketing at Siperian, Inc.