When requests for proposal (RFP) for master data management solutions are narrowly focused on a short-term business need within a single business function or on a single business data type - such as customer (customer data integration - CDI) or product (product information management - PIM) - critical master data management (MDM) functionality can be easily overlooked. Wrongly, IT teams and systems integrators 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 yet, 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.
By including the most important 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. A well thought-out RFP will allow you to quickly reap the returns from selecting a complete and flexible MDM platform that is able to address both your current and future business requirements.
Ten MDM Blunders to Avoid
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 that 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 ROI.
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 companys 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.
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 about 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.
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 holders 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.
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. Because 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.
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 CDI or 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.
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.
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 roll back any manual errors or exceptions - a typical activity in large organizations where several data stewards are involved with managing master data.
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.
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.
The Best Antidote: Selecting an Integrated and Flexible MDM Platform
It is likely that once your organization starts to make its departmental MDM 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. And so, it is important to first seek out and evaluate an MDM solution that adequately addresses these ten fundamental MDM capabilities. It is just as important to also assess the MDM platforms 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 reduce technology risk and improve your ROI because additional integration and customization efforts will not be necessary in order to make the system operational. Yet another benefit gained by having these 10 MDM components integrated within the same MDM platform is that software deployment is much faster and easier to migrate over time. Lastly, it is wise to check customer references to evaluate their enterprise-wide deployment and to ensure that the vendors MDM solution is both proven and includes all of these enterprise MDM platform capabilities.
To avoid the common blunders made by MDM software evaluation teams and guarantee long-term success, you should make sure that these essential components are built into your master data management RFP. By including these ten critical MDM requirements in your RFP, you will be well on you 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 your organization.
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