Data becomes an asset only when it is efficiently harnessed and managed. Because firms tend to evolve into silos, their data often gets organized that way as well, resulting in multiple references and unnecessary duplication of data that dilute its value. Master Data Management (MDM) architecture helps to avoid these and other pitfalls by applying best practices to maximize data efficiency, controls, and insights.

MDM has particular appeal to banks and other financial institutions where non-integrated systems often make it difficult to maintain a comprehensive, 360-degree view of a customer who simultaneously has, for example, multiple deposit accounts, a mortgage, and a credit card. MDM provides a single, common data reference across systems that traditionally have not communicated well with each other. Customer-level reports can point to one central database instead of searching for data across multiple sources.

Financial institutions also derive considerable benefit from MDM when seeking to comply with regulatory reporting requirements and when generating reports for auditors and other examiners. Mobile banking and the growing number of new payment mechanisms make it increasingly important for financial institutions to have a central source of data intelligence. An MDM strategy enables financial institutions to harness their data and generate more meaningful insights from it by:

  • Eliminating data redundancy and providing one central repository for common data;
  • Cutting across data “silos” (and different versions of the same data) by providing a single source of truth;
  • Streamlining compliance reporting (through the use of a common data source);
  • Increasing operational and business efficiency;
  • Providing robust tools to secure and encrypt sensitive data;
  • Providing a comprehensive 360-degree view of customer data;
  • Fostering data quality and reducing the risks associated with stale or inaccurate data, and;
  • Reducing operating costs associated with data management.

Not surprisingly, there’s a lot to think about when contemplating and implementing a new MDM solution. In this post, we lay out some of the most important things for financial institutions to keep in mind.


MDM Choice and Implementation Priorities

MDM is only as good as the data it can see. To this end, the first step is to ensure that all of the institution’s data owners are on board. Obtaining management buy-in to the process and involving all relevant stakeholders is critical to developing a viable solution. This includes ensuring that everyone is “speaking the same language”—that everyone understands the benefits related to MDM in the same way—and  establishing shared goals across the different business units.

Once all the relevant parties are on board, it’s important to identify the scope of the business process within the organization that needs data refinement through MDM. Assess the current state of data quality (including any known data issues) within the process area. Then, identify all master data assets related to the process improvement. This generally involves identifying all necessary data integration for systems of record and the respective subscribing systems that would benefit from MDM’s consistent data. The selected MDM solution should be sufficiently flexible and versatile that it can govern and link any sharable enterprise data and connect to any business domain, including reference data, metadata and any hierarchies.

An MDM “stewardship team” can add value to the process by taking ownership of the various areas within the MDM implementation plan. MDM is just not about technology itself but also involves business and analytical thinking around grouping data for efficient usage. Members of this team need to have the requisite business and technical acumen in order for MDM implementation to be successful. Ideally this team would be responsible for identifying data commonalities across groups and laying out a plan for consolidating them. Understanding the extent of these commonalities helps to optimize architecture-related decisions.

Architecture-related decisions are also a function of how the data is currently stored. Data stored in heterogeneous legacy systems calls for a different sort of MDM solution than does a modern data lake architecture housing big data. The solutions should be sufficiently flexible and scalable to support future growth. Many tools in the marketplace offer MDM solutions. Landing on the right tool requires a fair amount of due diligence and analysis. The following evaluation criteria are often helpful:

  • Enterprise Integration: Seamless integration into the existing enterprise set of tools and workflows is an important consideration for an MDM solution.  Solutions that require large-scale customization efforts tend to carry additional hidden costs.
  • Support for Multiple Devices: Because modern enterprise data must by consumable by a variety of devices (e.g., desktop, tablet and mobile) the selected MDM architecture must support each of these platforms and have multi-device access capability.
  • Cloud and Scalability: With most of today’s technology moving to the cloud, an MDM solution must be able to support a hybrid environment (cloud as well as on-premise). The architecture should be sufficiently scalable to accommodate seasonal and future growth.
  • Security and Compliance: With cyber-attacks becoming more prevalent and compliance and regulatory requirements continuing to proliferate, the MDM architecture must demonstrate capabilities in these areas.


Start Small; Build Gradually; Measure Success

MDM implementation can be segmented into small, logical projects based on business units or departments within an organization. Ideally, these projects should be prioritized in such a way that quick wins (with obvious ROI) can be achieved in problem areas first and then scaling outward to other parts of the organization. This sort of stepwise approach may take longer overall but is ultimately more likely to be successful because it demonstrates success early and gives stakeholders confidence about MDM’s benefits.

The success of smaller implementations is easier to measure and see. A small-scale implementation also provides immediate feedback on the technology tool used for MDM—whether it’s fulfilling the needs as envisioned. The larger the implementation, the longer it takes to know whether the process is succeeding or failing and whether alternative tools should be pursued and adopted. The success of the implementation can be measured using the following criteria:

  • Savings on data storage—a result of eliminating data redundancy.
  • Increased ease of data access/search by downstream data consumers.
  • Enhanced data quality—a result of common data centralization.
  • More compact data lineage across the enterprise—a result of standardizing data in one place.

Practical Case Studies

RiskSpan has helped several large banks consolidate multiple data stores across different lines of business. Our MDM professionals work across heterogeneous data sets and teams to create a common reference data architecture that eliminates data duplication, thereby improving data efficiency and reducing redundant data. These professionals have accomplished this using a variety of technologies, including Informatica, Collibra and IBM Infosphere.

Any successful project begins with a survey of the current data landscape and an assessment of existing solutions. Working collaboratively to use this information to form the basis of an approach for implementing a best-practice MDM strategy is the most likely path to success.