Financial institutions of today are adrift in a sea of data — product and service purchase history, customer information, financial transactions, marketing campaigns, and more — sourced from various mobile applications and devices. This abundance of data provides valuable opportunities, but it can also create challenges if this crucial data is inconsistent across different systems and mishandled as a result.

Add to that the fact that certain financial services organizations are under intense regulatory pressure, and you have the potential for a perfect storm. Take, for example, banks, which are subject to the Beneficial Ownership Rule (BFO). Implemented and enforced by the Financial Crimes Enforcement Network, BFO requires banks to identify all significant owners of legal entities and their accounts in order to prevent illegal financial activities such as money laundering, tax evasion, and fraud, as well as other major crimes such as terrorism. The consequences for violating this and other rules and regulations are often severe.

The risk of any of the problems mentioned above can be significantly reduced, if not eliminated entirely, by master data management for financial services organizations.

The Cost of Bad Data

“Poor quality data is a problem” isn’t exactly a revolutionary statement, but businesses across all sectors are guilty of underestimating how significant a problem bad data really is. For a bit of perspective, poor quality data costs the U.S. economy $3.1 trillion dollars per year a number that becomes even more staggering when considered on a global scale. And the consequences of poor quality data aren’t just financial; for financial services organizations, bad data comes with the added risks of poor decision-making, client dissatisfaction, and regulatory non-compliance.

The Growing  Need for Master Data Management in FinServ - Supporting Graphic 1

The good news is that inaccurate data doesn’t have to be a permanent problem. It’s leading causes — human error, departmental silos, data duplication, and so on — are easily identifiable, which makes it possible to plan around them. To craft a solid data strategy, you should first ensure that your existing data and any new data entering your systems meets the following criteria:

  • Data must be accurate and free from error.
  • Data must be complete and comprehensive, without any gaps in collection.
  • Data must be consistent; the data stored in one system should not contradict that same data stored in another.
  • Data must be standardized and input in the correct format.
  • Data must be timely — that is, it must be collected at the right moment in time.
  • Data must be up to date so that you have access to the most relevant information.
  • Data must be legitimate and must be extracted from a credible source.

Another key element of a strong data strategy is proper data governance, which leads us to master data management.

What is Master Data Management?

Master data management (MDM) refers to collaboration across business units and departments in an organization in relation to the orchestration, enablement, and workflow of a given data domain. In the financial services sector, data domains typically include client, product, and assets. Mastering these domains provides a comprehensive view of all the data stored within those domains. Having a single consolidated database enables business users to:

  • Identify the relationships between clients
  • See which products a client already has (and which ones they don’t)
  • Determine which regulations apply to each transaction for compliance purposes
  • Detect fraud
  • Simplify Right to be Forgotten requests
  • And more

A key concern for MDM is security. Managing who has access to business-critical information, who gets to make changes to that information, what kind of changes can be made, and when those changes will be enacted is challenging. MDM provides a single location for managing these changes and gives clarity on how they affect all business units.

MDM is designed to be an “active data” solution that keeps enterprise systems synchronized. For example, you could use a data service to automatically capture, verify, store and distribute/synchronize client information, categorize individuals based on where they are in the sales pipeline, and so on. This ensures that the right data is captured at the right time, and that that data is accurate, consistent, and up to date. This can be challenging given the fact that financial institutions often draw data from multiple sources, some of which churn out “ugly” or inconsistent data. This data must be cleaned up and made consumable so that business users can utilize it to make more efficient decisions around which markets to target, how to prevent fraud, how to upsell and cross-sell to customers, and so on. Therefore, a key element of MDM is screening data before it is entered into the system in order to ensure that it meets a bank’s data quality standards.

Roadmap to Financial Services Data Management

As with any successful data strategy, proper financial services data management requires careful consideration and planning. First and foremost, you need to establish the scope of your MDM project — that is, define key business areas and which data needs to be governed. To do the latter, you must determine which domains are the most critical to master, how those domains affect your organization, and what the potential risks of not managing that data are.

From there, you need to create an inventory of all of your existing data sources and figure out which ones you can afford to eliminate and which ones you can consolidate. In most instances, it’s possible to replace multiple smaller systems with a single, more robust solution. The fewer systems there are to keep track of, the easier it is to create a central repository and enforce good data hygiene.

During the scope stage, you’ll also need to choose your implementation style. There are four common MDM implementation styles:

  • Consolidation: Master data is consolidated from multiple sources to create a golden record, which serves as a single source of truth.
  • Coexistence: Similar to Consolidation style, master data is consolidated from multiple sources and stored in a central MDM repository and updated in its source systems.
  • Registry: Duplicates are identified and eliminated by comparing data across multiple systems, and unique global identifiers are assigned to matched records.
  • Centralized: Master data is stored in a central repository, where it is enhanced and then returned to its respective source system.

Note that choosing an implementation style can be challenging for financial institutions that have acquired multiple companies over time because they need to figure out how to manage those companies.

Once you’ve established the scope of the project, you’re ready to create an organizational structure similar to the one shown in the chart below:

The Growing  Need for Master Data Management in FinServ - Supporting Graphic 2 

Each and every individual included in this structure plays a key role in ensuring the overall success of your financial services data management project. For example, your MDM Governance Council should, ideally, consist of a small group of company leaders responsible for sponsoring the program, setting its direction, and issuing final approval on scope, structure, and processes. Your MDM Core Team should create and consistently update MDM supporting structures and provide leadership for other team members, and your IT Enabling Resources should include data analytics responsible for profiling, cleansing, enriching, and auditing data.

Now that you’ve created your organizational structure, you need to figure out which processes you’ll need to manage it and which technology you’ll use to support it.

At the end of all of this, it’s important to note that master data management for financial services is a combination of people, processes, and technology. It’s possible to master data without technology — but without people and processes in place, no technology can give you what you really need.

Banking MDM Implementation Do’s and Don’ts

There are a few simple do’s and don’ts every financial institution should observe in order to guarantee the success of their MDM project:

DO allocate and invest the right amount of time from your team.

DON'T underestimate the critical importance of executive buy-in.

DO remember to address organizational change management.

DON’T take a technology-first approach.

DO consult internal subject matter experts to understand different parts of your business.

DON’T neglect to include portions of the MDM roadmap.


DO balance governance and business benefits.


DON’T treat MDM like a one-time cleanup.

DO leverage MDM to turn data into valuable information.

DON’T take a one-size-fits-all approach.

Consolidate Your Data With Hitachi Solutions

Take the guesswork out of financial services data management by partnering with a dedicated team that’s navigated these waters before. At Hitachi Solutions, our experience in the financial services sector and our expertise in project implementation and organizational change management make it easy for us to eliminate the friction that comes with knowledge transfer. We take a technology-agnostic approach to data management in banking, which enables us to work with clients from any background, on any system.

Best of all, we won’t just lead you through the MDM process — we’ll collaborate with you every step of the way, so you can take ownership over your achievements and develop the confidence to tackle any challenge that comes your way. Get in touch with the Hitachi Solutions team today to get started!