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 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.
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
Roadmap to Financial Services Data ManagementAs 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.
Banking MDM Implementation Do’s and Don’tsThere 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. DO remember to address organizational change management. DO consult internal subject matter experts to understand different parts of your business. DO balance governance and business benefits. DO leverage MDM to turn data into valuable information.||DON’T underestimate the critical importance of executive buy-in. DON’T take a technology-first approach. DON’T neglect to include portions of the MDM roadmap. DON’T treat MDM like a one-time cleanup. DON’T take a one-size-fits-all approach.|
Consolidate Your Data With Hitachi SolutionsTake 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!]]>