By Paul Bergamo

Master data management programs seeking to establish one version of the truth for critical data assets continue to come and go in organizations.

MDM explained Organizations seeking to address legal and regulatory requirements to better know their customers, or who seek to drive cross-marketing initiatives with common customer information, or make business intelligence insights more actionable often seek out Master Data Management (MDM) solutions to help. MDM enables you to connect targeted pieces of information across departments, branch locations, and lines of business into a single view. MDM is a combination of business and technology processes, technologies, and tools required to aggregate, match, and correct data, and to maintain and use it consistently across many applications or the enterprise.

For some organizations, deriving or even communicating the value from these programs is hard to articulate effectively, and these implementations are often at risk of failure or cancelation. Other firms seem to have good management support and recognize the value and opportunity resulting from MDM implementations. So what drives this success, and what kinds of value are these organizations realizing?

Let’s grant the obvious: Successful MDM or data consolidation work requires executive management sponsorship, good cross-business support, and governance capabilities led by business stakeholders. Let’s even assume a level playing field in data governance and data management capabilities (a topic in and of itself). What then drives success? Simply put, a well-articulated destination, bounded by clear near-term tactics that can demonstrate quick value. Also, incrementally built capabilities and measurements that initially help direct activities and behaviors while communicating value, but that also help sponsors see benefits and stay engaged.

For banks, as with most organizations, core target benefits are:
•    Forward looking. They allow for cross- and up-sell opportunities, and improved customer service.

•    Foundational. Data quality/consistency is improved, enabling better and faster insights, improved service offerings, and the modernization and rationalization of IT systems (costs, simplification, optimization, etc.)

•    Operational. Time to market for new products is improved and complaints are reduced.

•    Compliance and privacy. They provide more accurate and complete data to support compliance reporting (BASEL II, SOX, etc.) and consolidated mechanisms to consistently support privacy opt-in/out etc. across all business lines

The key is to be clear on what your targets are, how you will measure success, and most important, how that success will be achieved incrementally. Big bang, top-down-only, or single, large program solutions almost never work. As an example, we often look to MDM solutions to help improve business intelligence time-to-answer (TTA) metrics. Complex organizations often have very high TTA for important processes and situations. One firm sought to reduce its direct marketing campaign time from around three months to a day or less. With a clear target in mind, and the realization that that could not be achieved in one step, they established a plan that realized business improvements in TTA, while building out the technological and business capabilities required to support their needs. 

In the end, over a multiyear period, they transitioned from a TTA of 45 days to one day. Now, for targeted interactions, they can do highly automated self-learning with a TTA of 200 milliseconds. In addition, as they made progress they sought to build a flexible and sustainable solution designed to continue to evolve. The solutions were a dramatic improvement by any measure and they did it by keeping the goal in mind, but driving to incremental goals to demonstrate success and value; and by communicating the impact to both business and IT. In this case, they were able to show a correlation between business capability investment, and sustainable platform improvements and costs.

In another example, a firm sought to improve its ability to sell across very siloed lines of business. Opportunities for cross-selling existed, but each business head saw the problem differently. Each also saw major impediments, but engaged in heroics to make it work since the CEO was the sponsor and it was something they needed to do.

After three years of working to establish basic capabilities and drive consistency in a warehouse platform, they had little in the way of good metrics to demonstrate the impact of the work and they still struggled with achievable goals. On the surface they looked like they were doing the right things, but they didn’t take them deep enough or establish good, achievable rally points for the different teams. They had large measures like total numbers for cross-sells, but few milestones that reflected the reality on the ground, which was that they had 30-year-old platforms with different data meanings and contexts across different lines of business with different priorities, customer privacy, and legal entity concerns. So, where it was difficult or unclear on how to agree on definitions, measures, opportunities, or even basic ownership or stewardship, they simply avoided it, leaving big holes in their capabilities and causing growing executive frustration that the expected benefits were still unrealized.

Once the issues were put on the table, the business recognized the need to change its approach. When it began to take actions in this direction, it started to see even more executive support across lines of business and better front-line engagement in their efforts.

 

What changed? (1) The basic data management capabilities were maturing during this time; (2) The workers realized the need to rally resources around just one tactical solution; (3) They created real accountabilities with clear, top-to-bottom measures to move the effort forward (percent compliance with consolidated data standards, definitions, delivery to platform, etc.); and (4) They collected measurements. While the four components are critical, it is clear that simple, aligned metrics at the larger program level, as well as at the frontline worker level, helped to alter biases and behaviors blocking success.

The bottom-line is that as you look at your existing MDM solutions or consider altering them to better align with changing compliance, regulatory, competitive, or operational needs, you must lay out a simple executable plan with near-term benefits and milestones, with business measures that incrementally mature with your capabilities. 

 

 

Author

Paul Bergamo is a General Partner/Practice Leader with NewVantage Partners, a business and technology strategy firm specializing in data and analytics. Paul leads the firm’s practice in the areas of big data, data governance, IT governance and operations, and process architecture. He is a frequent author and speaker on issues in data governance and IT effectiveness.

 

 

[This article was posted on March 13, 2012, on the website of ABA Banking Journal, www.ababj.com.]   

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