FREE DM Review Site Registration!
Sign-up today and access DM Review on the Web!

Your FREE registration entitles you to:
FREE email newsletters

FREE access to all DM Review content

FREE access to web seminars, resource portals, our white paper library and more!

   

Lessons Learned in Master Data Management

BI Best Practices

  • Radha R
  • DM Review Online, December 13, 2007

Radha would like to thank Srikanth M for contributing this month’s column. He is a principal with MindTree Consulting, where he heads the company’s master data management practice and is responsible for conceptualizing and defining the go-to-market strategy as well as anchoring customer implementations. 

 

At last, I am happy to note that master data management (MDM) is getting the attention it deserves; at the same time, I pray that the industry does not blow it out of proportion and take the attention away from the core issue. In hindsight, it’s surprising how master data that forms the foundation of any enterprise initiative was never discussed holistically when organizations initiated multimillion dollar initiatives such as customer relationship management (CRM), supply chain integration/optimization, data warehousing (DW), business intelligence (BI) and other integration-related initiatives. It probably is one primary reason for many of these initiatives not delivering their anticipated value.

 

Master data is the most important and, in fact, the only information asset available to integrate data and business processes in a seamless manner. Treating master data issues as a trivial technical issue is the biggest mistake that many architects and designers make when architecting complex business solutions. Only when business users reject strategic and big-budget solutions after concluding that the newly built information infrastructure has added to the never-ending confusion on the subject of information trustworthiness do people realize how important identifiers and master data attributes are. There are many organizations that have seen their CRM, DW/BI or other enterprise-wide solutions fail to create an impact due to the improper and incomplete foundational master data. Quick fix solutions to master data are applied as a band aid to go-live with the implementation, and these temporary mechanisms introduce additional complexity into the overall architecture. These solutions built with a myopic view can never withstand the frequently changing business and regulatory environment, and problems continue to fester in ongoing operational maintenance of the system until they erupt and become a major issue.

 

A lot of MDM initiatives have taken off but, unfortunately, are driven by the IT team and with a technology-focused approach. A few organizations have even progressed with setting up the information governance processes; however, very few have managed to integrate these data governance programs into their core business processes (with business ownership), which I believe is the cornerstone to solve data governance issues. Intelligently embedding data governance process components as part of core business processes is critical in making the initiative successful by assisting change management and ensuring adoption by business. A “simple” technology stack with a strong governance program well integrated into the core business processes is the key to overcome master data issues.

 

I truly believe in a holistic people-process-technology approach with multiple parameters to be focused on each of these pillars to ensure a successful master data program in an organization.

 

Four Critical Dimensions of an MDM Strategy

 

Any MDM strategy has to address four critical dimensions: business impact, scope, solution and change management. MDM initiatives have to be carefully planned and thought through holistically. There has to be an emphasis on continued focus on managing information assets and it should not be viewed as a project, but an ongoing enterprise-wide program. See Figure 1.

 
Figure 1

 

Business Impact

 

It’s very important and often challenging to quantify the ROI for an MDM program. However the impact of not having harmonized master data is easier to derive. One of the easiest ways to do this is to identify initiatives which critically depend on a successful MDM program. The business value and ROI defined for the other initiatives will hence be at jeopardy if MDM is not successful. Another approach to set the goal for an MDM initiative is to identify questions which business cannot answer today due to master data issues. Irrespective of the approach adopted to build a case, the bottom line is that it is important to identify and specify the business goal for the MDM program. See Figure 2.

 
Figure 2

 

Scope

 

Seldom do MDM initiatives focusing on a single master data domain such as customer, product or supplier address all business issues. At the same time, all master data domains cannot be addressed in one go. A step-by-step prioritized approach and a well-defined scope to harmonize critical domains are important. Key scope-related aspects that have to be thought through while defining the MDM strategy are:

 

  • Any taxonomy/identity related changes required for harmonization such as adoption of industry standards and regulatory compulsions have to be assessed and included in the scope of the MDM program.
  • Depending on the business goals set for the MDM program, the spread of the program in terms of the business lines to be covered and business partners relevant for the program have to be included as part of the scope.
  • Based on the business goals, the primary need for the MDM program - whether to address analytical or operational needs has to be assessed.

The outcome of these steps will help in defining the solution and the scope of change management required in the organization.

 

Solution

 

The first decision to be taken on the technology front is the impact that you are willing to absorb on the enterprise architecture. Various implementation styles exist. However, across implementation styles the fundamental components remain the same, and they include:

 

  • Authoring source – Where will master data authoring happen?
  • Publishing – Who are the consumers of master data?
  • Latency – How soon should master data in the repository be published to the consumers?
  • Integration – How will the MDM platform be integrated with rest of the enterprise applications?
  • Workflow – Should the solution include workflow and how should the business process be controlled?

Based on the business goal and the envisioned governance process, the right style that will work for the organization has to be decided. Based on the architecture blueprint, the right technology has to be chosen to implement the solution. The MDM market is still immature with innumerable products and technology platforms available. At one end of the spectrum are specialist vendors with technologies to support specific master data domains, and at the other end, there are generalists with technologies, which can support managing master data for all domains. Hence it’s critical to assess the business needs from a longer term perspective and adopt the right technology platform for the enterprise. Choice of technology should also align with the long-term enterprise architecture envisioned by the organization.

 

The solution has to be finally tested to check for resilience against changes to the business organization such as addition of new business lines and consolidation of business lines. Mergers and acquisitions on master data domains have to be assessed and included as part of the scope. See Figure 3.

 
Figure 3

 

Change Management

 

Last but not least, the most important pillar, which makes or breaks the MDM initiative is managing the transition towards the new way of governing information assets. Old habits die hard; hence it’s important that a practical approach which is or appears noninvasive be taken. Carefully embedding governance processes within existing business processes is the key. A focused team, monitoring the quality of data and at the same time enriching master data so that new business questions can be answered or newer insights into business can be gained, will go a long way in ensuring success of the MDM program. 

 

Before you proceed with your MDM program there are many fundamental questions to ask as shown in Figure 4.

 
Figure 4

 

In fact there are many more important questions to ask as those suggested in Figure 5, and making an earnest attempt at answering these questions can guide you onto the right path to a successful MDM program.

 
Figure 5

 

I have found that it is always good in MDM initiatives to take the help of experts who have lived through these problems. Please beware of those who will lead you through the garden path initially as this path is full of thorns and you have to wade through them carefully.

 

 


Radha R heads the Data Warehousing and Business Intelligence Practice for MindTree Consulting, an international IT and services company, co-headquartered in New Jersey and Bangalore, India. She is responsible for overall profitability and customer satisfaction for MindTree's DW/BI business across all global markets. With more than 17 years of industry experience, Radha has handled various product and service lines in her career. In her earlier role at MindTree, Radha was responsible for developing and cultivating all its India-based customer relationships.

For more information on related topics, visit the following channels:



Industry Vendors