A Business Case for Mastering Master Data Management (MDM)

by   |   October 14, 2013 7:15 am   |   0 Comments

Sakari Jorma

Sakari Jorma of Software AG

Businesses face a world of rapidly-changing technologies and huge data growth, which impacts directly on issues of data quality. Organizations that fail to address this are likely to struggle. Manually collating and analyzing customer data, much of it duplicated, across multiple IT systems is time-consuming, inefficient and prone to error.

Master data management (MDM) is an essential approach to address this critical issue. As consumers, we recognize the value of MDM in many ways. When we move to a new house, we must inform details to a large number of organizations ranging from banks to doctors’ surgeries in order to ensure our lives are not disrupted.

How much easier would it be if we could simply forward the information once to, say, the post office working under the principles of MDM, confident that the records of all relevant service providers were immediately updated through a seamless end-to-end process?

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The concept of effective MDM is gaining momentum. Gartner, for example, has predicted a 19.1 percent compound annual growth rate in worldwide MDM software revenues between 2010 and 2015, as organizations consider how to make the most of one of their most important assets—the data that sits within and across the business. These business leaders recognize that not only will this drive internal operational improvement but, combined with optimized business processes, will also enable a more proactive and personalized level of customer service.

For many firms, such change will represent a fundamental transformational initiative. It will likely require a degree of specialist third-party expertise in analyzing where the business currently sits on the MDM maturity curve and so determine what steps need to be taken to achieve a unified enterprise MDM strategy. The goal of this strategy would be to achieve a “one version of the truth” set of master information which can be easily accessed by authorized employees throughout the organization.

Consider another example. When a retailer adopts MDM as part of a properly-integrated back-office system within a multi-channel consumer offering, customers benefit from a uniformly high-quality user experience. This includes, for example, making sure that customers access precisely the same product information, irrespective of the country or sales channel they choose to adopt.

A key first step therefore for any business looking to address the challenge of providing this unified view is to standardize all the data around the product or service it offers. Manually making such a change across multiple disparate in-house systems is hard to achieve. By contrast, a process-driven MDM implementation which enables standardization across all systems, applications and processes will create a holistic view that is easy to set up, access and run.

A Path to Business Process Improvements
In looking at what makes a best-practice MDM approach, there is a critical difference to be made between data and information. Despite its name, effective MDM is not simply about managing data held within the business: it is about managing information, which requires the involvement of individuals to convert raw data into actionable output through intelligent analysis.

In short, this two-stage process requires the use of IT to standardize data and the involvement of people to manage information through effective data governance processes. In implementing an MDM initiative, a business should not only look at issues of data quality but also at technologies to enable and support behavioral change.

For example, if a data centre operative continues to fill in a particular form incorrectly, new automation technology will not correct this but simply highlight the error more quickly. It is only by introducing parallel training and performance monitoring that the relevant behavior will change and full benefit derived from any technology or process change.

However, there is no doubt that incomplete or inaccurate enterprise data will present serious problems to the business and its customer base. It renders automated processes inefficient, by throwing up unnecessary exceptions requiring manual intervention to resolve the issue, wasting time and causing frustration.

An effective MDM implementation minimizes errors and enhances straight-through processing, improving business process performance, operational efficiency, decision-making and forecasting, data governance and regulatory compliance while minimizing risk.

A Combination of Automation and Central Management of Data
All this highlights the critical importance of focusing on process rather than data held across disparate systems. By looking first at key business processes within, say, CRM, logistics or HR and only then assessing what data each needs to be effective and where this is held within the business, this is likely to provide greater return and so form the basis of a more powerful business case.

By answering a series of questions around existing business processes therefore, a basic MDM strategy will emerge in which data relating to each process can be managed centrally. These questions cover such areas as whether the MDM vision aligns to the broader organizational strategy, the existence of an internal data governance committee for master data decision-making and whether data quality is monitored in core processes, in order to determine where the business currently sits on the MDM maturity scale.

The impact can be dramatic, as the combination of automation and centralization can not only improve outcome quality by removing human error but also cuts process times from weeks to minutes. The resulting processes are also more compliant and deliver better customer service and confidence levels. And it becomes possible to link such performance improvements directly to specific key performance indicators such as time saved in completing business processes.

A best practice MDM strategy is likely to reflect a flexible “think big, start small” process-driven approach which will build out across the enterprise on the back of initial project successes. The result will ideally incorporate a set of tailored industry-specific processes, enabling the business to maintain complete control of all its data assets, relating to customers, products, finance, vendors and partners.

Sakari Jorma is regional director of data solutions for Europe, Middle East and Africa at Software AG, a maker of business process and data integration technologies.

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