Best Ways to Implement a Master Data Management System


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As companies leverage new technologies to gain insights about processes and operations, IT infrastructure, customers, vendors, and other stakeholders, they do it from two deliberate perspectives – analytical and operational. The analytics POV covers reporting and compliance aspects and optimizes partner and channel engagement. The operational POV assimilates the organization’s ‘best-version of truth’ via accurate and consistent data across the lines of business. Both of these perspectives pivot around efficient data management – a key to strategize business moves effectively.

In the digital day and age, Master Data Management (MDM) is no longer an additive. It is a crucial component for all enterprises, not only those who rely on inaccurate management reports or are awash in data deluge.

MDM allows companies to accelerate insights by delivering accurate, real-time views of critical master data, thereby increasing the innovation strike rate. Additionally, it increases revenue growth, improves productivity, customer satisfaction, and supply chain optimization.

In times of rapid growth and the resulting complexity, efficient MDM systems are sought after by organizations desirous to anchor decisions in high-quality managed information and accurate analytics. Amidst all these ‘data-fixing’ endeavors, often businesses – to their detriment – forget that implementing Master Data Management may be a technology decision, but its overall approach is a business one. Like any other enterprise-wide implementation, MDM, too, focuses on two approaches – the ‘rear-view’, and ‘windscreen view’ (the past and the future). So, enhancing past data purity is equally essential as altering business processes and reinventing organizational governance to ensure future data does not degrade.

A Deloitte study talks about how 86% companies surveyed for Data Management and Architecture (DM&A) strategy, state that they find it very challenging to leverage data to provide new products or services. 36% still rely on manual processes to support data management. In fact, assessing implementation failures across a decade indicates two root causes:

  • Relying only on technology and tools without aligning with business units.
  • Remediating current data messes without a future focus.

The key to an MDM implementation is approaching it like a business initiative and not just a project.
This post digs deeper into the critical steps that go into a successful implementation process and the best ways to embark on it. Be it assessing the current state of systems and architecture, aligning key stakeholders, fleshing out the critical communication strategy, or even, carving out an MDM target state, the implementation must be driven top-down by the Data function, which is part of the business organization, and not just IT.

Making Master Data Management a permanent part of an organization’s Data Strategy calls for a long-term commitment. The preparation for it, however, starts with answering the following question:

Identify the business problem

What is the final goal? Or what is the business challenge, if any? Is it improving data quality and productivity, optimizing the supply chain, unearthing up-sell and cross-sell opportunities, meeting compliance, achieving brand consistency, enhancing customer experience, reducing costs, or is it improving the overall capability of business decisions!? As granular understanding is the business driver, the deeper will be the implementation strategy’s focus on prioritizing and optimizing downstream decisions.

Having established the goal (or the problem to be remediated), it is time to:

Set the right context

This sits at the heart of MDM implementation. At this stage, the company’s core data assets are ascertained and defined. Next, the documenting and sharing protocols, appropriate regulatory constraints, standard definitions, and other export/import rules for datasets are established. Finally, plans are determined to enforce policies evenly across the enterprise, and a framework to resolve ambiguities and conflicts is agreed upon.

Engage business stakeholders

The next step is to engage business stakeholders by identifying key players and seeking their buy-in for scope and resource commitments. Business stakeholders can be segregated by subject areas or lines of business or even, application areas. As most mid-size MDM implementations span years, they necessitate the backing of an executive sponsor. It is this reason which makes an MDM implementation bigger than a project.

Once the guardrails are in place for the implementation to be embraced by the entire organization, the next stage involves:

Think through the different MDM implementation styles

The next dot to connect is to think through the different MDM implementation styles. The criterion of choosing specific implementation styles is an exhaustive subject by itself. Basis the business requirements and organization structure, there can be four common MDM implementation styles. Gartner summarizes them as consolidation, registry, centralized, and, ultimately, coexistence.

These styles support differing degrees to which master data is stored and governed centrally, or in a distributed fashion. Some are more invasive or disruptive than others in their impact on IT and business environments.

Establish prioritized business outcomes

Establishing prioritized business outcomes can be done by recognizing critical success factors and instituting a measurement and communication process. Typically, MDM implementations run across multiple phases, with each stage designed to achieve a business goal. The roadmap highlights concrete progress milestones (or needs course corrections) and keeps the stakeholders energized. So, be it improving data quality, or reducing average data delivery turnaround time, or decreasing operational costs; prioritizing business outcomes, relies on a robust governance model. The representative committee (usually the C-Suite and departmental heads) brings a process to resolve issues and oversees revisions in business needs, operations, and technology.

In conclusion, no organization starts a multi-year business and IT initiative without significant planning and preparation. As the organizations’ size varies, so does their process maturity, and growth trajectories. However, all MDM implementation journeys should start with a data strategy and consider the current state along with the expected business outcome.

Rajneesh Kumar
Rajneesh Kumar is Director, Marketing at Pimcore GmbH. He is developing the marketing plans. Operationally he is responsible for the marketing program and driving business outcomes. He builds data-driven decision-making frameworks – customer segmentation, Go to Market strategy, targeting, cross-sell/up–sell. Pimcore is an open-source platform for product information management (PIM/MDM), digital asset management (DAM), content management system (CMS), and eCommerce.


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