MDM Essential Driving Factors In Making An Organization Data Driven

MDM Essential Driving Factors In Making An Organization Data Driven

Master data management (MDM) technology is entering a new stage of maturity. Driven by the rising demands of Cloud and big data, today’s MDM is drastically different, offering greater capabilities, Hybrid deployment options, and more user flexibility than ever before.

The market forces that have shaped the MDM evolution are complex and include:

  • The need for companies to differentiate on customer experience
  • Increased regulations
  • Growth strategies driven by mergers and acquisitions, and more.

Most of us working in this area of information management know that MDM implementations have a high failure rate. According to a study, only 24% MDM programs end successfully. There are many reasons why these projects sink or don’t deliver what was initially promised. Poor management, lacks of organization support, unclear objectives are some of the common reasons to blame.

We have been helping customers with MDM implementation for almost a decade now. Keeping aside aforementioned reasons, my experience has shown me that there are two key organizational traits that trigger failure.

The number one reason is, the absence of a holistic approach to MDM. Many organizations think implementing MDM in a silo environment, purely as a technology endeavor is a good way to go. But as most of us know, this approach as often proved to be catastrophic.

Second important reason for MDM failure is the missing agility, the inability of organizations to adapt to the change. MDM requires a nimble methodology, which helps in looking at data quality issues not just as an afterthought, but also to fix the complications in an incremental and iterative manner.


As we enter this new era of MDM, where all your data is big data, three big problems emerge:

  • First, traditional match-and-merge alone no longer cuts it in this new world. Everything from web page clickstreams to social media activities to transactions and customer service interactions become fodder for customer intelligence—creating new requirements for data management capabilities.
  • Second, data insights must be served directly to business users across organizations. Data stewards can no longer be the gatekeepers of MDM tools. Therefore, MDM tools must expand back-end capabilities to scale and undergo a massive front-end makeover to become faster, easier, and more intuitive to use.
  • Third, MDM must support a hybrid world, providing the flexibility for customers to deploy on premise, in the cloud (including Hadoop), or in a hybrid deployment environment.


Reflecting a successful (if not occasionally bumpy) adaptation to complex market pressures, today’s MDM is both highly capable and much easier to use. Supporting our new data era, it provides a rich, comprehensive view of the customer, supplier, patient, citizen, product, asset, and dozens of other master data entities by combining traditional transactional data with a wide variety of other data. The key to MDM’s generational shift, though, lies in fostering data from inception to consumption—and improving that data along the way. These end-to-end data management capabilities make modern MDM invaluable.

Some of the most important end-to-end capabilities include:

  • Discover and Model—With so much data available from so many sources, MDM must discover untapped sources of relevant data, even when they are not obvious or easy to find. Similarly, it must infer and suggest the best format for managed attributes (e.g., recommend the optimal model for a customer record).
  • Cleanse and Enrich—Modern MDM comes with embedded data quality and data enrichment capabilities to improve the overall quality of data. It provides a seamless experience across the solution, checks for incomplete or invalid entries, and attempts to resolve conflicts. Having cleansed the data, it can now make the data more useful by adding missing information to the records, enriching them with information from other sources.
  • Match and Merge—While MDM has expanded beyond its humble beginnings, validating (match) and deduplicating (merge) data is still needed as much as ever before. Once the duplicated profiles are identified, companies also need a robust, flexible, reliable framework to adapt to new types and sources of data attributes when determining survivorship to build the gold (master) record.
  • Relate—Data is not an island. Its value is amplified exponentially when you capture its relationships to other data. For example, when a customer purchases a product, it’s useful to have a single view of that customer and that product.  But the value to the business is huge when you define a relationship between those records (customer and product). MDM delivers these relationship insights to the business. This is especially critical when managing customer data. Information such as whom the customer lives with or works for can be vital to understanding that customer and providing better recommendations and service.
  • Secure—MDM ensures that data conforms to business rules, which helps enforce regulatory compliance, as well as security and privacy
  • Deliver—The value of any master data is realized when that data is delivered to the right people, supporting the right processes, at the right time, and in the right context. MDM ensures that data gets delivered as needed to customers, employees, applications, and analytics systems throughout the organization.
  • Governance and Stewardship—MDM helps master data, turning it into an effective strategic asset. It allows companies to access the risk associated with that data, helps define policies, and helps monitor data quality. It supports effective stewardship of data by providing intuitive ways to evaluate, prioritize, and fix data issues.

In other words, MDM must be more user-friendly, adaptable, and scalable across organization and for any successful MDM implementation the process needs to holistic and agile as covered below


MDM implementations are unique in that they are not run in a silo setting delivering value to only a department or a small business area. MDM impacts large area of an organization. As you start consolidating master data records, which have multiple touch points and application specific usage, you soon will realize that you not only need technology, but also require changes to the way people and processes use master data. Being holistic is about being business driven.

Being holistic is about being business driven. Implementing a solution to address a specific problem occurring in a business function is a great way to start MDM. At the onset this might sound tactical, but the strategic aspects can happen in following ways and help you implement MDM holistically.

  1. As you plan to integrate master data from different sources, ensure you have done systematic data discovery process. This allows you cross check your data with your business rules and accordingly tune your transformation layer to ensure clean data gets loaded to MDM.
  2. Every entity and data element housed in MDM should go through strict standards and quality check process.
  3. Master data should be managed under your organization’s data governance umbrella with focus on stewardship, accountability and clearly defined roles and responsibilities.
  4. Different sources of master data such as order processing, customer service, billing etc. treat data in their own unique way resulting in lack of consistency. The data quality and integration effort should focus on applying standard rules to bring the data to a common, agreeable, enterprise standard structure.

Many organizations are still looking at MDM implementation from a purely infrastructure and technology standpoint. This has to stop and the only way to achieve that is to bring the business leaders to the table. As many of my blogger friends and analysts have been saying – always have the MDM owned by the business, backed by strong data governance practice with primary focus on improving the quality of master data.


Being agile is about doing things in small chunks (phases or sprints) with a vision on bigger picture. Sounds like a key missing aspect of an MDM implementation isn’t it?

In reality, agile implementation style offers more. Agile is about doing things quickly (and failing fast) so we learn from it. This works perfectly with the data management efforts where the chance of failures is high. You want to try different approaches, which are precise and short to see if they deliver expected result. And if you know that a particular approach is not a right option, you take a different route.

One of the examples we can provide you here is the de-duplication process which is a key feature of MDM. Although the matching & merging rules change across organization we implement MDM, I spend couple of weeks (a sprint) with customers to use out of the box rules delivered with our MDM product to identify and remove duplicated records. One key advantage of this is, these rules are tuned & enhanced with years of experience. Most of the time, these exercise prove to be very beneficial as the rules we are providing with product turn out to be right fit (with minor tweaks off course), many times much better than what clients tend to come up with on their own.

 Agile provides a lean approach to MDM implementations. It helps you achieve your stated enterprise master data management goals in a sustainable way. With this approach, you can greatly increase your chance of success.

 One of the things we don’t often hear is the aspect of technology being a reason for MDM program failure. Tools and technical acumen are sure necessary, but what matters the most is, how your organization approaches MDM culturally. Winning organizations succeed because of their disruptive thinking. They are agile to changes and challenge the norms, which help blur the organization boundaries. This is key aspect of agile and helps you succeed with MDM.