There are many exciting topics under the Data Management umbrella, but if I had to choose one that resonates with me the most, I’d go with Master Data Management (MDM). Why? Because MDM programs can enable any organization to realize the value of one the most crucial assets: data.
Master Data Management is a hot topic in the IT industry. However, many organizations are on the fence about implementing it due to the wide impact of changes and a relatively high rate of MDM project failures. Yet, despite this, developing a comprehensive MDM strategy should be a priority for modern, data-driven organizations.
In my two decades of work in the data management space, I’ve noticed that even though MDM has gained considerable traction, isn’t always well understood. In this post, I’ll share and hopefully debunk the most common myths that surround it.
But before diving in, let’s review the fundamentals of MDM.
Master Data Management is a framework that allows organizations to generate uniquely identifiable, business critical data. This data is often referred to as an “entity”. In essence, MDM makes corporate data an integrated, harmonious whole by continuously bringing together source data, assessing its quality and ironing out the inconsistencies to solve data-related business problems.
Now that we established what MDM is, let’s explore what it isn’t.
Too often, we see that MDM is perceived as a software solution, when it really is a framework. Unfortunately, no software can handle the entire MDM framework right out of the box. Many vendors will pitch their product as the ultimate, holistic system, but what they don’t tell you is that MDM software is just an accelerator.
Of course, there is an undisputed value in MDM software, especially when it comes to simplifying and expediting certain elements of the master data management program such as Identity Resolution, Automation, Survivorship, and Remediation. However, approaching vendors to find what is available on the market shouldn’t be the first step when planning an MDM program.
While tools can certainly help or hinder MDM efforts, a successful MDM implementation is not made or broken by a tool. The real key to effective MDM lays in identifying fundamental elements of the program and carefully designing the implementation roadmap. Planning early on will increase the probability of MDM implementation success and will help avoid unnecessary software spend.
Organizational silos and resulting data silos are generally not conducive to effective data management operations.
Let’s take a financial department of a large manufacturing enterprise. This department takes in information from a couple of different financial systems which leads to duplicated and inconsistent data. Organization’s CFO decides to run MDM solely for the finance department focusing specifically on the “client” entity. As a result, this newly implemented MDM solution generates unique client records within the financial systems. It all looks good until a client contacts the company to change his address. Although the change is promptly reflected in the CRM, the financial systems remain untouched. Even though the client received his product, the invoice never arrives since it was sent to the old address.
Successful MDM requires that we track our chosen data entity across the ENTIRE organization, without exceptions. If, as per above example, client data is in 80% of departments, then we need to incorporate ALL of them into the solution.
Because MDM is typically an enterprise scale project, it’s automatically associated with large investments and significant effort. As with any large-scale project, the strain on corporate resources is hard to predict and can go well beyond the initial estimates. In the presence of multiple risk factors (broad scope, high impact of changes, technology that’s new to the organization), MDM projects can be loaded with uncertainty. However, there are ways to mitigate the risks and pave way for success. One of the best ways to do it, is by phasing MDM implementation based on criticality of data entities and their prevalence across the organization. This approach helps companies validate the program with a small budget, while still offering value to the organization.
For example, rather than focusing on a major data entity that’s widespread across the organization, companies can drastically reduce the risk by starting with a “smaller” and less critical entity. For many organizations, “employee” is a good test entity to use as a proof of concept. Not only is it less prevalent across the enterprise than much more critical entities such as “customer” or “product”, but it also offers a good starting point for further program expansion.
A gradual, iterative approach to MDM alleviates the risk of failure, improves adoption and sets the program up for success when rolled out on a large scale.
In my experience, enterprises are very confident in Data Quality across their systems, until that’s put to the test. In most cases, Data Quality is lacking, and the organization is rarely fully aware of it. There are two main reasons why this happens:
Before embarking on an MDM implementation, it’s vital to address critical data discrepancies between existing systems. This can mean, for example, standardizing the way you specify state or province names across all systems (i.e. picking between an abbreviated name “AB” or a full name “Alberta”). Without proper Data Quality, records will remain separate and potentially create false-negative scenarios.
Assigning MDM implementation to IT might seem like an obvious choice. After all, the project will deal with both data and software systems. And while IT department is one of the major MDM stakeholders, and responsible for technical implementation, MDM is also about solving specific business problems. When IT is assigned to establish MDM, business tends to get less involved. This means that even though technology is handled, business needs may not be fully addressed. This can lead to low MDM adoption.
Due to wide scope and reach of MDM, the program should be centrally organized, overseen by a committee and implemented by a well represented, cross-functional team across the entire organization.
Each year, the amount of data enterprises gather and produce increases exponentially. But without a structured organizational approach to data management, not only does the data diminish in value but also poses liability risks when mishandled. MDM can do a lot more for an organization than just reduce these risks – it can actively help realize the full potential of data by establishing an enterprise-wide state of data clarity.
Curious about Master Data Management, but don’t quite know where to start? Contact us today and we’ll walk you through the steps for adapting the framework in your organization.