6 best practices for building an intelligent master data management strategy

Data has become a powerful commodity around which the business world operates, and decision-makers are not flinching from that anymore. Now, business strategies and processes are built on the foundations of reliable data, and their consistency cannot be fractured.

However, master data inconsistencies and redundancies are not uncommon for those who have been in the business world for even a year now. The impact can be felt significantly across the different levels of the applications and systems—customers, suppliers, enterprises, and product value chains. Today, the need of the hour is beyond just formulating a data strategy. There is an urgent need for an intelligent master data management (MDM) strategy.

While master data may be working wonders at the application level, it takes no blue-sky thinker to realize that the business management needs a more uniform and consistent data availability. Master data management (MDM) maintains the single version of truth of the core data across the enterprise. This centrally governed and trusted data is used by various business units, departments, and individuals for better decision making. Apart from decision-making, MDM also plays a key role in digital transformation initiatives.

Here are 6 key aspects that you should keep in mind while formulating an intelligent MDM strategy.

First, you must be realistic about your organization’s readiness while adopting an MDM solution. There are two ways of going about MDM implementation. You start and tackle all data entities in one go. The problem is that you do not know the direction, and it is bound to be a little scattered.

On the other hand, set your targets right and plan around that business requirement. This works against the traditional wholesale MDM implementation, which takes around 12-24 months. While there is nothing wrong with the conventional methodology, much data is going to either be obsolete or inconsistent at the end of implementation.

Instead, it would work best to take smaller goals and have a clear line-of-sight to the business benefits. Once you have clearly articulated MDM goals, you will get a clarity of which application and what master data needs your immediate attention. At that rate, the implementation of MDM can be done in shorter phases and set the stage for the next part of the implementation. Choose a business benefit, plan, execute, and then onto the next one.

Setting clear goals goes a long way toward a bigger picture. Not only does it help decide which initiatives need better master data, but it also helps to identify the right solution that is relevant to your MDM goals. Choosing a master data management solution based on the enablement and optimization of business processes will play an essential role in a holistic implementation.

While choosing the MDM solution, you must consider your organizational culture, data-driven maturity, a consensus of key stakeholders, and existing skill-sets for execution support. Knowing exactly which solutions are being impacted also helps track ROI at the end of an implementation phase.

The most important piece of the MDM puzzle is to eliminate data silos (inaccessible and unreadable data).