6 best practices for building an intelligent master data management strategy

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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.

Setting the MDM Strategy Game for 2020

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.

  • Assess Your Organizational Goals

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.

  • Evaluate MDM Enabling Technology

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.

  • Get the Right Stakeholders on Board

The most important piece of the MDM puzzle is to eliminate data silos (inaccessible and unreadable data). As a result, the stakeholders—employees, clients, and third-party vendors—will be able to navigate a streamlined and structured data to achieve business goals.

One of the most common reasons for an MDM strategy backfiring is a misaligned team clouded by the lack of clarity pertaining to data domains. The appropriate stakeholders must be explained about the requirements and quality of data going into the task at hand. Once the teams and people directly associated with the data are aligned with the end business objectives and the MDM strategy, the rest works like magic.

  • Keep Architectural Consistency in Mind

To ensure that your MDM strategy brings out the best results for your business initiatives and goals, it is essential to follow a robust architecture. From registry and consolidation to coexistence and transaction, evaluate all architecture styles carefully. It is also vital to identify the role that every implemented MDM solution plays within that architecture on the way to achieving an efficient enterprise information management (EIM).

Treat this as an opportunity to establish sound information architecture fundamentals, such as canonical transaction formats for master data domains as part of a well-managed data integration practice. Furthermore, MDM technology should seamlessly blend with the overall enterprise IT architecture while setting a precedent for bringing in best practices pertaining to information architecture.

  • Take Cues From Your Past Experiences

Leveraging previous experiences in newer initiatives can give you a head start. Bring into the picture some of the most reused data domains that, despite being fragmented, have brought desirable results. For starters, your MDM strategy could imply a way to defragment the data domains, giving place for newly managed master data.

Once this newly managed master data is exposed to your analytics platform, it can help streamline your data management efforts to the ultimate goal. Working with frequently accessed data entities that are most likely to deliver results early also helps you build confidence for the MDM initiative.

  • Don’t Confuse MDM with ADM (Application Data Management)

One of the most important parts of streamlining the journey is to know how different data entities are used. A common mistake that most MDM strategies fall prey to is the lack of distinction between master data and application data. At any given point, you must be able to classify application data into those that fit the bill of master data and those that do not. Ask this question - are the data entities being accessed across the organization, or is their usage limited to a particular application?

With the right classification, the business can focus on the least amount of data governance on the least amount of data that has the most significant impact on business outcomes.

As much of a change that your MDM can bring, there is a bit of a reality check to it—without the right amount of discipline and technology upgrade, MDM is no magic wand. To achieve the best results, it is vital to follow best practices across all levels of stakeholders.

In conclusion, an organization needs to play prudently while tying the tightrope between data-related targets and goals. The rope called ‘strategy’ has the potential to be free from inconsistencies and redundancies over time. An intelligent MDM strategy followed to the T can be a critical factor in deciding an organization’s master data standpoint.

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