Master Data Management Essentials You Can’t Afford to Get Wrong

Master Data Management is a complex concept. Gartner defines MDM as a “technology-enabled discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of the enterprise’s official shared master data assets.”

As global enterprises attempt to drive more business value from increasingly connected lifestyles, it’s becoming harder and harder for them to ignore the need for powerful data management solutions. 

A familiar example of this can be found with customer experiences in the telecom industry. 

Take for instance a consumer who’s been using the services from a leading telecommunication operator. Imagine if the service provider has saved a consumer’s details across different departmental databases (that do not interact with each other) leading to inconsistencies. Naturally, the consumer receives separate bills for each service. The effect of this data inconsistency comes to the surface when the customer success team sends the consumer promotional mails for services she’s already signed up for. This indicates only one thing – the operator’s data management is a mess! 

Similar stories can be found in enterprises around the world - lack of systems integration leads to data duplication, affecting the organization’s ability to serve customers efficiently.

This is where a solid Master Data Management (MDM) solution comes to the rescue; it manages the quality and consistency of data elements and helps remodel the data strategy required to accelerate business growth. Here’s a brief outline of MDM, that’ll come in handy for you.

So, What is Master Data Management?

Master Data Management is a complex concept. Gartner defines MDM as a “technology-enabled discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of the enterprise’s official shared master data assets.” 

Therefore, one thing is clear as-clear-as day, MDM involves both IT and business functions. 

From a business point of view, MDM refers to defining and managing an organization’s critical data assets and creating a single source of truth for all those assets. It encompasses both analytical data and reference data that aid decision making. 

From an IT perspective, MDM refers to a set of tools that can standardize data, eliminate duplicate records, and store them within a master file. However, it’s important to remember that not all data is master data. 

So, What is Master Data? 

Let’s take a stab at understanding the concept in the context of other kinds of data.

Unstructured data: It covers a whole range of data residing in disparate formats. This includes white papers, intranet repositories, emails, videos, etc. Also, data in the form of PDFs, product specifications, marketing collateral, etc., can be categorized as unstructured data.

Transactional data: Both monetary and non-monetary data from business activities are categorized as transactional data. Deliveries, sales reports, invoices, claims, issue tickets are some examples of transactional data. Unlike master data, transactional data is time-based and is often needed by other systems for analysis.

Metadata: Data about the data is referred to as metadata - it includes file specifications, tags, image names, etc. Metadata may either reside in a repository or remain uncategorized in XML documents, log files, reports, etc.

Hierarchical data: It defines the relationship between data points. Commonly, it’s a part of a separate system or descriptions of a company’s organizational structures or product information. 

Reference data: A special kind of master data that relates data to information beyond the bounds of the enterprise. It usually has a cross relationship with master or transactional data sets. 

Master data: Core data around organizational pillars such as customers, products, suppliers, locations, and assets all come under the umbrella of master data. This sort of information changes infrequently. An important point to note is that master data does not include transactional data but it does describe transactions.

Now that we have defined master data in the context of other enterprise data, let’s dive into the ideal MDM implementation practices.

Read Gartner’s Peer Insights and Make an Informed Choice About MDM!

Gartner’s “Peer Insights: Lessons learned in MDM implementation” is a document that lists the most significant learnings after analyzing 311 Peer reviews regarding MDM execution. From evaluating business needs, creation of a robust governance framework to developing internal resiliency—it throws light on the most pertinent aspects of MDM implementation. 

Download Gartner Report

 

Best Practices for Master Data Management (MDM) Implementation 

Executive sponsorship: Any MDM project will fall flat if it is an IT-only effort. Sure, it may create technical benefits, but without delivering business value MDM fails to deliver tactical efficiencies. As with any other enterprise-level project, ensuring executive sponsorship is the best way to align IT and business priorities.

Phased implementation: From developing business use cases to purchasing the right tools, any MDM implementation project should be deployed in a phased manner. Setting goals for each phase and prioritizing high-dependency workflows can also help streamline the project. For instance, the immediate business need could be to clean email data in order to drive an email campaign, and hence, this becomes a priority over the consolidation of contact numbers. 

Standardized semantics: The growing need to harness optimized analytics and reporting among enterprises, has led to the widespread integration of data from multiple sources to a central data warehouse. However, years of siloed functioning has also resulted in a business environment where terminology can often be confused in different contexts. For instance, think of the different ways in which the terms, ‘customer’ and ‘product’ could be used in different files. This inconsistency results in unreliable reports and organizational communication gaps. Therefore, semantics standardization should be made a priority at the outset of any implementation project. 

Stakeholder collaboration: Data quality management is a tricky competency. In order to get the best results, these specialists often work closely with key stakeholders from different teams – business, IT, vendors, and system integrators.

 Read Insight -  A Guide to Master Data Management Implementation Styles

 

While it’s great that you’re reading about the best practices for MDM implementation, it can be quite the task to put these insights to use - especially if your organization scores high on change resistance and your key stakeholders are always busy. That’s why you should always consider hiring an implementation partner to help you streamline workflows, establish realistic implementation goals and give you a clearly defined project road map. Coupled with disciplined effort, quick-adoption organizational culture, and well-established business objectives, your new MDM system should make it much easier to leverage marketing initiatives, improve data interoperability, and deliver richer business outcomes.

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Pimcore Strategic Partner
Shashin Shah
  • Chief Executive Officer
13 articles by this author

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