A Guide to Master Data Management Implementation Styles 

Master data is integral for the smooth running of core business processes and applications. High quality, reliable, up-to-date, and easily accessible master data enriches algorithmic processes for the uninterrupted functioning of operations and better business outcomes. If it is not managed and governed appropriately, organizations can suffer from lower operational efficiency, unsatisfactory customer experience, and higher IT costs.

Master Data Without Proper Management


The volume and variety of enterprise-wide master data is growing at an unprecedented rate. The biggest challenge for enterprises is to create a uniform set of identifiers and a standard set of attributes (extended one, as well) for its core entities customers, products, suppliers, employees, hierarchies, and more, especially when their different applications and systems have been developed and deployed in silos. This creates problems of data silos, multiple versions of data, data inaccuracy or errors, and outdated data. So, it becomes tough to know which elements of your data you can trust, which you cannot.

Here, the management of master data has little to do with just technology more about strategy and implementation. The essential factor is organizational commitment and skills to ensure that their master data always remains well-managed and up-to-date for higher business growth.


How a Master Data Management Platform Helps Your Business

 

A master data management platform enables you to consolidate, streamline, and distribute data across all your systems such as ERP, CRM, Apps/Systems, eCommerce, and more. It provides a single standard view of your master data. Enterprises that implement an MDM platform in aligned with a well-thought-out strategy and vision can gain significant business values. Key benefits of Master Ddata Management include:

 

Agility

Master data is core to all business decision-making. When your all master data and hierarchy data is clean, trusted, and up-to-date across business intelligence and analytical systems, it provides better agility in operation and greater accuracy in reporting. So, you can make smarter and instant decisions and improve the responsiveness of your business. A flexible master data management solution can significantly reduce transactions that suffer from excessive IT and business costs.

Growth

To accelerate business growth, MDM has a vital role to play. When you have high-integrity customer data, you can improve your sales, service, and marketing initiatives. By turning your master data in a single trusted view (customer, product, and service) help you improve your ability to understand customer needs precisely and try to meet them upfront. When your master data is truly streamlined across the supply chain, it makes easier to on-board new products ahead of the competition, thus grabbing more revenue opportunity.

Transformation

Any enterprise strategy— seeking to transform their business with the impetus of digital technology such as opening up new channels, expanding customer touch-points, entering into new markets or innovating customer experience— demands unified semantic data model for all its primary master data objects. A well-managed maser data can significantly reduce the headache of IT team to integrate new systems and the costs associated with organizational integration by removing organizational barriers that inhibit information reuse.

Master Data Management Use Cases

With master data management, a robust foundation for data modeling as per organizational perspective, and improved data governance for data reuse and sharing can be laid out that can explicitly accelerate your business strategy. Every industry implements an MDM platform as per their unique requirement. Some general-purpose MDM tools might not serve the specific purpose, so enterprises need a specialized one. There are many use cases of master data management and any use case majorly depends upon the criticality of business needs. Amongst several MDM use cases, the two most common are operational MDM and analytical MDM.

Operational MDM

Operational MDM is about establishing MDM at its source. So, master data is managed and governed at the point where the enterprise recognizes it. All the efforts are made to make sure that data consistency remains throughout the enterprise to ensure the integrity of the business process. In this segment, two discrete areas have emerged that focus on specific data domains. One is MDM for product data, or also called product data information (PIM), and the other is MDM for customer data. Currently, these two segments have quite evolved. In fact, they are now being addressed by a mix of single-domain-centric offerings and multidomain MDM offerings. Operational MDM's focus is on all-consuming systems, applications, and purposes.

Analytical MDM 

Analytical MDM is more about establishing MDM skills, tools, and technologies, like data cleaning and data quality. It refers to data accessibility that is utilized for business intelligence, reporting, and analytics. Unlike operational MDM, it does not mandate to fix the data at its source despite having all data at one place; in fact, it is used to measure the business. Analytical MDM's focus is on all downstream BI requirements. It is deployed downstream of the transaction/operational systems and is part of the BI implementations.

MDM Implementation Style Considerations

Master data management implementation is a tricky one. Sometimes, it becomes difficult for organizations to decide which MDM approach to adopt. Some organizations need to use MDM for product information management to support their global product data synchronization or supply chain management. Other organization may need to use MDM for customer data application to support customer-centric objectives. So, it all depends upon different business requirements that businesses have to get a single view of their master data.

However, MDM is a long-term commitment; organizations must use a best-of-breed solution that not just support their main data domains, but any number of data domains. So, organizations should choose single-domain MDM solution where it makes sense, and, if a single domain solution cannot meet requirements, they should consider multidomain MDM.

Gartner’s Implementation Styles of MDM

Master data can be stored in several ways and implemented in a range of styles. There are four master data management (MDM) implementation styles, and their different characteristics suit different organizational needs.

These include consolidation, registry, centralized and, ultimately, coexistence. These styles support differing degrees to which master data is stored and governed centrally, or in a distributed fashion. Some are more invasive or disruptive than others in their impact on IT and business environments.

(1) Consolidation Style
Used primarily to support business intelligence (BI) or data warehousing initiatives. This is generally referred to as a downstream MDM style, in that MDM is applied downstream of the operational systems where master data is originally created.

(2) Registry Style
Used primarily as an index to master data that is authored in a distributed fashion and remains fragmented across distributed systems.

(3) Coexistence Style
Used primarily where master data authoring is distributed, but a "golden copy" is maintained centrally in a hub. The central system publishes the golden copy master data to subscribing systems.

(4) Centralized Style
Used where master data is authored, stored, and accessed from one or more MDM hubs, either in a workflow or a transaction use case.

Selection Criteria for Master Data Management Platform

It is important to keep in mind that your master data management solution should support comprehensive implementation and end-user experience across domains, use cases, and implementation styles. While selecting an MDM solution, understand which business initiatives require better master data to succeed, such as:

  • Eradicate silos of master data
  • Improve agility and operational efficiency
  • Enhance collaboration and business process integrity
  • Innovate with customer experience
  • Automate workflows or business processes
  • Mitigate data related risks

Along with that, examine the architectural role that each implemented MDM solution will play in your approach to enterprise information management. Take into account your hands-on experience where you struggled while managing master data of different fragmented data domains. Utilize that experience and avert any type of confusion and hype related to MDM to ensure that you get the appropriate level of a technical solution that serves your purpose without costing you huge money.

Open Source Master Data Management

Pimcore MDM is the most powerful open-source master data management platform. It enables you to manage every aspect of each master record (products, vendors, customers, etc.) including hierarchy, structure, validations, versioning and enriching master data with attributes, descriptions, translations, documentation, and other related data components. Pimcore open source MDM comes with free license permits, unlimited languages, channels, and users.

PIM - MDM Challenges - Pimcore
“Whitepaper - 8 Master Data and Product Information Management Challenges You Can’t Ignore!”
Read this whitepaper, as it factors in some critical issues during PIM-MDM implementation and how to iron them out to suit the enterprise purpose. Here’s a list:
Table of Contents

 

  • How to use data from an ERP system?
  • How to ensure consistency and uniformity of data in external appearance and communication, efficiently and effectively?
  • How to create a single place for data, offer a consistent experience to customers on any device and any channel?
  • How to manage and get insights on data quality. Also, how to make the process of data quality improvement more efficient?
  • How to ensure that the data meets the laws and regulations?
  • How to distribute data to the customers?
  • How to create magazines, leaflets and brochures, quickly and inexpensively?
  • How to create an API driven IT architecture? And why should it be done?

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