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 This creates problems of data silos, multiple versions of data, data inaccuracy or errors, and outdated data.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.
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.
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 it easier to on-board new products ahead of the competition, thus grabbing more revenue opportunities.
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 a unified semantic data model for all its primary master data objects. A well-managed maser data can significantly reduce the headache of the IT team to integrate new systems and the costs associated with organizational integration by removing organizational barriers that inhibit information reuse.
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 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 management (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 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.
Why MDM Implementation Styles are Required?
Master data management implementation styles are crucial for successful MDM solution deployment. They play a key role in architecting the MDM system, whether you are building component-based systems or using purchased as a platform.
Right MDM implementation style improves the quality of your master data and enhances the consistency and managed use of this information in what is often a complex and somewhat tangled environment. It also helps you support the operational environment/decision-making environment, push clean data back into existing systems, build service-oriented architecture (SOA) fabric, enable demographic distribution, and other unique MDM requirements.
Different implementation styles have come to the fore to address different business needs. There are four most common MDM implementation styles adopted (1) Consolidation Style, (2) Registry Style, (3) Coexistence Style, and (4) Centralized Style.
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 organizations may need to use MDM for customer data applications 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, such as:
- Improve data quality and accuracy for analytical and operational usages.
- Eliminate data silos amongst disparate databases and systems.
- Link and centralize customer information.
- Enhance data governance, stewardship, and security capabilities.
- Improve the effectiveness and agility of existing master data across departments.
- Provide engaging customer experience and create upsell/cross sell opportunities.
- Get deep business insights and speed up decision-making.
- Reduce operational cost and system cost.
However, MDM is a long-term commitment; organizations must use a best-of-breed solution that not just supports their main data domains but any number of data domains. So, organizations should choose a single-domain master data management solution where it makes sense, and if a single domain solution cannot meet requirements, they should consider multidomain MDM.
What is Multidomain MDM?
Multidomain MDM is concerned with managing master data across multiple domains. While some MDM functions and disciplines can and should be leveraged across multiple domains, some of those functions and disciplines within each domain are still specific enough or have distinct business requirements, so they need very specific management and implementation.
As MDM is applied across more domains, these functions and their associated tools and processes become more reusable or adaptable. Certain functions, such as data quality, are extremely broad. Multidomain MDM programs need effective engagement and collaboration between the business and IT departments.
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.
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 275 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.
How to Mitigate MDM Implementation Challenges?
MDM implementation demands a long-term vision, effective technical infrastructure for collaboration, and organizational preparedness. It would be best if you did the necessary groundwork to clearly define steps and ensure enterprise acceptance with cost management goals. Here are few tips you should consider to overcoming the probable hurdles.
- Define and clarify your business requirements and build an MDM business case with a vision.
- Focus on real business-oriented problems or scenarios.
- Set budget for the master data management implementation program.
- Design and implement an MDM solution in small steps.
- Align your MDM implementation architecture with your long-term information infrastructure strategy.
- Scale MDM within the organization across departments and observe the benefits.
- Create a long-term MDM roadmap and maximize its benefits.
Selection Criteria for Master Data Management Platform
It is important to keep in mind that your master data management platform 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.
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.
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