Today, most organizations have several separate applications and systems where data crossing organizational divisions or departments can be duplicated, fragmented, or outdated. When that is the case, answering basic but critical questions about individual performance KPIs or metrics for an enterprise becomes a challenge. In this article, we provide answers to the benefits of master data management and how this has helped organizations address their data issues.
Some of the questions one may ask that can prove difficult to answer accurately may include;
- What is the size of my workforce?
- Who are my most profitable clients?
- What products have the best margins?
Indeed, access to accurate, timely information is a necessity. However, as sources of data increase, keeping data definitions up to date and managing it to ensure all parts of your entity use the same details becomes an uphill task going forward. For that reason, organizations seeking to address these issues turn to master data management (MDM). But before delving into that, you need to know what master data is all about.
What is Master Data?
Often known as the “golden record” of information in a data domain, master data corresponds to the firm that is the subject of the data that one is mastering. Also, note that data domains vary from one sector to the other. For instance, common data domains for manufacturers include materials, customers, suppliers, as well as products.
In the case of insurers, a data domain may include providers of medical insurance, members, products, and claims. On the other hand, banks may prioritize financial products, clients, and accounts. Supplies, patients, as well as equipment represent the applicable data domains for healthcare establishments. Meanwhile, reference data consists of order status entries, codes for states and countries, currencies, and other generic values.
The transactions that one processes in different data domains are not part of master data. On the contrary, master data functions as a master file of product specifications, dates, item numbers, names, customer IDs, addresses, as well as attributes that one can use in transaction processing analytics applications and systems.
What is Master Data Management?
The idea behind master data management is to bring accuracy, consistency, and order to master data. The concept can be challenging and complex to some extent. For example, there are thousands of corresponding suppliers and more than 367,000 parts in one 737 aircraft. In that case, most suppliers may manufacture the same part but end up giving it a different name.
In turn, that increases the complexity for supply chain and procurement planners. That is where MDM comes in handy. It creates common data across all stakeholders to foster consistency, accuracy, and streamlines processes.
There are two forms of master data management that you can implement separately or in tandem. These are;
- Operational MDM – Focuses on the master data in core business systems.
- Analytical MDM – Aims to feed consistent master data to data warehouses and other analytics systems.
Both operational MDM and analytical MDM provide a systematic approach to managing master data. Deployment of a centralized MDM hub where one stores and maintains the master data is what enables the systematic approach. As much as that is the case, there are different ways to architect MDM systems.
The determining factor here will be how entities want to structure their master data management programs and the connections between source systems and the MDM hub. The fundamental MDM architectural styles include;
1. Transaction Architecture
Also known as a centralized architecture, this approach dictates all management and updating of master data to the MDM hub. This is responsible for publishing data changes to each source system. Transactional architecture is the most intrusive style of MDM from an organizational standpoint. Since transactional architecture tends toward complete centralization, it yields the highest level of enterprise control.
2. Registry Architecture
It creates a unified index of master data for analytical uses without changing the information in individual source systems. Registry architecture is the most lightweight MDM architecture. The approach uses data cleansing and matching tools to identify duplicate data entries in different systems for cross-referencing in the registry.
3. Coexistence Style
It creates a consolidated set of master data in the MDM hub. But changes to the master data in individual source systems happen in the hub. After that, the next step is propagating the master data to other systems to ensure that all use the same data. Doing so offers a balance between centralized governance and system-level management of master data.
4. Consolidation Approach
Involves the pulling of sets of master data from different source systems and consolidating the data in the MDM hub. The result is a centralized repository of consistent master data for use in BI, analytics, and enterprise reporting. Note that operating systems continue to use their master data to process transactions.
Disciplines of a Strong MDM Program
- Technology – refers to the data hub and any enabling technology.
- Governance – entails directives that manage the organizational qualities, bodies, principles, and policies, which promote access to accurate and certified master data. It is the process through which a cross-functional team defines various aspects of the MDM program.
- Process – focuses on defined processes across the data lifecycle responsible for managing master data.
- Measurement – reviews your progress in line with the goals you have in place. Measurement should prioritize continuous improvement and data quality.
- Organization – the focus here is getting the right people in place throughout the MDM program. That includes those participating in governance, master data owners, and data stewards.
MDM Best Practices
Consider the Pros and Cons of AI/Machine Learning
Such techniques like probabilistic matching are ideal for large and varied datasets and lend themselves to advanced algorithms like machine learning. As such, most MDM vendors position their ML and AI capabilities as the only scalable option for mastering complex modern datasets.
Appreciate That MDM Goes Beyond Technology
Organizational commitment to change and executive sponsorship is a necessity for effective and sustainable data-mastering initiatives. Additionally, organizational politics, inertia, and culture demand careful navigation beforehand, and MDM can make all that achievable.
Be Careful with Record-Matching Methods
When matching record values, you can use a deterministic, heuristic, probabilistic approach, or a mixture of the three depending on your vendor-specific capabilities and dataset characteristics. Also, note that appropriate design and configuration of probabilistic approaches allow them to accommodate more varieties of data more flexibly.
Right-Size Your Data
Realizing success through MDM requires you to use smaller datasets. You can achieve that by consolidating records and eliminating silos with data migration efforts like migration from mainframe to cloud applications.
Opt for a Modular Approach
To realize “quick wins,” you can start by applying MDM to more stable, smaller, and simpler datasets. For instance, applying MDM to a product information system first allows your team to demonstrate early results. That, in turn, can win the support and funding for dynamic and complex initiatives like CRM.
You may imagine that MDM is a technological issue. However, you need to understand that a purely technological solution without corresponding changes to business controls and processes may not yield a desirable outcome. The details herein confirm that adopting MDM can help your company achieve much more than what you can expect from conventional business applications and systems.