Master Data Management Definitions: The A-Z of MDM. Part 1

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This guest blog post is written by Justine Aa. Rodian of Stibo Systems. The post is part 1 in a series of 3. Please stay tuned for part 2 and 3.

Master Data Management can seem complicated to understand and talk about. There are so many abbreviations, so much puzzling lingo. This blog post breaks down the most commonly used MDM terms and define them so that even your mum understands. Get your MDM definitions straight in this A-Z of Master Data Management.

A

Analytics. The discovery of meaningful patterns in data. For businesses, data analytics are used to gain insight and thereby optimize processes and business strategies. Master Data Management can support analytics by providing organized master data as the basis of the analysis or link trusted master data to new types of information output from analytics.

Application Data Management (ADM). The management and governance of the application data required to operate a specific business application. ADM performs a similar role to MDM, but on a much smaller scale as it only enables the management of data used by a single application.

Application Programming Interface (API). An integrated part of most software, such as applications and operating systems, that allows one piece of software to interact with other types of software. In Master Data Management, not all functions can necessarily be handled in the software platform itself. For instance, you want to be able to deliver or receive data to or from external systems and applications. By using APIs built into the software, you can do that and thereby expand the functionality of your MDM solution.

Assets. In the MDM lingo, an asset can be understood in slightly different ways. There’s the term “data as an asset,” where asset is defined as something that can be “owned” or “controlled” to produce value. Here we talk about a way of perceiving something as an asset. But, when you hear about asset management and enterprise assets in conjunction with MDM, an asset is a more tangible thing of which the management can be optimized. Assets can be physical (people, buildings, parts, computers) and digital (data, images).

Architecture. An MDM solution is not just something you buy, then start to use. It needs to be fitted into your specific enterprise setup and integrated with the overall enterprise architecture and infrastructure, which is why MDM architecture is required as one of the first steps in an MDM process.

Attributes. In MDM, an attribute is a specification or characteristic that helps define an entity. For instance, a product can have several attributes, such as color, material, size and components. MDM supports the management of product data, including related attribute data.

B

Business Intelligence (BI). Business Intelligence is a type of analytics. It entails strategies and technologies that help organizations understand their operations, customers, financials, product performance and a number of other key business measurements. MDM supports BI efforts by feeding the BI solution with trusted master data.

Big Data. Large or complex data sets that make traditional data processing tools inadequate. Big data is characterized by the three Vs: Volume (a lot of data), Velocity (data created with high speed) and Variety (data comes in many forms and ranges). The purpose of using Big Data technologies is to capture the data and turn it into actionable insights. The information gathered from Big Data analytics can be linked to your master data and thereby provide new levels of insights.

Bill Of Materials (BOM). A list of the parts or components that are required to build a product.

B2B, B2C, B2B2C. Whether you operate as a Business-to-Business company, Business-to-Consumer company or any combination, Master Data Management can be applicable if you deal with large amounts of master data about, for instance, products, customers, assets, locations or employees.

Business rules. Business rules are conditions or actions set up in your MDM solution that allow for the modification of your data. According to your business rules, you can determine how your data is organized, categorized, enriched and managed. Business rules are typically used in workflows.

C

Customer Data Integration (CDI). The process of combining customer information acquired from internal and external sources to generate a consolidated customer view. CDI is often considered a subset of MDM for customer data.

Customer Data Platform (CDP). A marketing system that unifies a company’s customer data from marketing and other channels to optimize the timing and targeting of messages and offers. An MDM platform supports a CDP by linking the CDP data to other master data, such as product and supplier data, maximizing the potential of the data.

Change Management. The preparation and support of individuals, teams and organizations in making organizational change. A necessity in any MDM implementation if you want to maximize the ROI, as it is very much about changing processes and mindsets.

Cleansing. As in data cleansing. The process of identifying, removing and/or correcting inaccurate data records (e.g., by deduplicating data). Data cleansing eliminates the problems of useless data to ensure quality and consistency throughout the enterprise, and is an integral process of any decent Master Data Management process.

Cloud. MDM solutions come in many variations, and a central question of today is whether to host it on-premises or in the cloud (or a mix, called a hybrid). Cloud MDM is slowly on the rise, and many vendors offer the possibility to host in the cloud, but still the majority of companies choose an on-premise solution due to security concerns. With a hosted cloud solution, typically run on Amazon’s Web Services, Microsoft’s Azure or Google Cloud, organizations don’t have to install, configure, maintain and host the hardware and software. It is outsourced to a third party and typically offered as a subscription service.

Communication. Is something you don’t want to forget in the implementation of an MDM solution. It’s important that the whole company is made aware of what MDM is, what value it brings, and what it means for everyone on a daily basis. That’s the only way people will commit to it.

Contextual. As in contextual Master Data Management. Sometimes known under the name situational MDM (ref. Gartner Hype Cycle). It refers to the management of changeable master data as opposed to traditional, more static, master data. As products and services get more complex and personalized, so does the data, making the management of it equally complex. The dynamic and contextual Master Data Management is forecast to be one of the next big hypes in the MDM world.

Learn more here.

Customer Relationship Management (CRM). A system that can help businesses manage business relationships and the data and information associated with them. For smaller businesses a CRM system can be enough to manage the complexity of customer data, but in most cases organizations have several CRM systems used to various degrees and with various purposes. For instance, the sales and marketing organization will often use one system, the financial department another, and perhaps procurement a third. MDM can provide the critical link between these systems. It does not replace CRM systems but supports and optimizes the use of them.

Customer Master Data Management. Also sometimes referred to as MDM of customer data. The aim is to get one single and accurate set of data on each of your business customers—the so-called 360-degree customer view—across systems, locations and more, in order to create the best possible customer experience and optimize processes.

Learn more here.

D

Digital Asset Management (DAM). The business management of digital assets, most often images, videos, digital files and their metadata. Many businesses have a standalone or home-grown DAM solution, inhibiting the efficiency of the data flow and thereby delaying processes, such as on-boarding new products into an e-commerce site. MDM lets you handle your digital assets more efficiently and connects it to other data. DAM can be a prebuilt function in some MDM solutions.

Data. Data is a computing term to describe the characters, symbols, numbers and media that a computer system is storing. Data is unprocessed information.

Deduplication. The process of eliminating redundant data in a data set, by identifying and removing extra copies of the same data, leaving only one high-quality data set to be stored. Data duplicates are a common business problem, causing wasted resources and leading to bad customer experiences. When implementing a Master Data Management solution, thorough deduplication is a crucial part of the process.

Domain. In the MDM world a domain is understood as one of several areas in which your business can benefit from data management, for example within the product data domain, customer data domain, supplier data domain, etc.

Digital Transformation. (or Digital Disruption). Refers to the changes associated with the use of digital technology in all aspects of human society. For businesses, a central aspect of Digital Transformation is the “always-online” consumer, forcing organizations to change their business strategy and thinking in order to deliver excellent customer experiences. Digital Transformation also has major impact on efficiency and workflows (e.g., the so-called Fourth Industrial Revolution driven by automation and data, also known as Industry 4.0). MDM can play a crucial role in driving digital transformations, as the backbone of these are data.

D-U-N-S. Data Universal Numbering System. A D-U-N-S number is a unique nine-digit identifier for each single business entity, provided by Dun & Bradstreet. The system is widely used as a standard business identifier. A decent MDM solution will be able to support the use of D-U-N-S by providing an integration between the two systems.

If you’d like the whole A-Z e-book in a downloadable format, please find it here.

Justine Aagaard Rodian is a marketing specialist at Stibo Systems with a background as a journalist. Five years in the data management industry has armed Justine with unique insights and she is now using her storytelling and digital skills to spread valuable business knowledge about Master Data Management and related topics.

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