Enterprise Asset Management (EAM). The management of the assets of an organization (e.g., equipment and facilities).
Enterprise Resource Planning (ERP). Refers to enterprise systems and software used to manage day-to-day business activities, such as accounting, procurement, project management, inventory, sales, etc. Many businesses have several ERP systems, each managing data about products, locations or assets, for example. A comprehensive MDM solution complements an ERP by ensuring that the data from each of the data domains used by the ERP is accurate, up-to-date and synchronized across the multiple ERP instances.
Enrichment. Data enrichment refers to processes used to enhance, refine or otherwise improve raw data. In the world of MDM, enriching your master data can happen by including third-party data to get a more complete view, for example, such as adding social data to your customer master data. MDM eliminates manual product enrichment processes and replaces them with custom workflows, business rules and automation.
Entity. A classification of objects of interest to the enterprise (e.g., people, places, things, concepts and events).
ETL. Extract, Transform and Load. A process in data warehousing, responsible for pulling data out of source systems and placing it into a data warehouse.
Golden Record. In the MDM world, also sometimes referred to as “the single version of the truth.” This is the state you want your master data to be in and what every MDM solution is working toward creating: the most pure, complete, trustable data record possible.
Governance. Data Governance is a collection of practices and processes aiming to create and maintain a healthy organizational data framework, by establishing processes that ensure that data is formally managed throughout the enterprise. It can include creating policies and processes around version control, approvals, etc., to maintain the accuracy and accountability of the organizational information. Data governance is as such not a technical discipline but an indispensable discipline of a modern organization—and a fundamental supplement to any data management initiative.
GS1. Global Standards One. The GS1 standards are unique identification codes used by more than one million companies worldwide. The standards aim to create a common foundation for businesses when identifying and sharing vital information about products, locations, assets and more. The most recognizable GS1 standards are the barcode and the radio-frequency identification (RFID) tags. An MDM solution will support and integrate the GS1 standards across industries.
Hierarchy Management. An essential aspect of MDM that allows users to productively manage complex hierarchies spread over one or more domains and change them into a formal structure that can be used throughout the enterprise. Products, customers and organizational structures are all examples of domains where a hierarchy structure can be beneficial (e.g., in defining the hierarchical structure of a household in relation to a customer data record).
Hub. A data hub or an enterprise data hub (EDH) is a database that is populated with data from one or more sources and from which data is taken to one or more destinations. An MDM system is an example of a data hub, and therefore sometimes goes under the name Master Data Management hub.
Identity resolution. A data management process where an individual is identified from disparate data sets and databases to resolve their identity. This process relates to Customer Master Data Management.
Information. Information is the output of data that has been analyzed and/or processed in any manner.
Learn more about the difference between data and information here.
Integration. One of the biggest advantages of an MDM solution is its ability to integrate with various systems and link all of the data held in each of them to each other. A system integrator will often be brought on board to provide the implementation services.
Internet of Things (IoT). Internet of Things is the network of physical devices embedded with connectivity technology that enables these “things” to connect and exchange data. IoT technology represents a huge opportunity—and challenge—for organizations across industries as they can access new levels of data. A Master Data Management solution supports IoT initiatives by, for example, linking trusted master data to IoT-generated data as well as supporting a data governance framework for IoT data.
Learn more about the link between IoT and MDM here.
Lake. A data lake is a place to store your data, usually in its raw form without changing it. The idea of the data lake is to provide a place for the unaltered data in its native format until it’s needed. Why? Certain business disciplines such as advanced analytics depend on detailed source data. A data lake is the opposite of a data warehouse, but often the data lake will be an addition to a data warehouse.
Location data. Data about locations. Solutions that add location data management to the mix, such as Location Master Data Management, are on the rise. Effectively linking location data to other master data such as product data, supplier data, asset data or customer data can give you a more complete picture and enhance processes and customer experiences.
Maintenance. In order for any data management investment to continue delivering value, you need to maintain every aspect of a data record, including hierarchy, structure, validations, approvals and versioning, as well as master data attributes, descriptions, documentation and other related data components. Master data maintenance is often enabled by automated workflows, such as pushing out notifications to data stewards when there’s a need for a manual action. Maintenance is an unavoidable and ongoing process of any MDM implementation.
Modelling. Modelling in Master Data Management is a process in the beginning of an MDM implementation where you accurately map and define the relationship between the core enterprise entities (e.g., your products and their attributes). Based on that you create the optimal master data model that best fits your organizational setup.
Matching (and linking and merging). Key functionalities in a Customer Master Data Management solution with the purpose of identifying and handling duplicates to achieve a Golden Record. The matching algorithm constantly analyzes or matches the source records to determine which represent the same individual or organization. While the linking functionality persists all the source records and link them to the Golden Record, the merging functionality selects a survivor and non-survivor. The Golden Record is based only on the survivor. The non-survivor is deleted from the system.
Multidomain. A multidomain Master Data Management solution masters the data of several enterprise domains, such as product and supplier domain, or customer and product domain or any combination handling more than one domain.
Metadata management. The management of data about data. Metadata Management helps an organization understand the what, where, why, when and how of its data: where is it coming from and what meaning does it have? Key functionalities of Metadata Management solutions are metadata capture and storage, metadata integration and publication as well as metadata management and governance. While Metadata Management and Master Data Management systems intersect, they provide two different frameworks for solving data problems such as data quality and data governance.
New Product Development (NPD). A discipline in Product Lifecycle Management (PLM) that aims to support the management of introducing a new product line or assortment, from idea to launch, including its ideation, research, creation, testing, updating and marketing.
Omnichannel. A term mostly used in retail to describe the creation of integrated, seamless customer experiences across all customer touchpoints. If you offer an omnichannel customer experience, your customers will meet the same service, offers, product information and more, no matter where they interact with your brand (e.g., in-store, on social media, via email, customer service, etc.). The term stems from the Latin word omni, meaning everything or everywhere, and it has surpassed similar terms such as multi-channel and cross-channel that do not necessarily comprise all channels.
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.