What is Master Data? (and what not?)
Master data represents the business objects that contain the most valuable, agreed-upon information shared across an organization. It gives context to business activities and transactions, answering questions like who, what, when, and how and expanding the ability to make sense of these activities through categorizations, groupings, and hierarchies.
It can cover relatively static reference data, transactional, unstructured, analytical, hierarchical, and metadata. Also, it is the primary focus of the Information Technology (IT) discipline of Master Data Management (MDM).
Organizations often view master data as non-transactional, but they may encounter gray areas where they consider specific transactional processes and operations as part of their master data. For example, information about customers, products, employees, materials, suppliers, and vendors can be included in master data. In rare cases, if this information exists only within transactional data like orders and receipts, without separate storage, organizations may consider it part of their master data.
The master data required for a logistics process is different from, for example, an accounting or medical process. Logistics mainly involves critical product data such as length, width, height, and weight, possibly supplemented with color, barcode, packaging specification, etc. It is essential to determine what the master data for your logistics process are, which data are of critical importance, and for optimal logistics handling.
What Is Master Data Management?
Master Data Management (MDM) is a method used to define and manage an organization’s critical data to provide, with data integration, a single point of reference. The mastered data may include reference data – the set of permissible values- and the analytical data supporting decision-making.
Challenges in Master Data Management
A Master Data Management tool can support master data management by removing duplicates, standardizing data (mass maintenance), and incorporating rules to eliminate incorrect data from entering the system to create an authoritative source of master data. Master data are the products, accounts, and parties for completing business transactions. The root cause problem stems from the business unit and product line segmentation, in which the same customer will be serviced by different product lines, with redundant data being entered about the customer (a.k.a. party in the customer role) and account to process the transaction. The redundancy of party and account data is compounded in the front-to-back office life cycle, where the single authoritative source for the party, account, and product data is needed but is often redundantly entered or augmented once again.
Master Data Management (Wikipedia) aims to provide processes for collecting, aggregating, matching, consolidating, quality-assuring, persisting, and distributing such data throughout an organization to ensure a common understanding, consistency, accuracy, and control in the ongoing maintenance and application use of this information.
The term recalls the concept of a master file from an earlier computing era.
Master Data Management is simply the complete management of master data. It includes the cleaning, governance, tracking, and controlling of all master data. Master Data Management consists of processes, disciplines, and technologies.
Master Data Management in Practice
It is a broad concept related to complete processes and business operations. In that broad perspective, Master Data Management is the entire policy aimed at visualizing and managing an organization’s critical data.
MDM collects all these master data in a file that can be made available to departments or officials when necessary. From the world of logistics and order processing, this definition can be translated into obtaining and managing critical product properties so that they become available in the supply chain when necessary.
An example use case: Master Data in Logistics
Every logistics process revolves around volumes, dimensions, and weights. The dimensions of your items determine the required storage space, and the weight determines the packaging and handling. To collect the master data of each article, some products can immediately collect the product dimensions accurately and efficiently and make them available in a WMS or ERP environment.
Other Types of (Master) Data and use-cases
To understand master data, it’s essential to look at other data in the enterprise that isn’t master data – but can sometimes look like it.
Transactional data describe business events, and it is responsible for generating the most significant volume of data in the enterprise. It resides in the CRM, ERP, SCM, or other systems.
Log data records events or takes snapshots of process states at moments. It is essential to system operational efficiencies and preventive maintenance applications. Most big data, such as sensor data, machine data, and change-in-state data, are examples of log data.
Metadata is data that describes other data; it is the underlying definition or description of data. Master data, reference data, and log data all have related metadata.
Big data has many different definitions, but the most common is from Gartner’s Doug Laney. He characterized “big data” by 3Vs: volume, variety, and velocity. It combines the four types of data: log data, transactional data, reference data, and master data.
MDM is necessary for an organization to realize its datasets’ revenue and profit potential fully, but it is not easy to implement. MDM first needs to become a permanent part of the data strategy for the company to have a long-term commitment. Next, it requires consistent governance and sponsorship from the top management, as well as persistent efforts from information stewards of IT/CDO departments and data stewards of business departments. The challenge of fixing existing data and system issues should not stop the adoption of MDM for an enterprise. Instead, applying MDM to the new data sources and new applications will lay the foundation to apply it successfully to the existing data and systems gradually.