What are 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 as well as 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. It is the primary focus of the Information Technology (IT) discipline of Master Data Management (MDM).
Master data is usually non-transactional in nature, but in some cases gray areas exist where transactional processes and operations may be considered master data by an organization. For example, master data may contain information about customers, products, employees, materials, suppliers, and vendors. Though rare, if that information is only contained within transactional data such as orders and receipts and is not housed separately, it may be considered master data.
The master data required for a logistics process is different from, for example, an accounting or medical process. In logistics, it is mainly about the critical product data such as length, width, height and weight, possibly supplemented with color, barcode, packaging specification, etc. It is important 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?
In business, Master Data Management (MDM) is a method used to define and manage the critical data of an organization to provide, with data integration, a single point of reference. The data that is mastered may include reference data – the set of permissible values, and the analytical data that supports decision making.
A Master Data Management tool can be used to support master data management by removing duplicates, standardizing data (mass maintenance) and incorporating rules to eliminate incorrect data from entering the system in order to create an authoritative source of master data. Master data are the products, accounts and parties for which the business transactions are completed. The root cause problem stems from 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 role of customer) and account in order to process the transaction. The redundancy of party and account data is compounded in the front to back office life cycle, where the authoritative single source for the party, account and product data is needed but is often once again redundantly entered or augmented.
Master Data Management has the objective of providing 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 control of all master data. Master Data Management consists of a set of processes, disciplines and technologies.
It is a broad concept that can relate to complete processes and business operations. In that broad perspective, Master Data Management is the entire policy aimed at visualizing and managing the critical data of an organization.
MDM is a method of collecting all these master data in a file which can be made available to departments or officials when this is necessary. From the world of logistics and order processing, this definition can be translated into obtaining and managing the important 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 in an accurate and efficient manner and make them available in a WMS or ERP environment.
Other Types of (Master) Data and use-cases
To really understand master data, it’s important to take a 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 largest 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 in time. It is extremely important 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. Quite simply, it is the combination of the previous four types of data: log data, transactional data, reference data, and master data.
MDM has become a necessity for an organization to fully realize its datasets’ revenue and profit potential, but it is not easy to implement. MDM first needs to become a permanent part of the data strategy in order 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 gradually apply it successfully to the existing data and systems.