Have you heard of the term “master data management” (MDM) but aren’t sure what it is and how it’s useful? This FAQ sheet answers your questions about what MDM is, the role that data quality plays, and how to get started.
Master data is data that needs to be shared by multiple systems or business processes, and it is shared in a type of master data repository. This repository can include both analytical and reference data. Examples of master data include customer, product, asset, location, employee, organizational units, or chart of accounts data.
Master data management (MDM) is a concept where applications are used to house a comprehensive master data repository of all critical data, which acts as a common point of reference. The data is used to support business intelligence and create a reference that shows transactions and analysis. MDM itself isn’t a technology, however; it is an ongoing program that includes data quality, data intelligence, and data modeling, among other activities.
Download the tip sheet to learn more about master data management including:
Chances are, your organization has some disparate, overlapping systems that are inconsistent with one another. MDM is a way to share master data across these applications and maintain consistency. It can also:
Think of data quality as the atoms that comprise a life form—your MDM program. You won’t get much mileage from MDM if the data itself isn’t fit for purpose. Data cleansing and standardizing can prevent poor quality data from entering your system and help you avoid rework. In addition, concentrating on data quality first may change your MDM priorities by exposing underlying flaws and root causes. That’s why qata quality is an essential component of transmitting the value of MDM.
Single customer view (SCV) is one potential outcome of MDM. While MDM initiatives could apply to linking internal relationships, SCV is about providing a single, consistent perspective of the data pertaining to individual customers across your business. Stack solutions are typically used by large, enterprise-level data management initiatives, but for small or mid-size businesses (SMBs) there may be solutions that can be bought piecemeal and fit together, starting with data quality.
While comprehensive, stack solutions are traditionally purchased for enterprise-level data management, SMBs can find solutions that can be bought piecemeal and fit together, starting with data quality. As Bloor analyst Philip Howard notes, “In practice, we do not know of a single user of MDM that has not simultaneously or previously implemented data quality to support their master data.”1 You can start in small, focused steps, like finding solutions that will help with data cleansing and data profiling.