Written by Sohaiyla Khalili
What is Master Data Management (MDM)? MDM is used to define and manage an organization’s critical data to provide, through data integration, a single point of reference. In many businesses today, especially global enterprises with hundreds of separate applications and systems (i.e. ERP, CRM), data can easily become fragmented, duplicated, and out-of-date. When this occurs, accurately answering even the most basic but critical questions about any type of performance metric or KPI becomes painful.
This is why master data management came about. The central goal of MDM and MDM tools is to create a single, trusted view of entities such as suppliers, customers, and products to support decision-making.
A single version of the truth
Organizations have been using master data management solutions for decades to ensure that data for the organization’s entities operate around a single, authoritative version of truth, regardless of where the data comes from. Curated, de-deduplicated, and enriched “mastered data” is imperative for organizations to leverage their data assets with accuracy, confidence, and impact.
Mastered data can answer many important questions such as:
- Who are our customers, and how can we better target them?
- Which products are our most popular and profitable?
- How can we get a holistic view of service consumption for more informed decisions?
Enterprises might also want to know:
- How can we reduce procurement costs?
- How can we enable smarter reporting, analytics, and monitoring/tracking, even if the information comes from many different sources?
- How can we eliminate manual, burdensome data entry and improve operational efficiency and strategic planning?
Answers to these questions are critical across a range of industries, from retail to manufacturing, financial services, and government. The downsides of not having mastered data are substantial. Most organizations see all the symptoms of poor master data in their critical business processes, whether it’s in the form of delayed product launches, high supply chain costs, frequent customer complaints, or hefty regulatory penalties.
Who needs to be involved?
All of this makes MDM a worthy goal, but achieving it is complex. MDM requires tools, technology, and processes to ensure that accurate, consistent, and complete master data is provided across the enterprise and to business partners. MDM also involves putting together the right people and talents. The three core roles involved in successful MDM include:
- Data governance individuals who help data stewards understand how data should be managed, and then hold them accountable to following the requirements.
- Administrators who set up and configure the solution.
- Data stewards who fix, clean, and manage the data directly within the MDM tool.
Depending on the organization and project type, other MDM roles might include program managers, project managers, system administrators, developers, business analysts, and others. In addition to these hands-on roles, it is important to engage stakeholders, such as subject-matter experts and business/IT executives.
One of the biggest challenges with MDM, however, is its inability to scale in today’s complex, increasingly large data environments. Data mastering involves producing clusters of records thought to represent the same entity. Once clusters are created, the next step is to construct a single “golden record,” representing each cluster. This involves finding matches. The traditional MDM solution to finding matches is to write a collection of rules in a proprietary rule language.
The rules-based approach quickly becomes a limitation. The primary driver of this challenge is the dependency on rule-based engines, which require data experts, developers, and business teams to collaborate on coding complex formulas that capture constantly changing business logic. Whenever new data or data updates are introduced to the solution, long cycles of iterations are needed among IT, data, and business teams to refine the MDM rules logic.
The benefits of Agile Data Mastering
Organizations understand the importance of creating golden, master records of critical organizational entities (e.g., customers, suppliers, and products). But that’s just one piece of the puzzle. To be successful, organizations need to be able to do this in an agile, efficient manner. MDM methods have been effective at managing the volume and velocity associated with Big Data, but struggle to keep pace with modern data variety. Agile Data Mastering overcomes this limitation by using machine learning to prepare data and create golden records.
With Agile Data Mastering, organizations can reduce resource requirements and handle multiple projects simultaneously. New data sources, even 1,000+, can be handled with ease and results can be delivered in days to weeks versus weeks to months with traditional MDM approaches. Agile Data Mastering also offers quick—in some cases instant—entity matching to reduce time-to-insight. Technical investments are less steep, because organizations can leverage existing data curation investments as needed and efforts and models from one MDM project can be replicated across multiple domains.
Master records are the fuel for organizational analytics; they represent a complete view of unique entities across the distributed, messy data environments of large organizations. Analytic tools rely on such records to ensure the data being pulled is relevant to the entity being analyzed and that all of the data is captured, ultimately ensuring completeness and trust in the result. MDM is a powerful way to fight data fragmentation and tap into data’s value. But now, an even more powerful, far more scalable approach is available: Agile Data Mastering leveraging machine learning. Download our white paper below to learn more.