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Time to Take Another Look at MDM

Master data management (MDM) as a discipline has been around for decades. And MDM software has been evolving over time to help organizations meet their changing data requirements. But if your understanding of MDM software is even a little bit dated, it’s time to take another look. The early days of MDM were not even about MDM software, because applications dedicated to the purpose hadn’t yet evolved. Let’s unpack the history. “Master data” is the collective term for all the core reference data that organizations track, including customers, products, locations, employees and just about any other information that might be kept in a list. This information was first managed in the primary business software applications of earlier days, including enterprise resource planning (ERP), customer relationship management (CRM) and supply chain management (SCM) software. These applications were the repositories of the organization’s “golden records,” or master versions of the data.

Manual processes were used to ensure consistency of this information across systems and applications. If one company acquired another and they needed to merge software systems, reconciliation required days or weeks of manual work and batch jobs to load data from one system into another. This all may sound archaic today, and it became complicated enough that MDM software emerged to automate these processes. Automation made the work easier, the data more reliable, and offered the promise that organizations could spend less time on data management and more time focused on their core business activities.


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About the Author

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David Menninger

SVP and Research Director
Ventana Research

David Menninger is responsible for the overall direction of research on data and analytics technologies at Ventana Research. He covers major areas including artificial learning and machine learning, big data, business intelligence, collaboration, data science and information management along with the additional specific research categories including blockchain, data governance, data lakes, data preparation, embedded analytics, natural language processing (NLP) and IoT.