What is Master Data Management?

Master Data Management is a set of tools and processes used to define and manage an organization’s critical data. Using MDM, organizations can create a single, trusted view of entities such as suppliers, customers, and products to support analytics in business decision-making and data streams in key operational applications.

Why Businesses Use MDM

Many businesses today have a pressing need: to uncover accurate analytics insight from hundreds of separate applications and systems. And to do this, these organizations need to consolidate, cleanse, and categorize data sets from internal data sources such as CRM and ERP systems as well as external reference data from data aggregators and third-party sources.In fact, organizations have been using master data management 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.One popular use case for MDM is mastering customer data so the organization has a 360 degree view across the organization. This consolidated view is valuable to teams across the organization, from sales and marketing, to finance and customer success.Businesses use mastered data to 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?

And 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?

Having answers to these questions is critical across a range of industries, from retail and manufacturing to financial services and the public sector. But the downsides of not having mastered data are substantial. And 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.

What are the Limitations of Legacy Master Data Management solutions?

One of the biggest challenges with traditional MDM 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” that represents each cluster. This involves finding matches. And the traditional master data management solution to finding matches is to write a collection of rules in a proprietary rule language.The rules-based approach used by traditional MDM solutions or some of the home-grown rule-based master data management approaches 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 Key Benefits of Cloud-Native Machine-Learning MDM

1. Accelerating analytic insights

Our studies have shown 90%+ accuracy mastering data with Tamr technology, compared to only 50-80% accuracy with rules-based models. Tamr’s output is clean, consolidated data that can then be used to power visualization tools such as PowerBI, Qlik, Tableau, and Thoughtspot.

2. Boosting operational efficiency

Tamr couples its Machine Learning technology with expert feedback to reduce the manual workflows required to integrate new datasets by up to 90%, requiring minimal hours of work from data stewards, as compared to months for IT-driven projects to adjust rules in master data management systems. The result is that data teams are able to focus on higher-value business initiatives instead of manual data prep workflows.

3. Lowering the total cost of ownership for data projects and enhancing data operations reliably with cloud-native data mastering.

It’s the widely-acknowledged consensus that today, cloud native subscriptions offer net savings of at least 30-50% over legacy, on-premises deployments for software. And that’s not mentioning the direct cost saving on labor (managing the infrastructure), hardware and utilities, and indirect cost saving that results from avoiding unplanned data downtime that causes productivity or even revenue loss.