What is Master Data?

Having well-defined master data is essential to running your business operations. But what is master data? Simply put, master data is a holistic view of your key business entities, providing a consistent set of identifiers and attributes that give context to the business data that matters most to your organization. It’s about ensuring that clean, accurate, curated data – the best available – is available for use throughout the company to manage operations and make critical business decisions.

Master data is different from other types of data captured across your organization. For instance, it’s not the same as the transactional data generated by applications. Instead, master data gives context to the transaction itself by providing the business objects – like customer, product, or supplier – on which the transactions are performed.

Transactional data, on the other hand, results from various applications supporting everyday business processes, and includes transaction-level information such as a shipping status or the reference number of a patient request. And when you run analyses on transactional data, you end up with analytical data such as average sale price or average invoice amount.

All three types of data are critical in their own way, and work in tandem to help you run your business efficiently and effectively.

Depending on your industry, the definition of “key entities” will differ. For example, healthcare organizations place high value on patient data while manufacturers prize data about their suppliers, parts, and materials. But regardless of which industry you’re in, one thing remains the same: having clean, accurate master data for operations and decision making is critical to your success.

A Use Case for Master Data
Master data applies to multiple use cases across your organization. Let’s take a closer look at one of them: Customer360.

If your organization is like most, customer data resides in multiple systems. And it’s not always captured consistently. Which leaves your operations team wondering – which account is the right one to contact? And from there, the questions escalate even further. Which address is the right one to use? And, is this data even accurate?

Without master data, your teams are left with inconsistent data living in disparate systems with an unclear picture of if multiple records are actually related to the same customer account. And it makes answering seemingly simple questions such as “which customer generates the most revenue” difficult, if not impossible, to answer.

But when you implement customer master data and next generation master data management, the picture becomes much clearer. By pairing machine learning with human feedback, you can break down data silos and deliver clean, accurate and curated customer data, unified into a usable form that supports governance, analytics, modeling, and consumption by the business users.

Here’s how it works. You start first with schema mapping where you align disparate data sources to a single schema. For example, recognize that the field “customer_name” may be captured as “cust_name” or “client,” depending on the system.

Next, you tackle the issue of duplicate records through record matching. By clustering records that represent the same entity within and across sources, you eliminate the issue of duplicates. Next, using Tamr, you can assign each cluster a Tamr ID – a unique and persistent ID to track and identify the entity. Then, using third party data sources curated by the Tamr team, you can enrich your internal records with external data, maximizing the value of your dataset. Finally, you deliver a “golden record” or a single record for each cluster that consolidates the best information and delivers the best data to feed your tools.

While the example above focused on a single view of a customer, master data applies to other areas of the business like spend analytics, supplier master data management, parts mastering, clinical trial conversion, and more.

Putting Master Data into Practice at Santander UK
Santander faced a number of challenges related to its data. First, they suffered from enterprise data silos which caused a limited view of customers. They also struggled to reconcile the disparate customer data from within these silos, which resulted in a tremendous amount of manual effort and slow lending decisions. Finally, they tried to remediate the issue using rules-based systems, but ultimately failed, which caused their challenges to persist.

To improve their lending process and the reliability of their customer data, Santander UK embarked on an effort to master their customer data. They created complete, up-to-date customer views from 45 sources and tens of millions of records. And they did so in under four weeks with Tamr!

They also used Tamr to generate a unique and persistent ID for each customer that was composed of corporate, retail, and global business data. Finally, they clustered customer views into business-ready segments by products, regions, etc.

The results speak for themselves. Santander UK was able to speed up their lending processes by cutting credit decision times in half with the rollout of a new lending system powered by data from Tamr. They increased the reliability of reporting data using unified data feeds for financial, credit risk, and regulatory reporting. And they empowered their sales teams for cross-sell opportunities by providing a holistic view of customers across divisions and products.

Having clean, accurate and curated data to manage operations and make better decisions is a critical success factor for modern businesses. Master data provides the holistic view of your data needed to optimize operations and fuel better decision making across your organization.

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