Trusted business decisions rely on accurate, complete, and consistent data. That’s why getting a single view of truth from multiple data sources across your business is critical to your company’s success. Traditional master data management (MDM) programs are fraught with challenges and often take years before they add value, but they don’t have to. MDM with entity resolution augmentation allows you to transform legacy IT applications without costly missteps. Learn how entity resolution enables you to create a rich, single view of your data, bridging the divide in your MDM programs. And you’ll discover a vast treasure in golden customer records.

 

Traditional Master Data Management solutions

Master data refers to your data that’s highly used and shared across various processes and often includes stable information such as customer data, product data, and location data. Master data management refers to the process of centralizing that information and ultimately to better manage the data.

 

The problem with master data is that it’s spread across multiple applications. For IT, this means moving the mastership of that data—to centralize it—within a single system. Ideally, what companies want and what MDM solutions are geared toward is allowing someone to update a customer record and then integrate it for propagation to other systems.

 

When companies tackle data management, they look for MDM products, many of which use data matching. They might choose a product with common features that enable users to centrally manage data and distribute it to multiple applications as part of a multi-year program.

 

However, the path to get there contains many hurdles, particularly a rigid data model. Such models take an age to ingest source system feeds and data matching that’s beset by data quality issues. Companies are often put off by these solutions because of the time necessary to deliver any value and the risks in delivery. As a result, many programs do not reach a conclusion when the funding dries up after a few years.

 

The challenge with data matching in MDM solutions

MDM solutions are a design approach for organizations to improve data management. These solutions provide features that aim to support the full process of centralizing master data. They include a centralized data store, the ability to manage attributes, data stewardship, and data matching capabilities. But they aren’t good at creating a single customer view. They lack the functionality they need to efficiently and effectively pull data together and resolve inconsistencies and quality issues.

 

Traditional MDM solutions rely on data matching, which compares each data string and applies a score across it to create a record-to-record match. Their probabilistic matching engines use an algorithm that evaluates records and record scores. This method isn’t ideal for customer records that have several variations because they might have multiple identifying attributes. Also, if records have a slight data quality problem, such as missing data or name variations, data matching is unable to identify them as matching records. Users can’t lower the score threshold because it over links some items and under links others.

 

A rich single view of data with entity resolution

A more powerful approach to master data management is entity resolution. This comprehensive method uses corroborating evidence from multiple records and cross-references them to build a central entity profile—a rich single view of a customer.

 

Entity resolution examines everything about a customer, using both internal and external data references. For example, this method can gather the following types of information about a customer:

  • The products or services they use
  • Products they hold
  • Differences in account assessments
  • Different IDs
  • Whether a customer is a director of a company
  • Linked accounts
  • Access to accounts
  • Potential family relationships

 

By pulling information from multiple sources, entity resolution generates a comprehensive view of everything known about a customer. In MDM, the entity profile you get results in a richer view of a customer than is otherwise possible, creating the veritable treasure—the golden record.

 

The entity resolution augmentation approach to MDM

To enable a single view of data, Quantexa uses Dynamic Entity Resolution. The Quantexa approach uses a schema-less model that’s agnostic to data structures, saving IT organizations time and money from performing preliminary data conversions. It creates a customer profile by taking data from multiple source inputs and creating a trusted set of attributes about them.

 

The Quantexa platform pulls in the raw data from different data sources and analyzes it, producing a single view of a customer. It also adapts to data source changes, such as when you need to change IT applications. If you need to migrate from an existing system of record in a line of business to a central MDM product, Quantexa provides a single view that enables you to push that trusted record in.

 

Unlike traditional MDM solutions, Quantexa provides data enrichment by using third-party data sources such as global corporate registry or open-source intelligence. These produce a better quality, merged customer record with more information than traditional MDM solutions can collect.

 

The Quantexa value

The value of entity resolution from Quantexa isn’t only in how quickly it creates the rich single view. It’s in the bridge it provides to accelerate your MDM program to produce the golden record. Plus, Quantexa provides lower-risk delivery for your MDM program because it targets the most challenging part of MDM, bringing together data from diverse sources into a single view.

 

Bridge the divide between your data sources to achieve your golden entity profile. By choosing entity resolution from Quantexa to augment your MDM solution, you’ll improve the visibility of your data and gain better decision-making between data sources.

Learn how Quantexa’s MDM augmentation approach can help you build a single, trusted view of your data.

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