Despite improvements in technology, implementation of master data management (MDM) solutions have long been a known pain for many organizations pushing to improve data quality and competency. The source of this pain is often due to the fact that traditional MDM solutions solve the data mastering problem using deterministic, rule-based approaches that do not easily accommodate nor scale for the increasing flow of messy, diverse data coming from disparate data systems.
Faster technology has not been able to remove this pain, but it can be relieved with a fresh approach to MDM. In previous blog posts, my colleagues have examined in detail this new approach while explaining the need for organizations to adopt an agile approach to the data mastering problem, as well as why this approach is critical to an organization’s digital transformation. Tamr’s API-driven, machine learning capability makes agile data mastering possible as it fundamentally changes the way we can tackle the data mastering problem.
Easier with Machine Learning
While it might be counter-intuitive for some, managing the logic for mastering large, diverse data sets through machine learning is significantly easier than creating and managing a network of custom rules and formulas. In fact, building machine learning models may not require any technical or data science knowledge at all – just general knowledge about your data.
With Tamr’s Unify, it only takes a customer a few days of answering questions about their data to quickly generate a custom-tailored machine learning model for their data ecosystem. The rest of the time is spent tuning the models with our validation tools and figuring out how to best integrate and leverage the resulting mastered data.
This agile approach to MDM experience using machine learning is a stark contrast to a team of developers iterating over hundreds of custom formulas as they attempt to capture the logic around the organization’s variety of data records and sources, which may always be changing.
Best-of-Breed Data Mastering
At Tamr, we often emphasize the importance of where Tamr fits in an organization’s overall DataOps stack. Organizations implementing modern data infrastructure deserve to be able to select the best software for their specific problems without being locked into any technology bundles that may only partially serve their needs (or create new problems).
To tackle that issue, Tamr functionalities are UI-friendly, but also extensively API-driven so that organizations can seamlessly integrate features that they need within their DataOps stack without being locked down.
For example, even if an organization has an existing technology bundle for its MDM solution needs, Tamr machine learning models for matching records, as well as other specific features, can be integrated with existing system infrastructure and DataOps pipeline.
Higher Expectations with Tamr Agile Data Mastering
Tamr’s machine-learning and interoperable, modular approach to the data mastering problem disrupt the expectations of traditional MDM solutions when it comes to flexibility, speed, scalability, and cost.
With our feature-rich solution for data mastering, Tamr customers that have taken months/years to implement a working MDM solution may now have a best-of-breed solution to their specific problems within weeks/months – allowing them to focus on the actual opportunities that come from curated, mastered data.
One of those opportunities includes leveraging mastered data to standardize golden records for customers, products, vendors, or any other type of entity that is a part of their business. If any of those entity records are being combined or distributed across systems, Tamr also helps organizations maintain a permanent ID to track them.
Tamr provides a plethora of features to help organizations capture the downstream values enabled by an agile, easily maintainable MDM solution. By taking a best-of-breed approach powered by machine learning to the real business problems that MDM solutions are meant to solve, Tamr continues to support an extensive amount of possibilities for leveraging an organization’s data assets and driving transformational outcomes.
For a deeper dive into how an agile approach to MDM can fundamentally change your expectations, dig into the white paper Agile Data Mastering: Raising Expectations for Master Data Management (MDM).