New Expectations for Mastering Data with Machine Learning
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 anagile approach to the data mastering problem, as well as why this approach is critical to anorganization’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.
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.
WithTamr’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.
Thisagile approach to MDMexperience 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.
At Tamr, we often emphasize the importance of where Tamr fits in an organization’s overallDataOps stack.
