By using expert-guided machine learning models, Tamr Core helps you curate a variety of data sources at scale, reducing overall time and effort to value.
First, your team of experts supply a small set of examples to the machine learning model. Next, the model uses this information to learn about your data and the similarities between records that indicate whether they refer to the same real world entity, or belong in the same category. Iterative guidance from experts enables the models to get smarter as they import more data. Additionally, you can measure machine learning performance against accuracy metrics.
With Tamr Core's machine learning models, you do not rely on deterministic rules programmed by developers that combine a handful of data sources for consumption. Rigid rules break down when the number of data sources increases, whereas Tamr Core is flexible and robust in the face of data variety.
Breakthrough insights result from these approaches, leading to new growth opportunities, cost savings, and operational improvements, delivered in a fraction of the time and cost of traditional approaches.
|Rules-Driven Approaches||Tamr Approaches|
|Developer-created rules that need to be constantly updated as data changes.||Machine learning based workflow that learns from yes/no responses.|
|Closed systems aimed at selling a broader suite of products.||Interoperability with existing applications across the data value chain.|
|High cost for adding new sources requires significant preprocessing and modification.||Easily onboard new sources by leveraging machine learning models' learnings.|
Updated about 2 months ago