Published By - Brian Curtis

The Forrester Wave: Machine Learning Data Catalog

The Forrester Wave: Machine Learning Data Catalog | YourTechDiet

The 12 Providers That Matter Most And How They Stack Up

Businesses across virtually every industry realize that the effective use of data is the key to many improvements in productivity, customer service, supply chains, product creation, and service delivery. A data catalog is a vital asset that enables such improvements, and automating this capability with Machine Learning is crucial to both the scale and value of a data catalog.

In our 29-criteria evaluation of machine learning data catalogs (MLDCs) providers, we identified the 12 most significant ones — Alation, Cambridge Semantics, Cloudera, Collibra, Hortonworks, IBM, Infogix, Informatica, Oracle, Reltio, Unifi Software, and Waterline Data – and researched, analyzed, and scored them.

Key Takeaways

IBM, Reltio, Unifi Software, Alation, And Collibra Lead The Pack Forrester’s research uncovered a market in which IBM, Reltio, Unifi Software, Alation, and Collibra are Leaders; Informatica, Oracle, Waterline Data, Infogix, Cambridge Semantics, and Cloudera are Strong Performers; and Hortonworks is a Contender.

Data Pros Are Looking For Data Understanding That Everyone Can Access The MLDC market is growing because firms want to scale data to the masses through selfservice.

However, back-end data management technology can’t support tribal knowledge, provide a good user experience (UX) for data consumers, and scale across a highly federated data ecosystem. MLDCs solve this and scale elastically by leveraging their machine learning (ML) capabilities.

Machine Learning, Collaboration, And Activation Are Key Differentiators Combining ML with collaboration and activation scales out data understanding and speeds up use. Thus, MLDCs are demonstrating ROI in many cases within four weeks. Additionally, ML provides certain insights from data as part of its analytic process that previously required an analyst to see facts, trends, and causal relationships.

This report shows how each provider measures up and helps enterprise architecture (EA) professionals make the right choice.

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