Zeenea was cited as one of the top Machine Learning Data Catalogs of 2020 alongside Alation, Collibra, data.world, Talend, Informatica, IBM, and more!
You can use machine learning data catalogs (MLDCs) to interpret data, accelerate data use, and connect data to business outcomes.
But to realize these benefits, you’ll first have to select from a diverse set of vendors that vary by size, functionality, geography, and vertical market focus.
Enterprise architecture (EA) professionals should use this report to understand the value they can expect from an MLDC provider and to select one based on size and functionality.
1) Improve Data Democratization With Machine Learning Data Catalogs
Metadata management is reborn with contextual based references and models to make data ready and reliable for activation.
2) Select Vendors Based On Size And Functionality
Organizations can achieve value from MLDCs by enabling operational alignment between data operations, data governance, analytics, and data consumers to activate data.
3) Harmonize Data Roles To MLDC Functionality
MLDCs will force organizations to address the unique processes and requirements of different data roles.
“Enterprises that aren’t able to put data at the heart of their resilience are failing and closing,” according to a blog post by Forrester VP, Principal Analyst and ML Data Catalog Report author Michele Goetz*. With such high stakes at play, we believe Forrester recognizes our data catalog’s flexibility, serving governance, analytics, and marketplace objectives with a clean and intuitive user experience.