New Forrester Report Explains How Machine Learning Data Catalogs Turn Data into Business Outcomes

Team Zaloni December 7th, 2020

A recent Forrester report, “Now Tech: Machine Learning Data Catalogs, Q4 2020,” named Zaloni as a vendor in the Machine Learning Data Catalog (MLDC) market. As defined by Forrester:

“A machine learning data catalog (MLDC) discovers, profiles, interprets, and applies semantics and data policies to data and metadata using machine learning to enable data governance and DataOps, helping analysts, data scientists, and data consumers turn data into business outcomes.”

Establishing an MLDC foundation is a critical component for the internet of things (IoT), blockchain, AI, and intelligent security technology trends to prosper. Finding an MLDC vendor is the primary force that enables such trends by accelerating data use, interpreting every data type and source, and connecting that data to business initiatives and goals. The report breaks down various vendors, listing 27 different companies while categorizing each by size. The Forrester report overviewed companies based on each vendor’s market presence and the unique functionality of the product, service, and solutions they offer to the MLDC market.

Our take on why Zaloni was recognized

From our perspective, the machine learning data catalog is the collaborative core of our DataOps software platform, Arena. 

Zaloni takes a unified DataOps approach to data management. We view the data catalog as the collaborative core of the DataOps approach, providing end-to-end visibility and control to each step of the data supply chain. As data travels from the data producers to the hands of data consumers, various steps in the supply chain may be automated or augmented using machine learning to improve accuracy, quality and increase efficiency. 

Arena serves as the seamless data platform that ultimately reduces IT costs, accelerates time to analytics, and standardizes data security. Below are two ML-powered features in the Arena platform: 

Data Mastering

At Zaloni, we work with many companies who are dealing with data sprawl and increasingly complex data ecosystems and want to unify their data by integrating siloed data sources. We utilize machine learning techniques to match and merge data sources into master records to provide data consumers with a single source of truth, giving confidence that the data they are accessing is reliable and accurate. This machine learning approach to data mastering is commonly used by our customers to integrate incomplete customer data from multiple sources to create customer golden records. With golden records in place, data consumers can create 360-degree views of the customer, allowing them to improve marketing and business outcomes with a single source of truth of each of their customers. 

Data Classification 

In addition to data mastering, Zaloni leverages machine learning to improve data quality and ensure data security through data classification. Machine learning classification algorithms automatically identify data categories, associate each type with data quality rules, flag them for sensitive data such as personally identifiable information (PII), and obfuscate the data based on governance policies to ensure compliance. Through ML-powered data classification, companies can have peace of mind knowing they are delivering quality, secure data to data consumers quicker than ever before.

Learn More

If you are ready to take on the next step and learn more about the importance of MLDC functionality for your organization, schedule a demo of the Zaloni Arena platform. 

about the author

Team Zaloni

This team of authors from Team Zaloni provide their expertise, best practices, tips and tricks and use cases across varied topics incuding: data governance, data catalog, dataops, observability, and so much more.