How the Machine Learning Catalogs Stack Up

You can’t do anything with data – let alone use it for machine learning – if you don’t know where it is. In the age of big data, this is not a trivial matter. It is also the main driver that’s propelling the rise of machine learning data catalogs, which the analysts at Forrester recently ranked and sorted. Just a word of warning: the name at the top of the list might surprise you.

According to Michelle Goetz’s June 21 Forrester Wave report, the percentage of analytic decision makers managing more than 1 petabyte of data (either structured, semi-structured, or unstructured) has essentially tripled from 2016 to 2017. That rapid growth has exposed all manner of problems in company’s existing data management and analytic endeavors.

Two of the biggest challenges that companies face today, Goetz writes, are gathering and managing data in a governed manner on the one hand, and managing the business processes that surround the data analytics activities on the other.

“For EA [enterprise analytics] professionals, relying on people and manual processes to provision, manage, and govern data simply does not scale,” the Forrester analyst writes. “Enterprises are waking up to this fact and turning to data catalogs to democratize access to data, enable tribal data knowledge to curate information, apply data policies, and activate all data for business value quickly.”

In the 2Q 2018 Forrester Wave for Machine Learning Data Catalogs, Goetz and company identify 12 data catalog software providers that should be on your radar. The report, which you can download courtesy of Alation (who was, not coincidentally, featured in the report) ranked these vendors across 29 different factors. Based on the vendors’ score across those factors, Forrester divided the vendors up across three groups, including Leaders, Strong Performers, and Contenders.

Here’s how Forrester ranked the various machine learning data catalog (MLDC) vendors across those categories:

IBM came out on top in this particular analysis. Forrester says that Big Blue “reimagined data” with its various offerings, including the Watson Knowledge Catalog, which apparently included many of the features that Goetz was looking for. In particular, the Forrester analyst appreciated how IBM designed its user interface, which hasn’t always been a strong suite for IBM product design teams.

“The UI lets roles work the way they want to and not reorient their data sourcing, stewardship, or administrative processes to match another role’s workspace,” Goetz writes. The one caveat to IBM’s good showing is the relative newness of the Watson Knowledge Catalog, which was just launched in enterprise mode and didn’t yet have all the features needed, such as full lineage analysis (due out this summer).

Coming in right behind IBM was Reltio, which is best known for being a master data management (MDM) provider. However, Forrester says Reltio didn’t let its MDM origins prevent it from offering compelling value as a data catalog, although it does take some getting used to. Data engineers and data stewards should be comfortable in the cloud provider’s self-service setting, according to Forrester.

Unifi Software took home third place in the rankings thanks to the simplicity of its Unifi Data Platform that elevate the user’s intent, according to Forrester. The product’s natural language interface, which allows users to ask questions about the data, drew favorable reviews from Forrester. The one drawback was the data science workbench, but the analyst group said that shouldn’t keep Unifi off customers’ shortlist, especially thanks to the way that Unifi mixes data preparation and self-service in with the data catalog functionality.

Coming in fourth was Alation, which Forrester credits with kicking off the MLDC trend back in 2012.