IBM Expands Watson Data Platform


New offerings from IBM include data cataloging and data refining.

IBM has announced new features for its Watson Data Platform. The new offerings include data cataloging and refining to make analyzing and preparing data for AI applications easier. Improved data visibility and enforcement of data security policies will allow uses to connect and share data across both public and private cloud environments.

IBM said in its announcement that. according to IDC, by 2018, nearly 75 percent of developers will build AI functionality into their apps, but must address the obstacle of collecting and making sense of complex data that lives in different places and needs to be securely ingested in real time to power those apps.

IBM says the expanded functionality of the Watson Data Platform is designed to address those challenges. It provides an integrated set of tools, services and data in the IBM cloud that will allow users to gain intelligence from their data and access machine learning, AI and analytics.

AI offers a holistic view

“We are always looking for new ways to gain a more holistic view of our clients’ campaign data, and design tailored approaches for each ad and marketing tactic,” said Michael Kaushansky, chief data officer at Havas, a global advertising and marketing consultancy.

“The Watson Data Platform is helping us do just that by quickly connecting offline and online marketing data. For example, we recently kicked off a test for one of our automotive clients, aiming to connect customer data, advertising information in existing systems, and online engagement metrics to better target the right audiences at the right time,” Kaushansky said.

According to a post on IBM’s website, the new features include the following:

  • New Data Catalog and Data Refinery offerings, which bring together datasets that live in different formats on the cloud, in existing systems and in third party sources; as well as apply machine learning to process and cleanse this data so it can be ingested for AI applications.
  • The capability to use metadata, pulled from Data Catalog and Data Refinery, to tag and help enforce a client’s data governance policies. This gives teams a foundation to more easily identify risks when sharing sensitive data.
  • The general availability of Analytics Engine to separate the storage of data from the information it holds, allowing it to be analyzed and fed into apps at much greater speeds. As a result, developers and data scientists can more easily share and build with large datasets.

Sue Walsh

About Sue Walsh

Sue Walsh is News Writer for RTInsights, and a freelance writer and social media manager living in New York City. Her specialties include tech, security and e-commerce. You can follow her on Twitter at @girlfridaygeek.

Leave a Reply

Your email address will not be published. Required fields are marked *