IBM has introduced new offerings to its Watson Data Platform, including data cataloging and data refining, which make it easier for developers and data scientists to analyze and prepare enterprise data for AI applications, regardless of its structure or where it resides. By improving data visibility and security, users can now easily connect and share data across public and private cloud environments.
By 2018, nearly 75 percent of developers will build AI functionality into their apps, according to IDC1. However, they also face the obstacle of making sense of increasingly complex data that lives in different places, and that must be securely and continuously ingested to power these apps.
Addressing these challenges, IBM has expanded the functionality of its Watson Data Platform, an integrated set of tools, services and data on the IBM Cloud that enables data scientists, developers and business teams to gain intelligence from the data most important to their roles, as well as easily access services like machine learning, AI and analytics.
"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.”
Specifically, this expansion includes:
· 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 ability to use metadata, pulled from Data Catalog and Data Refinery, to tag and help enforce data governance policies. This enables teams to more easily identify risks when sharing data, and ensure that sensitive data stays secure;
· 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.
