IBM spices up its Watson AI platform

IBM has announced a ton of improvements to its big data and AI platform.

Computing giant IBM has unveiled several new additions to its Watson platform aimed at making it easier for developers to prepare enterprise data for AI technologies.

The company has implemented several new data-cataloging and data-refining features to give IT specialists a quick way to handle artificial intelligence data.

The features, which will improve data visibility and security for companies, allows users to connect and share information across a range of public and private cloud environments.

According to IDC, around 75 per cent developers have plans to integrate AI into their apps, but they don't have an efficient way to make sense of complex data.

Addressing these challenges, IBM has expanded its Watson Data platform, a set of tools and services that give IT and business teams a way to gain intelligence from large sets of data.

The new data catalogue and refinery features bring together datasets found in several areas of the cloud, and apply machine learning to cleanse it so it can be integrated into AI applications.

IBM is also improving its metadata capabilities. Companies can now use this information to tag and help deploy data governance policies, allowing teams to easily identify risks of sharing data.

The company is extending the general availability of its analytics engine too, separating storage mechanisms and the data it holds. As a result, developers can build and deploy large datasets easily.

To help companies better control their data, IBM has also unveiled new features for its Unified Governance Platform. There's now greater visibility of client data and capabilities for dealing with future data protection regulations such as GDPR.

Derek Schoettle, general manager of IBM Watson Data Platform, said: "The key to AI starts with a strong data foundation, which turns the volume and velocity of incoming data from a challenge into an asset.

"For companies to innovate and compete with AI, they need a way to grasp and organize data coming in from every source, and to use this complete index of data as the backbone of every decision and initiative."

Michael Kaushansky, chief data officer at global advertising and marketing agency Havas, said: "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.

"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."

Jeremy Perlman, VP Europe at Trifacta, said firms are facing a range of challenges when it comes to big data and AI. "The challenge faced by many companies managing data lakes is how to turn diverse data into actionable insights quickly," he said.

"Successful analysis relies upon accurate, well-structured data that has been formatted for the specific needs of the task at hand. Yet, today's data is bigger and more complex than ever before.

"Analysts can spend up to 80% of their time preparing data for analysis or going backwards and forwards with their technical counterparts to obtain the data they need. This is frankly a waste of time and resources.

"Companies are increasingly looking for self-service solutions for line of business users, i.e. the people who know and understand the data.

"Furthermore machine learning is now enabling business users and analysts to automate some of this data wrangling process. Both of these capabilities are improving the efficiency for how companies analyse big data."