Creating value and competitive advantage are two top use cases for Artificial Intelligence (AI). Now, it’s time for businesses to really get their hands on how to put it to work to those ends.
Most companies are ready and willing to take it on. In the case of large enterprises, they’re looking for ways to disrupt the disruptors – the smaller, nimble startups that harness the Cloud’s computing power to quickly innovate, taking a bite out of their markets. “Big companies know they need AI to get there,” said Jay Limburn, IBM Distinguished Engineer and Director of Product Management.
IBM’s proposition is to start the ball rolling with its recently announced Watson Studio and the Watson Knowledge Catalog. The IBM Cloud Services support end-to-end AI workflows. Studio is an integrated environment for connecting to and refining data to build and train Artificial Intelligence models at scale; these can be continuously trained against new data as they infuse business processes. However, “data cataloging is the first step to become effective with AI,” said Limburn.
The Knowledge Catalog enters the picture here as a rich, intelligent Metadata Index that provides a view of and easy accessibility by knowledge workers to structured and unstructured data and Analytics assets that have been profiled and classified – no matter where they reside. Watson’s Natural Language Processing supports getting intelligence from structured and unstructured data to use in a uniform way.
Its AI-enabled search and recommendations based on Watson’s understanding of relationships between assets help users discover relevant catalog assets for their modeling needs. “We use AI to improve your AI,” he said – think of it as a ‘Spotify for data’ recommendation model. “This model self-trains over time, so the more you use it the more exhaustive it is,” he remarked. Last but not least, with a data policy activation engine, data can be opened up for self-service use by defining rules that explain how that data may be used and by whom.
Additionally, artifacts created on AI journeys can be shared back into the Knowledge Catalog and reused, Limburn explained, so that the efforts that went into preparing data for a Data Science project don’t go to waste.
The technologies give Data Governance a whole new purpose, he said –it’s not just for compliance but for driving innovation and disruption, too. One client using the Knowledge Catalog to index information from some 200 databases, which came with the acquisition of another company, found that the Knowledge Catalog discovered additional unexpected data sources relating to a retired product and then put them into shape, too.
