• At IBM Think 2021, IBM released several capabilities that help customers craft what it calls an ‘intelligent data fabric’.
• IBM AutoSQL (Structured Query Language) for Cloud Pack for Data is designed to help streamline access to data stored in multiple locations.
Artificial intelligence (AI) is no longer the shiny new toy on the market that it was a few years ago. Many organizations have by now dabbled with the technology, and a large number have rolled up their sleeves and deployed multiple AI projects. As enterprises mature in their adoption of the technology, they are eager to deploy AI at scale, moving beyond one or two limited implementations to applying machine learning (ML) to more tasks and making it available to a larger number of business units.
But one of the challenges to scaling AI adoption is managing the data required to train ML models. Enterprises are collecting large volumes of data that can lead to valuable insights. However, that data may reside in multiple locations, including public clouds, private clouds, and on-premises, resulting in data silos. The data needs to be categorized and labeled, and it needs to be managed with appropriate governance policies before it can be put to use. The complexity involved in conducting these tasks can hamper advanced analytics initiatives.
IBM is rolling out new tools to address this challenge. At IBM Think 2021 the company released several capabilities that help customers craft what it calls an ‘intelligent data fabric’. The newly added IBM AutoSQL for Cloud Pack for Data is designed to help streamline access to data stored in multiple locations. AutoSQL allows users to create single query to use on disparate data sources when retrieving information for machine learning models. Users are less likely to need to move data or maintain multiple query engines, making the process less resource intensive and more efficient. The newly available AutoCatalog uses AI to maintain a catalog of data assets, helping developers more easily find data sets, regardless of where they reside. AutoPrivacy, also announced at IBM Think 2021, uses AI to automate identification of and enforcement of policies on sensitive data.
The new capabilities neatly complement IBM Cloud Pak for Data, which was created three years ago to address enterprises’ preferences for maintaining multi-cloud environments. IBM Cloud Pak for Data enables enterprises to use data from any cloud to train ML models. The tools also support IBM’s vision for an ‘intelligent data fabric’ that orchestrates disparate sources to provide the right data, at the right time, and with appropriate governance, to support applications, analytics, and business process automation.
Enterprises know that using advanced analytics to glean insights from the vast amounts of data collected today and transforming business processes and decision-making based on its findings can improve their competitive positioning. But managing the data and accessing it efficiently when it resides in multiple locations is a key challenge. The preference for multi-cloud environments is unlikely to change any time soon, therefore tools that help enterprises navigate the complexity of managing disparate data sources will be critical to deploying AI at scale.