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Rob Thomas, GM Analytics for IBM, kicked off a session on putting data to work with AI. Rob began talking about the impact standard shipping containers had on the shipping industry and how a similar move is required in data – something that will make it easy to combine and analyze data in a standard way. And that only this kind of data landscape can support systematic application of analytics and AI.
ING came on stage to talk about their information architecture – one that addresses regulatory issues but also makes it possible for everyone to access, understand and use data for better decisions. They pulled all their data into a data lake architecture and then mapped the core of this to a standard set of corporate data models/vocabulary based on industry models. On to this they layered governance etc. Plus this supports the application of AI both to improve the data and its metadata AND to improve decision-making.
IBM has a new solution offering – IBM Cloud Private for Data. This is designed to provide an out of the box environment for managing an organization’s data and supporting its broad an deep application of AI and analytics. It makes it easy to bring on-premise and cloud data, tracks machine learning models running against the data and provides integrated search and preview across the metadata for all this data.
Beth Smith came on stage to add Watson and AI into this mix. Lots of organizations lack the AI skills they need so IBM is launching IBM Watson Studio to help AI teams collaborate around the data an organization has, working easily with the new IBM Cloud Private for Data. It’s open, supporting open source as well as IBM-specific AI capabilities like the pre-trained Watson APIs. It’s underpinned by a catalog that combines data and any analytics you have built against it. It also supports and automates many of the experimentation and training runs that good ML and AI models require – helping reduce the manual load on data scientists – while providing a rich visual interface for much of the work. It’s designed to make it easier to build, easier to run and easier to share the tasks needed for AI.
IBM has also been investing in the services support that companies need and launching the Data Science Elite Team to deliver initial free workshops to help companies get over the hump and get started with more sophisticated analytics and AI.
Nice to see the investment in making AI and analytics easier. Wish IBM would include its Business Rules Management System Operational Decision Manager as part of this stack – would make operationalizing the result much easier.
