Analytics Emerges as a Means to an AI Solution's EndBy Mike Vizard | Print
Vendors are embedding machine and deep-learning algorithms into applications that promise to transform IT by using AI that's enabled by advanced algorithms.
Applying Machine-Learning to Data in Legacy Apps
While machine and deep-learning algorithms are being applied most aggressively against data in the cloud, IBM and SAP have signaled their intentions to also apply machine-learning algorithms to data residing in new and legacy applications running on-premises. IBM has announced it is moving to make machine-learning algorithms available on any platform that drives its mainframes, starting with the z/OS operating system.
"We think there's a high opportunity to apply algorithms against legacy applications," said Dinesh Nirmal, vice president of analytics development at IBM. "You’d be able to apply machine learning wherever the application is."
SAP, meanwhile, is focusing its efforts on applying machine-learning algorithms wherever it makes the most sense.
Bernd Leukert, a member of the executive board who oversees product development across SAP, said that in the company's view, it makes more sense to bring algorithms to the data rather than move data to some central repository. In fact, he noted, moving data into a central repository often creates additional cost and security risks that many enterprise IT organizations already find untenable.
Regardless of where it is applied, however, the days of applying analytics against data residing in a separate repository are coming to a close, Leukert said. "Bringing machine learning to the data will be game-changing," he added.
In fact, developers of internet of things (IoT) applications expect entire business processes to be automated via a combination of analytics and machine- and deep-learning algorithms. Sensify CTO Sathish Gajaraju said his company, which partners with SAP, already is working with a client that is enabling direct shipment of paper goods from factories in Asia to specific retail outlets in the United States, eliminating the need for a warehouse.
The process is possible because it's now economically feasible to attach RFID tags to even the most basic of commodity items, Gajaraju said, adding that "It's all about capturing data at the relevant point."
The upside for solution providers is the massive opportunity brought by the new era of the intelligent enterprise. The downside is that the expertise required to tap into that opportunity is getting more expensive with each passing day.
But the path to the intelligent enterprise invariably starts with analytics: When all is said and done, analytics is simply a means to an AI end that soon will be a requirement of any application worth building.