AI and associated machine learning will play a role in preparing and cleansing the data needed to make AI and machine learning work. “We’ll see more adoption of machine learning to assist with all aspects of data fabric and data management,” said Tendu Yogurtcu, CTO of Syncsort. “This will span data integration to data quality, as insights are only as good as the data that drives them, and only useful if the data is error-free and ready for advanced analytics.” As proof of this need, Yogurtcu noted that “data scientists are still spending more than 80% of their time on data preparation. Bringing machine learning into data management processes will help ensure automation of these data preparation steps. Using machine learning to drive business rules, in the data cleansing and modeling processes, will free up data stewards and data scientists to focus on deriving actionable insights from the data.”
There’s even a name for this application of AI to improve AI. Jerry Melnick, president and CEO of SIOS, sees “AIOps”—which is the use of machine learning applications in IT—as a growing proposition. Most IT teams “are broken up into silos, and each silo has its own set of analytics and diagnostic tools it uses to trace performance issues,” said Melnick. “AIOps eliminates this issue, using machine learning to track the relationship between every element of IT, and understand how these elements interact with each other. This elevates problem-solving about the siloed webs of IT, and gives teams actionable, data-driven solutions to IT’s biggest headaches.”
CompuCom CIO and CDO Justin Mennen agreed that AI and associated technologies will significantly shape data management practices, especially “when we consider the overwhelming need for organizations to leverage predictive and prescriptive analytics to compete.” Progress on this front, however, “requires a new construct beyond the data mining BI model of the last 2 decades,” Mennen said. He urges looking at data in new ways, including “the use of data transformation and graph models with analytics to view the intersections, correlations, and isolations among the noise,” combined with “the communication and storytelling of potential wisdom.”
Ultimately, of course, AI needs to deliver to the business. Doolittle sees more intelligent data management solutions that “combine machine learning with rule-based systems to watch and learn from changing data usage patterns and user behaviors. They automatically create data management rules that can automatically direct changes or actions to better serve changing business demands. Examples include identifying resource-consuming user behaviors that indicate a need for more shifting data workloads to a more appropriate or cost-effective data platforms, or increasing use of more detailed data indicating a demand for direct access to source data to improve analytical outcomes and lower data handling costs.”
Another technology development that is fueled by data is the rise of virtual assistants. The cutting-edge web companies are employing this type of solution, and it is coming to mainstream enterprises as well. Google, for example, is thriving with its Google Now virtual assistant, “which is only getting smarter because of its ability to use available data from web interactions to provide a personal experience for users,” observed Luc Burgelman, CEO of NGDATA.
“Companies—especially those in customer-facing industries such as banks, media, and retail—need to engage with and support customers through conversational interfaces, so we’ll see them add more artificial intelligence and cognitive services to their offerings to create interactive experiences,” said Burgelman. Data and the effective management of it are at the heart of such capabilities, he added.