AI, graph databases among top BI and analytics trends
Machine learning, graph databases and multi-cloud — William McKnight, president of McKnight Consulting Group, sees these as some of today’s top enterprise BI and analytics trends.
McKnight advised IT professionals to seize on important BI and analytics trends and work on putting the technolgies to use in their organization to help build a more advanced analytics future.
“What we really need are champions and leaders out there that are going to pick up the helm and find ways to get the stuff in the enterprise and get it moving towards bottom-line business success,” McKnight said in a recent Dataversity webinar. “Momentum is paramount.”
McKnight culled his trend predictions from several large-scale surveys he conducted, as well as from observing his clients. Although some of the BI and analytics trends that he detailed may not surprise forward-thinking IT professionals, they’re all already starting to affect large enterprises in tangible ways.
In the webinar, McKnight said the best use for enterprise’s data will be training AI algorithms. “AI algorithms are going to be important, and they are obviously trained on great data,” he said. “So, a mantra of mine is: ‘Let no data escape.’ If you’ve been letting data escape, you are just hamstringing your future AI efforts.” In an interview, Forrester analyst Boris Evelson said he agreed with McKnight’s view, adding that AI is unquestionably one of the biggest BI and analytics trends in 2019 — specifically, machine learning. “With machine learning, BI professionals can … find and identify every kind of pattern, trend, correlation, signal,” Evelson said.
McKnight said his clients often ask him how to architect the data management environments for what he sees as the inevitable AI-dominated future. He said his best answer is to create a strong data infrastructure. This includes effective foundational structures, such as a data lake, data warehouse or data mart. A mantra of mine is: ‘Let no data escape.’ If you’ve been letting data escape, you are just hamstringing your future AI efforts. McKnight said he’s also challenging his clients to change any initiative they’ve labeled as BI to AI and think about doing that initiative as AI. “If you think of the initiative as writing reports, building key performance indicators or something of that nature, you’re never going to get there in terms of thinking about it as AI,” McKnight said. “You have to get behind the reasoning for the report and the reasoning for the KPI.” “You have to understand why something is being done, and oftentimes, when you get to that why, that is what you can disrupt with artificial intelligence,” he added.
That doesn’t necessarily mean AI will dominate the BI and analytics trends in coming years, Evelson said. “A more appropriate statement would be enhancing or improving, as in ‘improve BI/analytics with AI,'” he said. A good way to introduce AI into enterprises is through automation, McKnight added. He said he’s seeing many BI vendors using AI to automate processes and insights.
