Regardless of the present thought that business intelligence and artificial intelligence are separate disciplines, AI-powered BI is a thought that ought to get more consideration.
Over the previous month, I invested energy at meetings committed to the two shafts of the analytics spectrum: business intelligence and artificial intelligence. I left away asking why they’re so far separated.
Both involve data analytical alogrithms at their core. But while BI is primarily retrospective in nature, AI is all about the future.
The statistical analysis behind BI is primarily about basic data analytical alogrithms, in contrast with the ultra-sophisticated machine learning and deep learning algorithms that underpin AI.
As a result of these distinctions, and the way that AI as a valuable tols is generally new while BI is yesterday’s news, we treat the two disciplines as wholly separate.
During my time at the O’Reilly AI Conference in New York in late June, I heard not a single mention of BI. Conversely, at Dresner Advisory Services LLC’s Real Business Intelligence conference held on the campus of MIT in July, AI was primarily used as a counterpoint to BI in discussions of the latter’s strong business value and high adoption.
AI and BI: An Idea Whose Time Has Come
But speaker Mico Yuk, CEO of consultancy BI Brainz in Atlanta, had an interesting idea. “I’m hoping that with more machine learning, key performance indicators will grow and evolve,” she said.
“I’m trusting we can take a shot at measurements utilizing data science and influence KPIs to work for you instead of you working for them.”
In her vision, machine learning data algorithms deployed in BI software will tell businesses what’s interesting in their historical data analysis.
Right now, data analysts typically have to define metrics for BI tools to track most predictive analysis. It’s a manual process and it only makes visible data the company already knows is important.
But machine learning-enabled BI could delve deeper into a business’ unknown unknowns, finding insights in previously unexamined data.
Taken a step further, AI-powered BI could take advantage of natural language generation capabilities to explain to the business what these insights mean and how they might act on them.
This isn’t precisely a revolutionary thought. BI vendor Sisense announced in April a data discovery component to its software that automatically reviews data and alerts users to new and potentially interesting features.
Other vendors are also adding machine learning components to their software. Data Analyst firm Gartner has been discussing data discovery since at least 2015.
Yet, it wasn’t until the point when this current spring’s Magic Quadrant give an account of BI and data analytics platforms that Gartner announced smart data discovery following disruptor in the BI programming market.
Perceptions Must Change
There is some momentum around the idea of combining machine learning, business intelligence and artificial intelligence. But based on my reporting, the idea seems to have seeped little into the general public’s consciousness. We still mostly see AI and BI as discrete areas.
This has to change. Think of all the time AI-powered BI platforms could free up for data analysts, who currently spend much of their time handling requests for Predictive analysis reports.
They could move on to more effective data science and predictive analytics projects. We’ve seen from companies like Uber and Google that smart approaches to machine learning can create entirely new, profitable business models. That’s the kind of thing people with strong analytics skills should be spending their time on.
Possibly once the buildup encompassing AI begins to wear off the contrasts amongst it and BI will appear to be littler and the open door more self-evident. All things considered, we’re as of now observing AI obviously affecting CRM, HR and FinTech programming. There’s no reason BI shouldn’t be next.
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