The Future of BI Is AI, Says Qlik CEO Capone

(VideoFlow/Shutterstock)

The future of business intelligence will be defined by how well AI and data science capabilities can be integrated into traditional analytic and decision-support tools that can impact the business at the speed of data generation, Qlik CEO Mike Capone tells Datanami in a recent interview.

While there will always be certain high-value use cases that rely on the extensive skillsets carried by PhD.-level data scientists, the amount of data and the opportunity to act upon it is just too great today to rely on a limited number of people to create business value from it, according to Capone, a technology business veteran who has headed Qlik since 2018.

“It’s always been this thing where the white coat data scientist are on the side,” Capone say. “You take the data, you give it to them, they look at some stuff, and then somehow or other it might get fed back into your core analytics.”.

Data and business move too fast today to have that sort of workflow. Like most BI vendors, Qlik is moving to integrate AI and machine learning capabilities directly into its product line, which will give business analysts the benefits of pattern and anomaly detection that only a machine can provide. All of Qlik’s major competitors are adopting AI and machine learning, according to a 2020 report by Gartner–not only for the ability to spot patterns and anomalies, but also to help prepare data and explain findings.

There’s a lot of benefit that can come from turning business analysts into citizen data scientists, Capone says.

“What we’re seeing and what we’re doing actually,” he says, “is we’re building out AutoML and AI capabilities inside of our analytics platform to really allow the average kind of BI user to be able to take advantage of AI and ML in kind of everyday analytics work and then taking that and bringing it and mainlining that into a normal business processes.”

AI and ML is being embedded into BI products (YAKOBCHUK-VIACHESLAV/Shutterstock)

Qlik, which is owned by the private equity firm Thoma Bravo, acquired AutoML functionality with a September 2021acquisition of Big Squid, a data science startup that was listed as a challenger in a 2019 roundup of AutoML vendors by Forrester. The Qlik AutoML functionality, which is being rapidly adopted according to a Qlik press release issued today, allows users to explore predictive models and test what-if scenarios. If the situation warrants it, the models can also trigger automated responses too.

It’s all about enabling quicker business decisions based on real-time and predictions about future states, Capone says. That’s not something that could be done using just SQL queries and backward-looking BI interfaces to query about what happened in the past.

“Locking insights up, in dashboards or reports, is really just not how business is done,” Capone says. “Business is done in in real time now, and the ability to actually develop insights in real time–learn from data in real time, but then take action on those insights in real time–is really what matters. And that’s what we’re trying to do, is bring all that together.”

For example, the food service company Aramark uses Qlik products to get in front of changing situations, Capone says. If demand for beer or hot dogs is up at Lincoln Financial Field (where the Philadelphia Eagles play), Qlik AutoML can detect that signal and automatically take action, without an Aramark employee having to lift a finger.

“What they’re doing is they’re analyzing that data in real time, understanding what implications are of a blowout score–or we’re running out of beer, [so] get beer trucks there–and then making decisions,” Capone says. “There’s nobody staring at a dashboard because the algorithms have already been trained to be able to make decisions and then push data back to the point of sale system. So drop the price of hot dogs because we’re going to have too many. That’s the modern data analytics platform.”

Aramark uses real-time data feeds and AI from Qlik AutoML to make adjustments in food delivery at Lincoln Financial Field (Andriy Blokhin/Shutterstock)

Optimizing the price of beer and hot dogs may not sound like the highest and best use of machine learning and AI. That’s one view. But seen from another angle, it’s an indicator that business leaders are ready to take AI and machine learning out of the ivory tower and get them into the field where they can have real world impact.

Capone’s view on the great potential of citizen data science is not shared by everybody. There have been concerns that citizen data scientists don’t have the necessary training to build predictive models that reliably give the right answers, reflect ethical values, and don’t incorporate bias.

“These essential, mission-critical–often regulated–models cannot, and should not, be created by anyone other than professional data scientists for the same reason that a hospital should not be staffed with ‘citizen surgeons,’ airlines should not rely on ‘citizen pilots,’ towers should not be built by ‘citizen architects,’ and your C-suite should not consist of ‘citizen managers,’” writes Kjell Carlsson, a former Forrester analyst who is now head of data science strategy and evangelism at Domino Data Lab.

But Capone is adamant that the potential good that comes from a more democratic sharing of data science capabilities with business analysts will be greater than any harm brought by the dilution of “pure” data science.

“Don’t get me wrong: Once in a while, you’re going to need to take data, run some R and Python code on it, and analyze it that way,” Capone says. “I’m not making some claim that data science in its purest form for really high-end use case is going to go away.  What I am saying is more and more of that [data science] is going to be brought back into your core analytics infrastructure. And then you save the data scientist for the really hard, complex problems, not the day-to-day problems.”

All of this is assuming that the data is in a clean, centralized, and useful format in the first place, which is often too big of an assumption to make. For many of Qlik’s customers, extensive work has already been done to integrate and normalize the data, usually in a cloud data warehouse, Capone says.

“Most companies have done a ton of work to get their data curated into an analytics platform,” he says. “Look, you’ve got these terrific modern kind of cloud data platforms like Snowflake or [AWS] Redshift, and so all the data is there. So it’s really horrible to have to take the data out of that, give it to some data scientists off to the side so they can go run some code against it. Why not just do all that there?”

Qlik CEO Mike Capone wants to democratize AI

In addition to selling BI and analytics tools–and AutoML capabilities, as we’ve learned–Qlik also has a line of data integration products. Some customers, such as Urban Outfitters, rely exclusively on Qlik’s data integration tools, while other customers use a mix of products.

And as it does with machine learning and AI, Qlik has a hard time separating data integration from analytics. “Our firm belief is that data integration and analytics is one thing. It’s a continuum. It is one thing,” Capone says. “And we’ve built our platform to actually take it end-to-end.”

It’s possible to get too caught up in following the latest-greatest technologies and religiously applying them where the experts prescribe, but Capone doesn’t seem to be susceptible to that foible. He fondly recalls his time as a technology exec at payroll giant ADP and how the company used the IBM iSeries midrange server–since renamed the IBM i server but still dismissed by some technology purists as a big iron relic–to great business success.

“When I was at ADP, I’d love those things. I’d use those all the time,” he says. “I paid 30 million people every payday on that kind of technology. It’s a workhorse, man. It’s terrific.”

Capone takes a similarly pragmatic view on machine learning and AI, which so far hasn’t delivered the same type of business value as that workhorse IBM machine.

“There was a time way back–you were part of it, I was part of it–where there was overhype of AI,” he says. “There were some companies that committed a lot of grief by overhyping it. ‘We’re going to cure cancer, we’re going to be better than doctors.’ That stuff was bad for all of us. But now it’s kind of been resurrected in a much more practical format.”

As the initial hype around AI and machine learning fades, it opens up opportunities for Qlik and other BI vendors to find more practical uses of it. Whether it’s spotting anomalies in the data or natural language question-and-answering, the experimentation with AI is likely to benefit everybody in the long run.

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