This time last year, a fascinating report from venture capital firm Work-Bench assessed the future of enterprise software.
This time last year, a fascinating report from venture capital firm Work-Bench assessed the future of enterprise software. The study argued that the largest technology companies were winning at artificial intelligence (AI) – not surprising, given they can hoover up all the best talent – but more importantly, business intelligence (BI) vendors wanting to ensure their future going forward needed to beef up their skillsets with AI. “Expect all modern BI vendors to release an AutoML product or buy a startup by the end of next year,” the report noted.
While that may not be entirely true, one company which is certainly ahead of the curve in this instance is data analytics platform provider Qlik. The company is focusing on what it calls ‘augmented intelligence’ – of which more shortly – with Qlik Insight Bot focusing on AI-powered conversational analytics.
At the forefront of this is Elif Tutuk (above), Qlik’s head of research. Ahead of her appearance at AI & Big Data Expo in Silicon Valley in November, AI News caught up with Tutuk to discuss Qlik’s research initiatives and concerns around artificial intelligence among others.
AI News: Hi Elif. What research initiatives are you and your team currently working on and how is this benefiting both Qlik and the wider industry at large?
Elif Tutuk: The Qlik Research team currently focuses on ‘augmented intelligence.’ Augmented intelligence is an approach that brings together the best of machine intelligence and human intuition to speed up time-to-insight, surface new and unexpected discoveries, and drive data literacy for users in any role and at any skill level.
These innovations lead new technologies that create a multiplier effect, where the human-machine collaboration outpaces anything either the human or the machine could do on its own. They help to decrease bias in human decisions and ultimately provide more adoption of analytics and the insights they provide.
AI: You wrote a piece last month exploring the issues with regards to bias in AI. What other concerns do you see organisations facing and encountering when they try to introduce AI/ML/automation into their workflows?
ET: There are niche applications for artificial intelligence that rely completely on machine automation, but more complex business problems require interaction and human capabilities. At Qlik, we do not believe that the ultimate purpose of the AI is to replace the user-driven analysis with a black box approach. Instead we argue that machine intelligence and automation must be combined with human-centred analysis. In this way, all users, even if with different roles and abilities, have access to data literacy.
A new school and culture is therefore needed to increase data literacy. Data literacy is the ability to read, work, analyse and discuss with data. It enables people to correctly interpret analytics results and insights generated by the machine.
AI: Which areas do businesses need to concentrate on first with AI/ML initiatives? Are there any quick wins available short-term?
ET: One of the biggest challenges that companies are facing in this fourth industrial revolution, as I mentioned before, is the lack of data-literate talent. The Data Literacy Index compiled by Qlik shows how the leaders of large global companies almost unanimously believe that it is important to have data-literate employees – this is perhaps not surprising, given that having data literate employees increases corporate value by 3%-5%. Two thirds plan to implement data literacy in the company.
AI: One of your speaking sessions at AI & Big Data Expo is around the third generation of business intelligence (BI) and how BI is evolving with AI. Aside from big data, what other technologies do you see converging with AI and why?
ET: AI will push forward the ideas of transparency, of seamless interaction with devices and information, making everything personalised and easy to use. We will be able to harness that sensor data and put it into an actionable form, at the moment when we need to make a decision.
AI: Are BI vendors more generally at serious risk of being left behind if they don’t embrace AI technologies quickly?
ET: Augmented intelligence and applications of AI drive less-biased decisions and more impartial contextual awareness, transforming how users interface with data, make decisions, and act on insights. It expands who has access to insights from analytics by delivering analytics anywhere and to everyone in the organisation, and does so with less time, skill and interpretation bias than current manual approaches. The goal of BI is to enable fact-based decisions – and the application of AI is a key enabler for that.
AI: What message are you looking to get across to the audience from your sessions? What are you hoping they will take away from your talk?
ET: During my session, I will talk about how artificial intelligence is not purely ‘artificial’, and about the ideal approach amplifying human intuition with machine intelligence to create a powerful multiplier effect. I will use analogies between how the brain processes information and data analytics and talk about the need for new data structure approaches to decrease bias in AI. The audience will also learn about what to consider when evaluating AI in analytics.