Data Universe has named tech veteran Alistair Croll as its Conference Chair.  Croll will lead an exceptional network of global experts from different areas of the data space to provide advice and guidance on our conference program, topics and event experience.

As co-author of Lean Analytics, Croll has become a noted expert in the data field and how to apply data analytics in a business environment. He is a veteran of the tech and data conference space, having founded or served as chair of education-led events in multiple countries. Data Universe sat down with Croll for a conversation about the most important topics in the data space and how Data Universe will be addressing them at the event next April.

Data Universe: One of the founding principles of Data Universe is using data for good. From your perspective, how do we use data to make society better?

Alistair Cook: By thinking critically about it. When things become cheap and abundant, we find unexpected new uses for them. For decades, data was precious. But the big data revolution was really about lowering the cost of processing huge volumes of data with great variety very quickly. And AI is accelerating that.

So the answer to making society better is to think creatively about what is now possible. There are many things in society, from government to business to healthcare that are built on folklore, rules that were in place in a world where data was costly, that are no longer valid. We have to question the folklore of scarce data and identify unintended consequences using data.

DU: Is there a challenge or an issue in the data ecosystem that's engaging you most right now?

AC: The obvious question on everyone's mind is how will artificial intelligence impact data science? You now have the ability to search through things and look for correlations at a scale that is unprecedented, so you can surface hypothesis very quickly. Then you can devise tests to find out whether the correlations are in fact causal, getting you to answers way more quickly.

So that's what I'm really interested in: the ability to for AI to surface new hypothetical correlations and classify previously qualitative information and what that means for finding advantage, optimizing systems, identifying fraud, all those kinds of things.

DU: AI is a popular answer to that question. Is there any anything out there that we're not paying enough attention to because all of the discussion is about AI?

AC: We're not paying attention to human nature and how tech advances are going to affect our behavior. We crave fatty foods, sex, the approval of our peers, etc., because those are things that helped us to survive in the caves. When we create systems that make something really easy, it is the natural state of humans to gravitate towards those things. We've assumed a bunch of things about the friction of actions in the world and built political processes and business processes around them. We have not spent enough time thinking about when something that used to be hard is now effortless. People who relied on effort as a proxy for quality are going to get screwed over.

DU: What do you think about the state of data expertise or literacy in the average organization. Are employees equipped to leverage the technology and tools that are currently available to collect, manage, analyze and draw insights from data?

AC: I think you have to go back to the business model of the organization. For companies dealing mostly in the physical world, the best you can hope for is optimization. Where's the manufacturing line getting broken? How can I use AI to inspect products so that there's a higher quality rate? They are mostly ready to adequately leverage data.

If the organization traffics in information, I think they have generational transformation problems. There are people who need to retire not only because they can't get it, but because their current job doesn't allow it. Most organizations are unwilling or unable to recognize the obsolete among them, partly because of unionization, partly because of traditionality. I think that people are not being honest with themselves about the breadth of their work and how much of their work simply involves moving information from one place to another. Getting teams to where they need to be will involve hiring, replacing, upskilling and automating.

DU: As Conference Chair for Data Universe, what value are you hoping attendees walk away with?

AC: For the curious, there’s a stage called Tech at the Fringe that will feature a lot of cool stuff. For the fearful, you will learn enough about AI and how it will impact your life to allay that fear. If you’re in business you’ll learn how to transform this data asset you have into actual value. Understanding how to think about your data as a foundation rather than like the byproduct of your business is super important.

I would say also one last thing about this that I hope we can communicate effectively at Data Universe. For the last 50 years, we've been building an IT stack—compute, storage, networking, processing, code, QA, all those things. We're about to build a second stack. Computing today is deterministic, it’s predictable. There is a second kind of computing called non-deterministic that is emerging. I put data in and what comes out is unpredictable. They have entirely different compute structures. That's freaky, but organizations are going to have to build a second stack for data and machine learning that exists alongside the traditional computational stack that people are used to.

So I think people will come and have their eyes opened about how to engineer, how to build, how to maintain, how to scale both of their data stacks—deterministic and non-deterministic.