IIeX North America is the home of research innovation and start-ups. Even before its move to Texas, the conference was Wild West territory, full of frontier thinking and claim-staking. It makes the implicit bargain that attendees will get the latest thinking – but it’s up to them to sort out striking oil from snake oil. Everyone at this event is placing bets on the future. They can’t all pay off.
If I was summing up the conference in one sentence, it would be this. The data revolution happened – now what the hell do we do? The big questions weren’t about collecting data or finding new sources, they were about managing, analyzing, simplifying and communicating it. Here are five themes I spotted.
Democratized Machine Learning is Tantalizingly Close
It’s safe to say the average insight professional has looked at the rise of machine learning tools with mixed emotions. Are they all they’re cracked up to be? How do we use them? Are data scientists going to steal my job? (Shortly before the robots do.)
What’s been clear is that firms which can genuinely democratize machine learning – by creating tools non-data scientists can use to build active learning models for analysis – are going to be big players in the near future. That moment is almost here. At IIeX we saw several providers who promise to take the hard labor out of machine learning, for example by supplying pre-built models to adapt, rather than build new ones from scratch.
Be Transparent Now (Before You’re Made to be)
Off-the-shelf machine learning can give researchers great power. As any Spider-fan knows, there’s another side to that equation. Questions of great responsibility, morality, and ethics kept cropping up at IIeX.
In one of the week’s best talks, Andrew Konya of Remesh broke down the moral hazards of AI-powered research. Researchers handling large amounts of passive data will also find themselves handling tough metaphysical questions. When you can observe anything, how do you establish consent? When you can influence behavior effectively, what behavior is healthy? When you can train machines to look for truth, how do you define that?
No Data is an Island
As big brands start using AI methodology more, there’s been a shift in tone. AI is only as good as the data and model it uses – as System1 alumnus Alex Hunt of PRS InVivo put it, without good data your insights are artificial, not intelligent. But brands work with multi-source data, so the power of new technology for data synthesis is back on the agenda. Machine-assisted research needs to be broad, not just deep.
Some of the most impressive presentations were from companies like the UK’s Black Swan, pulling together multi-source data for analysis, and working out how to value each source to ensure machine analysis is spotting real patterns, not mirages.
One unintended consequence of all this is a revival of qual, as you need a strong source of baseline insights to begin asking the right questions – I don’t think I’ve ever seen so many excellent qual presentations at an IIeX event. Shout out to Labbrand and Problem Child for their brilliant ideas about ritual and post-truth research, respectively.
Simplify and Energize
The data synthesis trend is being driven by research client needs – Microsoft, in particular, were engaged and candid about their “factory floor” for collecting and analyzing data. But, as they pointed out, you have to go further, and create energy around the data by making the outputs simpler, more engaging, and more energizing for your stakeholders.
This can take the form of democratizing analytics within an organization – the approach Microsoft is taking – but it can also take the form of fresher outputs and more user-friendly communication. Research podcast guru Jamin Brazil preached the virtues of an internal podcast. Debutant Hannibal Brooks reminded us that simplicity and familiarity via metaphor have always been how innovations spread.
We all have a hand in shaping how technology helps us do what we do.
System 1, for Everyone
You might be forgiven for thinking IIeX was all about machines. Really, though, it’s all about humans – those tricky creatures we’re asking the machines to understand.
So it was heartening to see, once again, so much talk about System 1, behavioral science, implicit decision making, and the power of emotion. Understanding how people really make decisions is fundamental to sorting good data and useful insights from the bad.
You can’t stop at understanding, though – you have to embed the ideas into an organization to drive outcomes. That’s what our IIeX panel was about – with huge thanks to Derrick Elsea from Microsoft and Kathryn Grater from Kimberly Clark, who talked about their organizational and personal journeys with behavioral science. It’s a quote from Kathryn in that panel which sums up IIeX as a whole, and its fusion of technology and the human: “We all have a hand in shaping how technology helps us do what we do.”
This article was originally published by System1.