“Our bodies break / And the blood just spills and spills / And here we sit debating math.”

—Retribution Gospel Choir, Breaker

Design got its seat at the table, which is good because we can shut up about it now. What used to be seen as the territory of bespectacled Scandinavians is now a matter of HBR covers, consumer clamour, and 12-figure market caps. People in suits now talk about design as a way to differentiate products and unlock new markets.

The table is a metaphor for influence, of course. Designers already have plenty of tactical influence – interface, layout, structure and all that – but this is influence of a different order. It is deep and internal: influence over culture, vision, and most of all strategy, the art of deciding where to go and how to get there.

In this realm, data is king. Whether from device sensors, social media chatter, or experiment analytics, data pours off every surface of the modern world, and people are happy to sell us expensive tools to analyse it.

Data has transformed strategy across many industries. Sports fans and insiders alike have become trainspotters: the minutiae of Moneyball, of take-on percentages and suspension loads are now mundane. Evidence-based medicine has put empiricism at the heart of the profession, with randomised controlled trials guiding new treatments and in some cases reducing mortality.

But outcomes are only half the story. Much of the appeal of this datafication is ideological.

“Quantified thinking is the dominant ideology of contemporary life: not just in scientific and computational domains but in government policy, social relations and individual identity.”

—James Bridle, What’s Wrong with Big Data?

The tech industry believes itself to be neutral and objective. This is pure self-delusion. Ideology runs hot through the veins of the sector. So blown are we by the winds of the New, it takes just weeks for a prevailing zephyr to align all ships in the same direction.

Today’s dominant tech ideology is Lean Startup, a California-ised nephew of Lean Manufacturing. The family resemblance comes in the elimination of wasteful work that fails to meet customer needs. So far, so obvious. In practice Lean Startup almost exclusively manifests as accelerated empiricism.

Lean Startup’s central tenet is that we’re surrounded by unparalleled uncertainty, to the extent that accurate forecasting is impossible. Therefore, adherents claim, the only worthwhile way to build is through stepwise iteration, in a perpetual cycle of Build-Measure-Learn. The notions of intuition and prediction are negated, deprecated by data.

I’m not convinced by the presumption. Certainly the tech industry operates amid flux, but the wide-angle view of this change is more predictable than many would admit. Bill Buxton famously claimed consumer tech has a 30-year ramp-up, pointing to the mouse and the touchscreen, first prototyped in R&D circles in the mid-1960s. Even the Gartner Hype Cycle, tacky as it is, offers a plausible model of trajectory and velocity for emerging technology. With intelligent extrapolation and study, the next five years of technology is hardly a mystery. The second-order and social impacts are murkier, true, but here a spot of science fiction scrutiny and primary research surely isn’t beyond us.

But the message is out of vogue, and a posteriori empiricism is in the ascendancy. So datafication it is, and with a narrow view of data at that. In Lean Startup as now practised, data is first and foremost quantitative, usually gained from user analytics and multivariate experiments.

I’ve studied a good deal of mathematics and statistics, and know the power of quant data. But I also know its limitations, and have seen first-hand the dangers of data ideologies excluding other decision-making inputs.

Scenario 1 – experimentation trumps coherence

I’ve worked with two companies where the primary product strategy has been reducible to “Increase this KPI”. The same sorry tale has panned out in both.

At the start, things look positive. Per executive edict, employees concoct product experiments to move the needle. Pace of execution goes up, pet projects ship, and people are pleased at the rapid throughput and product change. Sometimes the measure does indeed move, and from a distance it certainly looks like innovation.

But almost all these experiments are additive, so the interface gets crammed. White space is eroded by buttons and info. Successful A/B trials ship to 100% regardless of coherence and intent. The product slowly becomes cluttered and the value proposition becomes incoherent. Secondary metrics that lie outside the scope of the experiments, such as retention or NPS, start to plateau, then slip.

Worse, the internal framing of users shifts. Employees start to see their users not as raison d’être but as subjects, as means to hit targets. People become masses, and in the vacuum of values and vision, unethical design is the natural result. Anything that moves the needle is fair game: no one is willing to argue with data.

PMs and engineers decide that since they can ship pretty much whatever they like, they bypass what they see as designers’ obstructive, oversensitive tendencies. Deployment authority becomes the ultimate power, design morale plummets, and designers quit. This proves to be a leading indicator of company morale, and general confidence in leadership sags shortly after. Failure to provide a strategic North Star is itself an absence of leadership; a timid disavowal of responsibility for direction. So the short-term happiness soon fades, and the breakdown of collaboration and strategic coherence proves hard to reverse. Usually you have to sack an exec or two.

Scenario 2 – Safety dominates