In 1999, the Mars Climate Orbiter unexpectedly burned up in the Martian atmosphere.
Intended to be the first weather observer on another world, the $125 million satellite was just about to slip into a stable orbit around Mars when it vanished completely — never to be recovered.
In the months following, a NASA investigation team discovered the issue. The software that controlled machine’s thruster force was calculating in pounds. The software that took in the data calculated in newtons.
From botched space explorations to the financial downfall of some of the world’s most successful organizations, data gone wrong is disruptive, disastrous and, sometimes, even dangerous.
It’s time-consuming: data scientists spend up to 80 percent of their time sifting through disorganized information.
It’s expensive: around $3.1 trillion dollars annually in the US alone.
It decreases trust: 27 percent of business leaders doubt their digital inputs.
Still, 88 percent of business leaders report a greater urgency to invest in big data and AI. And, 55 percent of companies say that their 2019 investments in big data and AI now exceed $50 million, up 40 percent from just a year ago.
In a market obsessed with amassing more information, data runs the risk of becoming a technical taskmaster that disrupts productivity, innovation, and creativity.
“Data by itself is at best meaningless, and at its worse, misleading,” writes Microsoft principal Bill Pardi.
But it doesn’t have to be.
At my company, Jotform, we treat information as an opportunity to optimize human potential. We try to use data as a springboard from which to challenge our assumptions, develop insights and, ultimately, inform action.
In today’s analytics obsessed marketplace, this sometimes means swimming upstream.
But as history has consistently shown us, progress doesn’t come from following the pack.
Challenging assumptions: avoiding information bias
In a TED talk entitled “The Human Insight Missing from Big Data,” ethnographer Tricia Wang recalls a time when Nokia’s executives ignored qualitative insights that could have changed the company’s future.
During a randomized, 2009 study with 100 participants, Wang had observed that the average Chinese consumer was ready to shift to a smartphone. Company executives, in contrast, had several million data points telling them otherwise. Valuing quantifiable information over qualitative insight, they ignored her findings.
Soon after, iPhones began to infiltrate China, and Nokia rapidly lost market share.
“What is measurable isn’t the same as what is valuable,” Wang told the Nokia team.
It’s easy to think that more data is better. But, as a CEO, I seek to avoid big mistakes by noticing (and negating) my own information bias — and by inviting my colleagues to do the same.
Releasing our notion of data as a fixed quantitative model or a set of statistics doesn’t always come naturally. But, by opening ourselves to all kinds of information, we remain aware that innovations — both big and small — almost always come from unexpected places.
When we position ourselves as perpetual students of the culture and consumers we serve, we allow both to challenge and change us on an ongoing basis.
Developing insights: connecting analytics to competitive advantage
Forrester executives were baffled. Despite growing investments in big data, the companies they surveyed had become less enthusiastic about business analytics. In fact, their reported satisfaction rates dropped 21 percent between 2014 and 2015.
“We found that while 74% of firms say they want to be ‘data-driven,’ only 29% say they are good at connecting analytics to action,” Forrester Vice President Brian Hopkins wrote soon afterward.
Today, businesses like Netflix, Uber, Facebook, and Amazon know that actionable insight always trumps raw information. They operationalize this knowledge by processing data differently than other companies.
First, they focus: zeroing in on information that furthers their competitive advantage.
Second, Hopkins writes, they gradually integrate data into systems of insight, instead of trying to make it immediately actionable.
Third, they consistently look for ways to value human learning over machine learning, by applying employee observations to their algorithms.
“It’s not about the ingredients, it’s about the cook.” Microsoft’s Pardi explains.
I’ve written before about Dollar Shave Club’s great business motto: “We don’t respond to situations; we respond to people.”
From focus groups and formal research interviews to surveys, review platforms, and feedback or review invitations, at Jotform, we’ve discovered that information is only as valuable as the real, human insights it enables us to develop.
This approach may take more analysis upfront. But, last year, our commitment to the value of our insights over the volume of our information informed the company’s biggest release to date — our PDF Editor.
Informing action: data-driven creativity
Piper Kerman first stole our hearts in 2013, when she debuted as an unlikely criminal in the Netflix series, Orange is the New Black.
But Orange Is the New Black is more than just great television; it’s also an example of data-driven creativity in action.
There’s a growing fear that data might limit our potential to push the boundaries of entertainment and art. But, Netflix proved its potential to do the opposite.
Instead of using data to influence what people watch, Netflix focused on gathering data about their users’ preferences, which ultimately influenced who tuned in. Orange is the New Black succeeded because Netflix marketed the series to people they knew would love it.
“Netflix’s advantage wasn’t knowing there was a critical mass of fans of women-led ensembles or of creator Jenji Kohan’s previous hit, Weeds,” data and entertainment experts Michael D.Smith and Rahul Telang explain in Harvard Business Review.
“Netflix’s competitive edge was knowing exactly who those fans were as individuals and being able to serve the show directly to them.”
But data-driven creativity is about more than content placement. If we put data in its proper place — to inspire, instead of instruct, ideation — veteran art director Matt Vescovo says it can be a powerful force for new ideas.
“Any good creative person has a good back and forth between their right brain and their left brain,” he explains. “The right brain comes up with the creative idea and then the left-brain fact checks it. I’ll come with an idea, and say ‘Do other people think that, or just me? How many times have I seen this? How many times have others mentioned it to me?’ The data side of my brain keeps the right side of my brain honest.”
Creativity doesn’t happen in a vacuum. It’s the process of re-arranging insight, observations, information, and ideas in ways that surprise, delight and, sometimes, even challenge and change us.
“In some scenarios, the data from your head can suffice,” Greg Weinstein, an audience development and content marketing specialist, writes in Adweek. “At other times, data from a spreadsheet will help.”
It’s what you do with data that counts. And, that’s where we come in — as creatives, strategists, technologists, founders and yes, even data scientists.
Collectively, we can commit to questioning what we think we know. We can value insight over information. We can take the time to ensure that our data drives interesting outputs, instead of dominating them. And, together, can further human creativity and innovation, and change the world in ways that numbers alone never could.
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