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The Rise of the Data Natives

Digital natives program their thermostat. Data natives expect the thermostat to program itself.


Sometime at the start of the decade, YouTube was abuzz with viral videos of small children — yet to speak, read or write — “pinching” magazine articles with their fingers as they would an iPad. These children were heralded as members of a new generation of “digital natives”: People who grew up surrounded by computers, shaped by always-on technology and the Internet.

Today we are witnessing a new revolution, this time of “data natives” who expect their world to be “smart” and seamlessly adapt to them and their taste and habits.

While digital natives were most concerned with what they can do with technology, data natives are more concerned about what that technology can do for them.

  • Digital natives program their thermostat. Data natives expect the thermostat to program itself.
  • Digital natives use the Starbucks mobile app. Data natives want the app to know their favorite drinks — and when to suggest a new one.
  • Digital natives use a cloud-connected baby monitor. Data natives expect their baby monitor to automatically calculate crying percentiles based on millions of other babies.

The data-native revolution is on the rise as the appetite for data-driven products keeps growing stronger. From connected homes to wearables, people expect their lives to be better, richer and easier due to an explosion of networked devices (50 billion of them by 2020, according to Cisco).

Many scenarios that we once dismissed as science fiction are actually happening today.

A Jawbone Up wristband turns on your Philips Hue lights and starts your WeMo-enabled coffeemaker when you wake up. Water heaters and thermostats learn from your usage patterns and save energy. Connected door locks and doorbells make safety more convenient.

But there are frustrating times ahead for data natives.

In a constantly connected, data-rich world, all of our expectations are evolving quickly. Autocomplete and autocorrect are everywhere — and we make fun of them when they don’t work. We’re frustrated when our GPS doesn’t autocomplete, or when it shows us a restaurant 1,000 miles away, and we wonder why it hasn’t already learned our preferred route. We’re offended when LinkedIn recommends a job that’s too junior, or when we feel that TiVo stereotyped our ethnicity. We’re disappointed when ad-supported services don’t know us, amused when Amazon recommends “Dexter” when we’re buying a knife — or not so amused when YouTube plays a “Texas Chainsaw Massacre” ad in front of a cartoon our kids are watching.

We “settle” for self-parking, lane-following cars with adaptive cruise control when what we really want are self-driving ones. Your “smart” watch wakes you up at 3 am to tell you its battery is running low. Your Roomba is gathering dust — and not in a good way. True, these are the prime examples of first-world problems, but it’s fair to ask: If we haven’t yet fixed the small things, how can we be trusted with the innovations that would really enhance all our lives?

The good news is that data natives’ frustration with not-so-smart technology is exactly what will make the promise of “big data” a reality.

This friction is what spurs innovation that’s applicable well beyond tech early adopters. Local governments are hiring data scientists, and open data is increasingly driving public policy. Personalized medicine is coming — but in order to overcome regulatory challenges, better algorithms and better data are not enough. It’s the public’s expectations that will accelerate its pace, and the data natives’ frustration with the fact that we can optimize ads, but not our mood, health and quality of life.

Last month at SXSW, Shaquille O’Neal quipped that he’s becoming an algorithms expert while declaring his passion for wearables. In 2012, Nate Silver became a household name. Data journalism became de rigueur, and storytelling with data is constantly in focus. Chelsea Clinton tweets about her love of data. Data conferences are growing at a rate I would proclaim exponential if I wasn’t a data geek.

You might call this a data bubble, but I call it great news. It’s great news because there’s hope for improved numeracy in the U.S. If adults believe data science is “sexy,” there’s hope for math to be a subject that’s not “cool” to hate as a child.

And that is great news — because the data natives’ expectations can’t be fulfilled without people who not only live and breathe data, but who can also communicate with those who don’t.

We need data scientists, data engineers, data designers, people who “get” both data and the product experience, people who are creative with data and who have empathy for the wider audience that’s now represented and shaped by the data natives. In response to the oft-cited McKinsey Global Institute report anticipating unfulfilled demand for 190,000 big-data positions, data-science education programs have proliferated within traditional universities, MOOCs, large companies and incubator-style training programs.

However, technical skills are not enough to satisfy the data natives’ expectations. The tide is now turning toward making data and sophisticated algorithms invisible. Top-notch design and user interaction, combined with data and algorithms, are what make products feel smart. These two sides of the data-product coin are reinforcing each other, creating a virtual cycle. Easy-to-use devices or software that seamlessly integrates into your life generates higher volume, better-quality data, while personalized experiences enabled by data are big contributors to a product’s ease of use.

Something as basic as fast, easy-to-use autocomplete enables you to quickly get the job done, and in the process generates more data to make the algorithm even better. In turn, autocomplete is what makes the user interaction feel smooth and smart. This change in how we interact with “smart” technology turns out to be crucial to its success.

Despite its early promise, artificial intelligence has made little progress over the past few decades. That’s because demanding or expecting perfection from non-deterministic algorithms is a recipe for disappointment. Why, then, should we believe that “this time it’s different”; that the “big data” hype is more warranted than the “fuzzy logic” hype from a few decades ago? It turns out that keeping the human in the loop is the crucial piece of the puzzle.

A good user interface offsets algorithm shortcomings, and helps technology make the leap from novelty to indispensable.

Humans, after all, are highly adaptable if they can reap the benefits. The Newton’s handwriting recognition was not quite there yet — but Palm came along with Graffiti, standardizing strokes for each letter to hold us over until technology advanced. Siri (or your bank’s customer-service voice-recognition system) might not understand everything you could possibly say — but keeping the domain restricted and guiding the user toward keywords does the job. Yes, this means you have to speak punctuation marks when you dictate your text messages, but while doing so, you’re training the system to infer them in the next version.

While data natives expect technology to be smart, they understand that a little human input goes a long way.

Just like the children playing “iPad” on their magazines, too many of us still experience a parent-to-child relationship with technology — you need to tell it what to do very specifically, and correct often.

Data natives, on the other hand, are working toward a more grown-up relationship with technology — a technology that anticipates your needs while requiring little input beyond passive observation. This is the holy grail of artificial intelligence — and it will be here sooner than we think.

Monica Rogati is vice president of data at Jawbone. Reach her @mrogati.

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