The Rise of the Data Natives

Monica Rogati
4 min readMay 17, 2016


A longer version of this article appeared in Recode two years ago, before Amazon Echo, Tesla Autopilot, Google Photos and the AI hype. Since then, it has inspired a conference that is now a yearly event, and we’re all becoming data natives (or at least really well-assimilated data immigrants).

A few years ago, YouTube was abuzz with viral videos of toddlers pinching magazines 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.

We are now witnessing a new revolution — that of data natives who expect their world to be “smart” and seamlessly adapt to their taste and habits.

While digital natives ask what they can do with technology, data natives are more concerned about what technology can do for them. It’s not enough for a magazine to be digital and responsive; it should be personalized and context-sensitive.

  • 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.
Autocorrect miss on a tweet featuring “the SCIENCE guy”, “US Chief Data SCIENTIST”, and “learning some sconce”, a phrase with zero Google hits (vs. 12K for “learning some science”). We’re not there yet.

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 — so we’ve replaced it with Google Maps or Waze. Now, we wonder why it hasn’t already learned our preferred route.

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. We sigh and repeat “operator” when the cheery phone bot is telling us to state our problem again. True, these are 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 artificial intelligence a reality.

Technical skills are crucial, but they 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.

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. 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. We’ll get there, albeit after suffering through a few more years of Clippy 2.0, DeepClippy and ClippyBot.



Monica Rogati

Data Science advisor. Turning data into products and stories. Former VP of Data @Jawbone & @LinkedIn data scientist. Equity partner @DCVC. CMU CS PhD.