by Julia Rubinic

When Hurricane Irene arrived in NYC, and my anxiety shifted quickly to boredom, I stopped following the barely damp reporters on CNN and picked up James’ Gleik’s The Information to reread the chapter on the telegraph. Makes sense, right? It does when you understand how dependent the establishment of meteorology as a science was on the weather patterns captured, for the first time, thanks to the telegraph.

Before the telegraph, weather was local and myopic. Bad weather was forecast by bleating animals, neighbors’ best guesses and farmers with sensitive bones. As the telegraph grew in popularity in the middle of the 19th century, people began to communicate weather from town to town, and relationships began to emerge. A rainy day in Edinburgh on Sunday often meant a rainy day in Glasgow on Monday. A choppy sea outside Calais meant a Basque fisherman should stay anchored the next day. All great winds are “highly curved.” Weather follows patterns. For the first time, local communities realized they experienced weather within a larger context, and they began to use patterns to understand, forecast and, ultimately, make decisions about their world. In 1854 the British government founded the Meteorological Office in the Board of Trade, establishing the practice and science of studying weather.

This new science, and eventually profession, took on the role of interpreter. New methods of gathering data were invented, communities were informed and personal and business decisions were made. This “interpreter / audience” model continues today, as I understood all too well as I visited site after site desperately seeking information on Irene’s progress. But this model feels outdated, and it’s not just about weather.

As marketers we look at patterns all the time, especially online. We model clickstream data, search trends and advertising metrics to understand how people navigate the internet. We examine how people participate by measuring buzz and analyzing user-generated content. We explore trends over time, including site visits, purchase behaviors and twitter topics.

We sometimes use this data to provide a service or utility, such as advertising targeted to interests. Or recommendations based on past behaviors. Or content tailored to preferences. At times we have to convince ourselves this is a service or utility. We’re controlling and interpreting the data behind the scenes, and occasionally offering a nugget of usefulness.  (In the excellent TED talk by Eli Pariser “Beware Online Filter Bubbles,” he makes the case that even those nuggets have gone too far in removing control from the individuals directly affected.)

All of this might be fine, if this were still the 19th century. But our audiences are more data savvy. And we’re missing an opportunity. In the best cases we’re figuring out ways to put data in the hands of our customers, along with the tools to help surface the utility. Somewhere between data tables and recommendation engines, there are applications that enable users to manipulate data themselves to serve their unique needs.

Travel sites like Bing’s are starting to help customers make decisions based on patterns in historical data, such as cheapest dates to fly. This is a vast improvement over the stale filters producing identical results across dominant travel sites. Malcolm Gladwell demonstratesthe use of a simple tool to upend the way we evaluate top law schools, showing that the addition of value as a criteria results in the Universities of Alabama, Virginia and Colorado knocking some Ivy League institutions out of the top ten.  Revised criteria allow one to rank based on everything from rate of employment after graduation to the availability of Tibetan food within 600 miles. Open data projects are allowing developers to mash up separate tables into coherent and useful patterns, such as maps combining the locations of bars and late night crimes.

Law School Ranking

New criteria for ranking law schools, from reputation to availability of Tibetan food.

The opportunities are endless. I know data exists to respond to very basic questions posed by my friends and family, but in most cases the answers are beyond our reach. What are people saying about my friend’s restaurant, and how it compares to others, beyond reviews posted on Yelp? Which neighborhoods are best for renting an apartment based on my cousin’s desire to be close to a park, near restaurants AND have 2 beds/1 bath? How can the weight loss experiences of others guide my uncle to a program that’s just right for his body type, activity level and stubborn personality?

Companies should have faith in the skills and energy of our customers, and provide platforms for self-guided data discovery. They’re working the system anyway. Just ask anyone the number of steps they go through online before booking a vacation, and the tips and tricks they’ve evolved to try to get around the popular sites’ limitations. Our role shifts from interpreter to provider, and our value is in providing the layer that simplifies data manipulation, cedes control back to the user and enables confident, personalized decisions.