Wednesday, August 1, 2012

The Data Revolution #3: Big Data

How big data will drive our social, interconnected world


The data that we're producing from our obsession with self-tracking isn't just useful to ourselves. After all, no man is an island. The information about our daily lives impacts and influences our social network. Combined with data from the masses, it can also influence how the systems that we live in work. And when used and interpreted in the right context, our data can even change the world...

"The Data Revolution Series" delves into the issues and intricacies of our increasingly data-driven lives, from tracking our everyday behaviour to contributing data points for the social good. Based on insights and commentary from technology journalist Nora Young's (@nora3000) first book, "The Virtual Self: How our Digital Lives are Altering the World Around Us", we'll take a look at the pros and cons of the changing data landscape. This third post in the series will cover the impact of our data on people and our planet.



The Social Data Map


Our individual digital Data Maps aren't isolated from those around us. In fact, that couldn't be farther from the truth. The share-ability of our data, combined with the power and connections within our social network, means that our Data Maps are as social as ever. As Young puts it, "Our relationship to data has changed dramatically, and self-tracking has gone from being an individual, personal practice, shared only with confidants, to being one that is radically social. Social media has made us comfortable being public with personal information."

Not only has it become comfortable and natural to share our data with those in our social network, it has become insatiably rewarding, also. Every broadcast of our activities provides an opportunity for feedback and reinforcement from our peers. Think of the feeling when your friends "Like" your status updates on Facebook and when your tweets are retweeted on Twitter. "Being admired by others, getting attention from others, getting feedback from others, those things are endlessly rewarding." This feeling only makes us want to share our data more.

The Social Data Map isn't just about sharing our activities with others, of course. It's about having our friends activities shared with us, also. With everyone's data flowing back and forth within their social networks, we always have information on what our friends and connections are doing. "Social-media status updates,” Young writes, causes a 'ambient awareness'—"We are passively, peripherally aware of what out friends and acquaintances are up to."



The Collective Data Map


Moving beyond our social networks, the data that we produce has even more value when it's combined with that of everyone else. That's because looking at the data that is produced by a large number of individuals in aggregate can reveal insights about group behaviour, cities, and even society as a whole. This gives rise to the idea of the “collective”, what Young specifically refers to as the "emergent intelligence of the collective" and the "shared, collective experiences" that we have had. "Individual content, whether that’s status updates, opinions, distances run, or beverages consumed, can be aggregated, and used for purposes other than its original intent."

What kind of purposes? The kind that helps and informs society as a whole. Take Google Flu Trends, for example. Combining information about online searches for terms related to the flu with their location, Google can make a rough prediction of where flu outbreaks have happened, before they are reported. This Collective Data Map is "a constantly evolving, near real-time digital version of the world around us,” which provides us to shift and change our own behaviour for the betterment of society as a whole.

The information that the collective is generating in the digital realm can even influence the physical world. "With the addition of location-based information, we are turning our direct experience not just into data but into an actual map," Young writes, "a graphical representation of our actions and experiences.” Take real-time traffic data, for example. Google Maps can track traffic congestion and provide drivers with better routes while they're on the road. When this data is used to change and improve the efficiency of cities, it can transform the way we move about and experience the world around us.



Big Data, Big Issues


It isn't all clear as crystal, however. There are several issues that must be overcome before Big Data can truly bring about the "Data Revolution" that it's capable of. The first is the reliability of the Collective Data Map itself. Is the aggregate data that is currently being collected by those that are self-tracking truly representative of society as a whole? Is it a reliable way of making city-wide conclusions, or does it just reflect the activities and opinions of a small group of tech-savvy individuals?

Secondly, there is a tendency among many an amateur data analyst to look for correlations and patterns among the aggregate data that is available. But correlation, as the saying goes, isn't the cause. Patterns between different data sets often have no relation to each other, and since data is collected in isolation (and not as part of an experiment), it can be difficult to determine the cause of the patterns that we see. Without clearly establishing causation, big, city-changing decisions can be next to impossible to make.

Finally, there is the issue of objectivity. Many people will incorrectly assume that numbers equal objectivity. But data is always presented within a specific frame of reference, and that frame of reference is set by a human being. As Young laments, "Data purports to be objective, but it comes with a very human backdrop of assumptions. The risks," she observes, "are that we use the persuasive power of numbers as ideological tools." The Collective Data Map, in this sense, can be very dangerous.

This leads into the topic of my fourth and final post in "The Data Revolution Series", which will explore the downside of data—the pitfalls that we must avoid and the watch-outs that we must prepare for. Get ready!

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