Editor's note: Nathan Wolfe founded and leads Global Viral Forecasting, which works in 23 countries around the world to identify potential pandemics and stop them before they spread. His book, "The Viral Storm," will be published this fall by Henry Holt & Co. Lucky Gunasekara leads the information and communication technology division of Global Viral Forecasting. Zachary Bogue advises and invests in big data startup companies.
(CNN) -- The big issues in the air at this year's World Economic Forum in Davos were privacy and "big data" -- the vast amounts of revealing information we generate when using computers and mobile phones. Pundits have framed the discussion in simplistic terms, as an epic struggle between individual rights and corporate profits.
But there is a third way to look at big data -- not just as a commodity ripe for exploitation and abuse, but as a source and multiplier of social good.
Big data can help us change our world for the better.
There is no doubt that we are in the midst of a data revolution. We can call nearly anyone from just about anywhere on the planet. With a few mouse clicks, we can find out where we are and how to get where we are going. We can rapidly answer important questions, entertain ourselves and instantly keep up with friends and what they are doing. As we take advantage of these newfound abilities, we also generate unprecedented amounts of data about ourselves.
At a meeting of the forum's Young Global Leaders, we discussed how the data revolution can be used for social good. The possibilities seem limitless. Applications can help you better understand your energy bills in the context of your friends' and neighbors' spending.
Smart traffic grids mine massive traffic camera datasets to better understand how to reduce congestion and smog. Services like Mint.com help us save and manage money, by pulling together and analyzing millions of personal finance records.
Other applications mine online medical records to prevent health care provider errors and improve the accuracy of diagnoses.
Nathan's organization, Global Viral Forecasting, works to predict and prevent pandemics. It has done this for more than a decade using on-the-ground epidemiology. It has established "listening posts" in the viral hot spots of the world where the group collects specimens and analyzes them in the lab to identify and catch epidemics before they spread.
For example, in Cameroon, GVF studies hunters who have frequent contact with "bushmeat," wild animals that are hunted and eaten for daily survival. Bushmeat often includes species of primates whose susceptibility to microorganisms dangerously mirrors our own. Therefore, the multitudes of unidentified pathogens that they harbor are often easily transmittable to humans.
GVF has documented a range of viruses passing from bushmeat into people, and it is such cross-species transmission that is believed to have sparked the global AIDS pandemic. Continued sampling of this kind in more countries is vitally needed. But we do not believe that it will be enough.
These days, the GVF office holds a team of young researchers furiously working on their laptops at a long boardroom table. In typical Silicon Valley uniforms of jeans and hoodies, they crunch data that we believe is going to be an important part of the future of pandemic prevention. But it is not lab data -- it is digital data.
Along with its partners, Global Viral Forecasting uses sophisticated engineering and software techniques to mine the open web to monitor a complex tapestry of web reports on outbreaks around the world. This jigsaw puzzle of epidemic data helps us identify outbreaks outside our listening posts and investigate them rapidly.
However, the GVF team incessantly talks about needing more --they need big data. Data has power, but it is difficult to predict precise benefits without actually crunching the data. We have already identified several areas where we think there could be real benefits. Instant information from large retailers on purchases of over-the-counter medications could give us a head start in chasing down new outbreaks.
Using mobile phone data to model social interactions and mobility patterns can provide insight into sickness and outbreak patterns in cities. Witness how trends in online searching, as tracked by Google Flu Trends, can reveal influenza patterns before organizations like the Centers for Disease Control and Prevention see them. These applications are really just the beginning.
In discussions here at Davos it has become clear that companies want to help. However, many of them feel hamstrung by privacy and other concerns. We argue that personal identifiers are not necessary for most socially good uses of data, so companies can strip all links to individual customers out of their data before providing it to organizations like ours.
There are, however, examples where insufficiently anonymized data was matched back to the individual, so companies do face legal liability and negative publicity. Additionally, we should not overlook the potential risk to the individual in such situations. However, none of these risks are insurmountable and the significant potential public benefit outweighs the costs of resolving them.
What we need now is data philanthropy, a term that emerged spontaneously during a Davos conversation with open-source visionary and World Economic Forum CTO Brian Behlendorf. We are calling on companies to provide data as part of their strategic philanthropy, and to work with recipients like ourselves to establish processes to safeguard and properly anonymize data.
When properly stripped of personal identifiers, anonymized data will provide the answers to many questions of significant value to human populations. In 10 years we may very well look back and see that the information we all provided as we led our connected lives helped change the world for the better -- and perhaps even saved our own lives.
The opinions expressed in this commentary are solely those of the authors.