Big data is worth nothing without big science
As with gold or oil, data has no intrinsic value, writes Webtrends CEO Alex Yoder. Big science, which bridges the gap between knowledge and insight, is where the real value is.
Editors' note: This is a guest column. See Alex Yoder's bio below.
We are living in "the age of big data," according to The World Economic Forum.agrees. I do too.
As the likes of Google, Facebook, Adobe Systems, and IBM embrace big data with gusto, startups are also popping up with the promise to help companies discover what one of the most valuable assets in the world can accomplish for them. No industry is untouched by big data, which is notably transforming the way social networks work today. However, the key factor that will determine success for companies in this age is not simply big data, but big science.
Gold requires mining and processing before it finds its way into our jewelry, electronics, and even the Fort Knox vault. Oil requires extraction and refinement before it becomes the gasoline that fuels our vehicles. Likewise, data requires collection, mining and, finally, analysis before we can realize its true value for businesses, governments, and individuals alike.
According to IDC, the amount of data that companies are wrestling with is growing at 50 percent per year -- or more than doubling every two years. Many organizations are rich in data but poor in insight. That's where big science comes in.
The collection and mining of massive amounts of digital data currently defines the term big data. Those are processes that businesses largely handle. However, the analysis of that data -- that magic ingredient of algorithms and advanced mathematics that bridges the gap between knowledge and insight -- is big science. It is where the value is. It is the future.
Put simply, the analysis that big science brings to the table makes big data relevant. I envision big science combining with big data to create big opportunities in three significant ways: real-time relevant content, data visualization, and predictive analytics. Although I think that these trends will be especially important for my industry, digital marketing and analytics, I have no doubt that they will impact all industries, as chief marketing officers are inevitably drawn closer to chief information officers in an effort to tame and harness big data.
Getting right message to the right customer at the right time is the promise of relevant, real-time marketing. Big science, not big data, will bring this to life.
Marketers, using analytics to collect massive amounts of digital information, have been working with big data for years now. In fact, they are flooded with geographic, demographic, and ethnographic data about their customers. The big science of processing and analyzing this data, through human expertise and machine intelligence, will empower marketers to identify and segment their customers, tailor and target the most relevant content to them, and deliver these experiences in real time across a range of digital channels and devices.
As was stated, human intelligence is a part of the big science that will help to deliver relevant content in real time. The human intelligence of big science will be fueled by data visualization.
Visualizations of Web traffic have been around for years. These are relatively simplistic, however, and they typically visualize data that is historical. Big science will take the raw potential of big data and make it digestible for the human mind in real time. Imagine a retailer being able to visualize and track both the shipments of new goods and the intake of returned or unused items in real time through a bright, simple, and dynamic user interface. The opportunities for optimizing business processes, and revenue in just this one scenario are endless.
But what if you could use your big data to see not just what's happening now, but also to model what you could be doing to optimize outcomes for the future? Enter big science fueling predictive analytics.
The big science of predictive analytics will take advantage of the historical patterns ingrained in big data to unlock insights to inform current and future strategies. Should you change the image in an advertisement from a black-and-white graphic to a color photo? If you did, what sorts of results could you expect? Big science can help show you the way.
It takes complex algorithms, powerful computing and, perhaps most of all, human analysts to build and administer the big science that turns the "then and now" nature of big data into "when." Last year, the McKinsey Global Institute projected that the United States alone needs 140,000 to 190,000 more workers with "deep analytical expertise."
Those who become experts in the science behind the big-data phenomenon will become the next wave of digital and corporate geniuses. One potential genius, Gilad Elbaz, the influential investor and inventor behind big-data startup Factual, recently told The New York Times, "I have been thinking that we need to get more personal data. I want to get people to figure out a way to get people to leave their data to science."