A recent New York Times op-ed looked at the ascendance of big data, noting that the World Economic Forum has compared the rise of big data to “transformative innovations like the steam locomotive, electricity grids, steel, air-conditioning and the radio.” Author James Glanz points to lackluster productivity growth since the emergence of big data (which he dates back to 2005) and asks why we aren’t seeing more obvious productivity benefits from this supposedly “transformative” development. At Context Matters, we’re not really surprised that big data hasn’t lived up to its promise yet. It’s always been our belief that the presence of data alone (even in vast quantities) is not inherently valuable. You have to ask the right questions to gain real value from data of any size.
In recent years there’s been an enormous expansion in the ability to gather data, which has in turn, led to an explosion in the quantity of data available. We have not seen a corresponding expansion of talent or resources devoted to extracting value from all this new data.
As our CEO Dr. Yin Ho said last year in Xconomy, “A lot of people focus on the big part of it, instead of the impact. I think it’s more about the ability to glean some kind of insight from the data.”
More of the Same
Adding large amounts of data to the mix without adding analytical sophistication will let businesses do more of what they are doing now, and do it better. That is valuable, but it’s not transformative.
Extracting transformative value from data requires two kinds of sophistication: technical statistical experience and analytical sophistication. The first is the most obvious, but it is the second that is more likely to create transformation.
So what do we mean by analytical sophistication?
- Translating business priorities into questions that data can answer
- Designing analysis to provide insightful answers
- Understanding what business opportunities arise from those answers
In short, it is the ability to turn data into valuable insight that drives change.
Ultimately, the roadblocks aren’t fundamentally technical – designing the right data architecture or applying the right statistical technique. The real challenges are organizational – aligning the right resources with the data to generate true insight, and then acting on that insight.
Businesses and economies aren’t likely to be transformed by getting more or better answers to the same types of questions. They will be transformed by radical changes in what’s possible or by seeing the world in a different, better way. New data (big or small) has the power to transform - but not by itself. We have to know how to ask (and answer!) the right questions.
Our industry understands the power of data and is genuinely hungry for ways to transform. Specifically, most of our customers want to speed drug development and make better use of patents. But we wonder – can the fast increase of data we’ve seen over the past few years alone help companies achieve transformation?
From our point of view, the real question regarding the influence and impact of big data comes down to the allocation of resources. Are we investing too much in the process of gathering the data - at the expense of understanding what it actually means?
If so, what is the right balance?