Last year, Gartner created quite a stir by retiring the “Big Data” Hype Cycle from its popular “Hype” curve series. According to Betsy Burton at Gartner, “We’ve retired the big data hype cycle. I know some clients may be really surprised by that because the big data hype cycle was a really important one for many years. But what’s happening is that big data has quickly moved over the Peak of Inflated Expectations and has become prevalent in our lives across many hype cycles. So big data has become a part of many hype cycles.”
This “mainstreaming” is a precursor to a broader trend I’m seeing, which is the movement to Machine Learning and Machine Intelligence. Some of the research for that post got me thinking about other areas within the content space in which Analytics is increasingly an integral part of the value equation. And how some of our old labels and terms in the content space, while still describing a totally valid value equation, are no longer sufficient to describe the new ways in which these technologies can be pushed into the future.
Take “capture” for example, a term that has been with us since before I came to AIIM (and that’s a hell of a long time).
At first, we thought of “capture” as something we did exclusively to paper and with a scanner and with the end objective of creating digital images of the paper that existed in the physical world.
We moved from there to thinking about "information capture" – not just paper, and not just creating images, but automatically extracting information off images as data, and injecting this information as fuel for a business process. We’ve replaced paper forms that we used to spend countless cycles digitizing with forms that are natively digital. And we’ve spent the past few years extending these information capture capabilities to mobile devices and pushing them closer and closer to the customer and to the point where information is created.
There is no shortage of business still in this business of “information” capture and process automation. I am constantly amazed at how many green fields there are out there for this set of extended technologies. Though it seems like we’ve been at this for years, many organizations have truly only begun this journey. I get more questions that I would call “Capture 101” than ever before. Even for those who have been in the process automation business for a long time, it’s a new game. The entry of mobile and the cloud changes the fundamentals of “how” and “how expensive” for capture and opens up all sorts of new avenues for customer experience.
But there’s another huge opportunity for organizations out there, and what we’ve learned in the capture space is incredibly relevant for this opportunity. And that is applying what we’ve learned to the task of understanding exactly what is in all of that dark (as well as big) data and content in our organization, extracting insight from it, and putting those insights to work. "Content analytics" is an example of Gartner’s “mainstreaming” of “big data.”
So I think there’s a “walk and chew gum” opportunity out there for both users and solution providers out there.
On the one hand, we need to continue to search relentlessly for opportunities to automate and improve our business processes and customer interactions and make unstructured information truly useful by pushing the limits of information capture.
At the same time, we need to use what we’ve learned along the way to push our organizations to the next opportunity. And that opportunity is to make unstructured information insightful through content analytics as well as useful. And thereby set the stage for the machine intelligence revolution that is coming.