In the search for insight, how relevant is the “size” of the data?
I love Big Data—or at least the idea of it. But somewhere between the vision of Big Data and the practical reality of putting it to work, there’s been a pretty big disconnect. Heck, even IBM’s Jeopardy!-winning Watson, with its snazzy learning algorithms, has struggled to find much work, according to a recent Wall Street Journal article.
This is why I’ve been a proponent of thinking small when it comes to Big Data—and why a growing number of organizations are reorienting their efforts to everyday problem solving and analytical solutions that don’t require a Ph.D. to operate.
Why are we seeing a growing discontent with Big Data, and a shift in emphasis from “bigger is better” tools for data scientists to “simpler is better”? Why do purpose-built apps, wearables, and dashboards appeal to the masses?
Simply put: Computers like data, but people like answers.
Want to get more ROI from your Big Data initiative? Provide useful insights. And deliver them to the widest possible audience. Focus less on gathering more data and more on connecting users to the most relevant snippets, recommendations, or answers. If you embrace this viewpoint, it quickly changes the discussion from the size of our data stores to the reach of our data and resulting insights.
Thinking small gets us closer to the problem
The “Small Data” philosophy is very much about this “last mile” of Big Data and creating consumer-style, more responsive, more social apps that turn insight into action. Compared to traditional Big Data, which is all about the “3 Vs” (variety, velocity, volume) and machines and processing power, Small Data is about end-users, context, and individual requirements. Big Data tends to be about growing our data assets, while Small Data is about harnessing the data that’s all around us, discovering its meaning, and delivering insights and answers to the broadest set of users.
Yet, whether you start with (and believe in) Big or Small Data, it’s critical to focus on specific problems, frontline users, and everyday tasks to demonstrate value (the fourth V). To be successful in harnessing the potential of information at all levels of the organization, we need to start with a purpose.
As I explored in a recent DCG study, the purpose of Big Data may in fact be its potential to revolutionize the way businesses interact with customers, transform how customers access and consume (and even wear) useful data, and redefine the relationship between buyers and sellers. In other words, we need to get to consumers and understand what they’re asking, and streamline their path to finding the best answer. Marketing needs to lead the way.
More specifically, marketers need to play a more active role in defining and driving Big and Small Data initiatives—and framing the narrative and questions and answers that offer the biggest bang for the buck. And, of course, marketing teams looking for analytical solutions need to demand personalized analytics and campaign tools that are tailored to business roles versus technical users, and integrated platforms that make it easy to access, test, apply, and share insights in the stream of their daily tasks.
Even more so, as long as IT and the data jockeys are driving the technical side of Big Data, marketing needs to be the voice of the (nontechnical) end-user, especially when users include external customers.
The good news is that we’ve faced this challenge before. Remember when everyone was spun up about artificial intelligence (AI) and knowledge management (KM)? I was in the middle of it as a researcher and data scientist in the early 1990s. Just like Big Data, AI was going to change the world, and top firms were loading up on AI talent and even hiring chief knowledge officers to lead the charge. Yet, without specific applications for everyday tasks and workers, it was just a vision that received lots of press, was great for consultants, and generally bad for the bottom lines of organizations that bought into the hype. Sound familiar?
Technology runs in cycles, yet too many folks think that Big Data is this brand new idea. It’s not. In fact, the key foundations, deals, and development that shape today’s Big (and Small) Data initiatives go back more than 20 years. Of course, today we not only have more processing power, ubiquitous connectivity, and more data sources, but also increasingly savvy customers and support teams who need tools that provide insights neatly packaged and delivered where and when they need them.
This is why being data-driven is less about the size of our data and more about using the right data to get closer to our customers, create awesome experiences, and provide just the right advice, offer, or answer to get them on their way.
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Allen Bonde, Digital Clarity Group
Three-time CMO Allen Bonde is VP of product marketing and innovation at Actuate, where he works with clients to ascertain better market insight through Big—and Small—Data. An early proponent of data-driven marketing, Bonde began his career as a researcher and data scientist in the telecom sector, and served as an analyst and practice leader at Digital Clarity Group, Yankee Group, and McKinsey. Additionally, he was cofounder of social media campaign creator Offerpop. Bonde is a self-proclaimed data scientist turned CMO and advisor who has an affinity for BMW roadsters, indie music, social media, and travel. The bassist “in a semi-famous indie blues rock band outside Boston,” called Three Stories—who were finalists in WFNXBoston Phoenix’ Last Band Standing competition in 2006—Bonde blogs at smalldatagroup.com and tweets at @abonde.
