Why People Data Directs Big Data

Why People Data Directs Big Data

Big Data cannot provide a competitive advantage until people across the enterprise connect and collaborate to transform data into profit boosting insight. The tale of Big Data’s rise is fraught with great expectations and poor ROI. But, there’s hope! We like to call it “people data.”


People matter. Every sales person, service rep, and operations manager holds a slice of the Big Data cake that should be sweetening your customer’s experience. Connecting them is the key. Big Data is filtered through people, and those people have to get other people to act on their findings to catalyze change.


If we can start hearing everyone’s perceptions on their slice of the Big Data problem, innovative solutions will emerge.


I. Big Data Stumbles…


Wikibon’s preliminary research shows that enterprises expect a three to four-fold return on every Big Data dollar. Reality check: respondents pegged the actual return at $.55 for each dollar invested. In June, Gartner’s Adoption survey confirmed that Big Data interest continues to accelerate. 64% of responding organizations reported Big Data projects (up 8% from 2012). Still, more often than not, investments yield abysmal results. Why?


Wikibon’s analysis identified 2 reasons Big Data projects underperform:


  1. No Use Case: Often, IT departments are running experimental projects that aren’t tied to clear business outcomes. In fact, 56% of Gartner’s 720 respondents confessed that they don’t understand how to get value out of their data.
  2. Staffing Shortages: Even when Big Data pilot projects succeed, enterprises found they lacked the data scientists, admins, and developers necessary for a large-scale addition of projects.


So, who’s successful with Big Data? Is there a model that turns useless data into profits?


II. People Collaborate to Innovate


Yes, enterprises that start with small use cases and clearly identify talent gaps before expansion are well positioned to capitalize on Big Data. For most businesses, the core challenge is to align leadership around the right first use case and accurately assess staffing needs. Solution: people data.


Imagine, if you will, that you’re coaching a high school debate team. Out of business experience, I know, but roll with me here. You’ve got to help ten sharply dressed hipster types tackle both sides of this issue: “The United States Federal Government should invade Syria.” How you gonna’ do it?


If you’re smart, you won’t start suffocating them with data. Interviews, op-eds, and special briefings from the New York Times (the debaters’ equivalent of Big Data) are important, but you’re wasting everyone’s time until you get a feel for what they already know.


Each kid’s ideas and perceptions about the topic are “people data.” When you get them to start sharing their thoughts, something magical happens: ideas collide, change shape, take on new forms, and start sparking brilliant arguments.


Generate use cases just like debaters spark arguments. Coach your Big Data practitioners to solicit feedback from as many bottom-line contributors as possible. The more varied the voices, the more confident you’ll be that you’ve found a successful project. Make sure you get the perspectives of sales, marketing, customer service, and delivery professionals. They’re constantly touching customers and often feel the pains Big Data solves.


Ask questions like:


  • Where are your biggest strategic blind spots?
  • Is there a reservoir of data we could tap to remove that blind spot?
  • What internal and external talent do we need to understand this data?


III. Connect Voices to Speed Data Flow


After you create a list of potential Big Data use cases, ask stakeholders to rank the options. You want to find out where leadership is aligned and why.  Rank them in terms of:


  • ROI: how easy is it to monetize anticipated benefits?
  • Time-to-insight: How quickly will stakeholders experience results?
  • Repeatability: Will this use case serve as a predictable resource model for future projects?
  • Barriers: Does project X face any identifiable barriers to deployment?


One of our Fortune 500 clients walked through this exercise to evaluate over 50 possible innovation project investments. 100 senior executives identified which projects had the highest potential for return, faced the fewest resource barriers, and enjoyed almost universal buy-in. Executive respondents also recommended external and internal partners to expedite implementation.


Every recommendation our client received came not from Big Data itself, but from the minds of the stakeholders that were responsible for the investment. Because they collected insight from executives across the enterprise, they were able to start small and pick the project that had the greatest chance for success.


You can do the same. Steve Johnson, the author of Where Good Ideas Come From, points out that “chance favors the connected mind.” Everyone in your organization has hunches that can help you turn Big Data into real profits. Frequently, hunches lurk in the minds of many people across the enterprise and it’s not until you put them all together that you see an actionable Big Data roadmap.

Zachary Enos


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