One of the more interesting comments regarding big data during a recent Gartner conference was that customers aren’t necessarily talking about big data — analysts and vendors are. This is bringing the big data question to the forefront for many organizations. The idea that well, yes, we have a lot more data these days seems obvious. Data is created more quickly (velocity), with more diversity (variety), by more individuals in larger amounts (volume), and with more intricacy (complexity) than ever before. The movement and dispersal of this data are also mind-boggling:

Source: Intel

In the context of the service experience, there are two primary questions that come to mind about big data:

  • What makes data “big”?
  • What does big data have to do with the service I provide my customers?

Truthfully, the term reminds me of taking sociology in college. We were tested on definitions that seemed self-evident, like “family unit”, or “nuclear family”, and so on. I always felt like I was missing something—like I could almost put my arms around it but it would slip away because when it came down to it, the definition seemed self-evident — which often times it was not. Big data is a little like this — it’s just data on steroids with a twist, or, maybe data squared. However, boiled down to its core, it’s simply “data”.

The first article available in the ACM Digital Library that references “big data” was published in 1997 by Michael Cox and David Ellsworth. The article was made available at a Visualization conference where they noted the issue of having so much data that it taxes computer memory capacity on all fronts (the volume issue). This idea that we would one day have so much data coming at us at such a rate with such complexity that we would be hard-pressed to turn it into something valuable, has strengthened over the past 75 years (“A Very Short History Of Big Data,” Gil Press). Today, the notion of big data as volume, velocity, variety, and complexity combined with the ability to pull together, link, compare, and analyze this data in order to get to a more advanced solution is overwhelming. Interestingly, definitions vary as to the core components of big data (see Gartner’s Big Data Definition Consists of Three Parts, Not to Be Confused with Three “V”s and  “Critical Questions for Big Data” in Information, Communications and Society.)

What Makes Data “Big”?
When we’re talking about post-sales service, what type of data makes data “big”? Gartner did a good job of shedding light on this.

First, they categorized the data typically tracked in service:  descriptions of a customer, social attributes, location, typical interactions, relationship history, next steps, etc. Next, they compared what data has historically been tracked vs. the addition of big data in order to gain a 360-degree view:

  • Historical service data tracking would include gender, income, age, and demographics; big data might also include values, attitudes, lifestyles, personalities, and interests (psychographics).
  • Historical tracking would include home and business address; big data includes real-time location.
  • Next-available-agent turns into next-available-agent based on the tracked personality of your customer.
  • Historical transactions could now include ongoing pattern analysis of your purchases not just with the current vendor but also with competitors.

In essence, the potential data being collected is monumental. Collection is one thing– what you are able to do with it is obviously the key (“The Embarrassment of Big Data and the Dilemma that it Poses,” Michael Maoz, Gartner).

What Does Big Data Have To Do With The Service I Provide My Customers?
Customers expect more from us. They expect us to anticipate their needs, not just answer their questions. One takeaway from a Gartner Big Data session went something like this:  Our customers expect us to be cooler than we would normally strive to be. They want us to provide service that incorporates the newest technologies, but it must be seamless and it must provide exceptional results. Big data facilitates our ability to provide this. It’s interesting insight, and important as we continue to take next steps in the service excellence continuum.  However, big data does have its issues.

When it comes to big data, we inherently have blind spots, imposed or unrecognized biases, contextual issues, unclear datasets, ethical limitations and more. When we talk about customer service and how to better service our customers, it may mean more to look at the well-connected “little data” (“The Knowledge Revolution Is Not About Big Data, It’s About Well-Connected Little Data,” Anthony Kosner) that connects these huge wells of information (mined company tweets and posts, aggregated, company-wide customer data, etc.) and a little bit of common sense (trust your instincts) to offer customer service improvements one step at a time. Harnessing big data can be a monstrous undertaking. Connecting pieces of what you can gather, knowing the data is inherently biased, enables you the flexibility to approach the data-gathering task in a manageable way.

The approach? Guerilla-based service.

  • No project is too small if you put the mechanisms in place to track your results.
  • Assign an owner.
  • Analyze your customers’ feedback.
  • Take a few baby steps to correct, enhance, or introduce new concepts.

One takeaway might be that the information your service organization has today, combined with any amount of the “big data” that’s being harnessed, plus your collective knowledge of servicing your customers, can enable your organization to take those few small leaps that differentiate you with your customers.

In the end, these small leaps created to increase revenues and loyalty, may just yield “big” results.

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