For those of us involved in the collection and analysis of data, we simply can’t avoid the topic of Big Data. It now has the dubious honor of being the most confusing buzzword of the decade—and for good reason. It’s a label that is liberally applied across the technology landscape. From industry thought leaders trying to one-up each other to companies trying to make their product releases more appealing, Big Data has popped up everywhere, and one can easily feel overwhelmed trying to find its promised oasis of unparalleled insight in a desert of attractive looking mirages.
This is the first in a series of postings where I’ll be talking about some of the illusions that I see surrounding Big Data as it relates to digital marketing. My intent is to promote the reality that any organization can take advantage of Big Data and that it doesn’t take millions of dollars or moving mountains to get started.
The Bigger Picture
First, a reality of Big Data is that it can give you a high-resolution view of your customers. The illusion is that simply having more data will somehow magically accomplish this.
Doing the same analyses on a deeper set of data will definitely increase accuracy as you move from analyzing samples to populations (which is fantastic!), but without tying in additional data sets or doing different types of analysis (addressed in my next post), new insights will be limited.
With Big Data, it is often assumed that we need to go deeper to gather all the information we could possibly need. In fact, we should be going both broader and deeper, meaning we need to integrate our data sources together. As data continues to multiply, more data will be available tomorrow and from more sources than today, and most of it will be unstructured. Who knows what is going to be digitized and datafied tomorrow? Our customers are interacting through multiple channels including mobile, video, websites, and point of sale contacts daily. Capturing this unstructured data set and joining it with another data set to see what shakes out—that is when we begin to get to the reality of Big Data.
Let me illustrate: I had an experience this past summer that was a prime of example of a nonproductive use of Big Data. I bought a set of golf clubs from a national chain sporting goods store. I had a great in-store experience and even signed up for the rewards program. However, a few days later, I received an email offer for 25 percent off the driver I had just purchased. Whoops. All that equity the brand built with me started going down the drain. If their data collection was fine-tuned and integrated, they would have known that I already purchased the club and would have sent me a notice of savings on shoes or some other accessory. Instead, the notice served more as a slap in the face or “gotcha” than as a profitable marketing event.
I’m sure that each team involved probably thought they hit a home run. The demand-generation team that sent the email out probably saw the percentage of open emails go up. The team in charge of in-store point of sales saw another person sign up for the rewards program. The personalization team sent a golf-related email to someone who has a golf interest. Separately, the team that sent the email, the analytics team, and the personalization team were all under the illusion of success, high-five-ing one another in their individual silos. Was it successful? I have yet to take them up on any offers.
The disconnect is obvious when we peer in from the outside. For whatever reason (technical, organizational, political, etc.), these teams are not communicating and connecting their data, which results in lost opportunities.
This is what I’m talking about by making Big Data broad and not just deep. Complete, integrated data sets don’t have to be massive to realize the promise of Big Data for business, which is knowing your customers well enough to provide valuable experiences that keep them coming back again and again.
At Adobe, we are getting a higher-resolution view of our customers because we are connecting data sets and gathering more data from wider, outside sources. For example, Adobe doesn’t own Facebook, but people post a lot of information about Adobe there. It is important that we capture that data and get a closer look at it in relation to all the first-party data we collect. As we layer other data (social, email, advertising, etc.) on top of our Web data, the picture of our customers becomes clearer and new insights are revealed.
Realizing the promise of Big Data can start with one connection. You don’t need to boil the ocean. For instance, most Web analytic providers have out-of-the box integrations with many email providers. It’s a quick win for new insight and can help pave the way for organizational buy-off as you un-silo more complicated data integration.
A word of caution here though: keep in mind that with more data comes more types of analysis and more room for “data fitting.” One of the cardinal sins of data analysis is misusing data to justify a course of action based on an agenda. Good data governance will keep this to a minimum.
Striving for Completeness
Let’s talk about data completeness a little more. A lot of organizations that try to get a handle on Big Data will start to collect every bit and byte under the sun and then sample their data sets, creating more problems. The key is to get closer to the population with the goal of completeness, not just a big silo of data. Full data sets provide the freedom to explore, much like a high-resolution photo, but this is where sampling can cause it all to fall apart, like turning that high-res photo into a small JPEG file.
We can be more confident with our predictions with more sample (or complete) data than with smaller sets. Frankly, that’s the whole point of capturing data: to run analytics on it and predict what offer or action is going to make our customers pull the proverbial trigger on a purchase.
Cutting through the hype and getting to the reality of the use of Big Data is necessary to improve your customer engagement strategies. The misconception regarding Big Data is that once you’ve obtained it, your business will naturally improve its bottom line based on the information you’ve received. The concept of data completeness, as well as being confident in our customer predictions using data, must be augmented by integrating data sets. Only then can we use analytics to make sense of the data we want to take action on.
In Part 2, I will address Big Data analytics. I submit that Big Data doesn’t make any sense until you run analytics on it. Big Data … or “Big Analytics”? Where does value reside? What say you?