“We have #surveys that go back 10 years, but we haven't analyzed them,” my colleague said.
“Really?” I questioned somewhat surprised.
Five years ago, I was managing customer relationships at a small firm, and I was on the hunt to learn more about the people who purchased the company’s products. I wanted to create #personas to further characterize audiences. I figured that discovering who was purchasing the products would help to further define and structure the new marketing department’s efforts.
However, as I learned that we had traditional yet relevant data, analyzing it would involve a tremendous amount of effort, time, and money. Immediately, marketing’s trending topics of the day like "#bigdata" and "#informationoverload" became a reality for me.
The marketing team along with the editorial department spewed out an enormous amount of content and collected analytics along the way. However, the analytical focus was placed more on email and web traffic. Yet, we were beginning to notice the information that existed could help with the way we addressed customers. We had gathered all sorts of data, but we were still scratching our heads trying to figure out just how we could analyze the information and do so affordably.
In addition, we didn’t have someone on the team that had a love affair with numbers. If a person at the company loved math, they were too busy crunching out sales numbers and financial data. They resided in a whole different department located in another country.
As a small company, people were dedicating hours that went beyond the typical work week to meet revenue goals. It was a place where a person held a title, but they had more than one job. So, analyzing the information would mean a new hire and hiring was incremental based on success and revenue.
Finding Structure
The organization’s dilemma of producing a deluge of content, collecting huge amounts of unfiltered, randomly stored data; and determining how to analyze the data wasn’t abnormal at the time. Oh, the struggle over big data was real. The piles of data were increasing as well as the need to extract it to get business insights and to create value.
However, few companies at that time actually committed to a strategy. Companies needed to get people onboard, but this was at time when people were still trying to figure out Facebook and Twitter’s return on investment. And, even if company executives were thinking about a strategy, they needed to define their big data first.
So, data was disorganized or unstructured. However, #marketers wanting to understand, predict, and engage each individual consumer needed to transform the data so it could become actionable or more “smart.” Meanwhile #Gartner, a global analyst firm, was taking note of the activities occurring in e-commerce as well as the #datamanagement challenges and provided a framework known as a the 3V’s: volume, velocity, and variety.
Acceptance of Big Data
Over the years, new technologies helped companies gain more command of their data. For instance, they’ve combined data streams and found new uses for datasets. This has helped companies answer some of their biggest questions, but they’ve also used data to support their business activities and to show value.
Today, forward-thinking organizations have moved beyond the “Wow. We have data; and, a lot of it!” Instead, they are finding new ways to put that information into good use to increase the value of the business.
Big data is no longer on the sidelines; it’s driving business operations, according to Gartner. For instance, executives are making data and #analytics a part of their business strategy. Now, the goal is leverage.
What comes to mind when you think of “big data?” Well, for me, the theme music for Star Trek begins to play. Greater amounts of data, new sources of data and new uses of data has made it possible for us to make new discoveries and go on new journeys and adventures within science. Ultimately, this will lead to more innovations that could set off disruptive change.
If a crystal ball was placed in front of us today, the future would reveal that the advances in big data have not only awoken the possibilities for artificial intelligence and machine learning, but it has also helped to usher in their rapid rise and significance.
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