Setting The Stage For Big Data Success
The tenth iteration of this biannual CMO survey of nine separate areas of marketing leadership showed some surprising results after another full year of full-court press (and plenty of hype and big buzzwords) on the importance of using data to drive marketing decisions and performance.
More than 4,000 top marketers were surveyed, and respondents -- mostly VP-level or above marketing leaders -- reported a decrease in the percentage of marketing projects in their companies that used analytics, from 37% on February of 2012 to 29% in August 2013.
And a majority of companies that said they did use marketing analytics reported not evaluating the quality of the analytics efforts in increasing rates: 53% in August 2012 and 67% a year later in August 2013.
So what gives? Is Big Data and all its promises of riches and glory really just this century's version of snake oil elixir and have all the smartest marketers called fraud on it?
Not at all.
What we're looking at here is simply a case of data analysis efforts going through the fits and starts of infancy. In other words, marketing analytics is still wobbling -- not yet fully on its feet.
Actually, here's a better analogy for you: Have you ever seen a small child trying to eat an adult-sized hamburger? You know what it looks like. After picking the biggest, most impressive-looking burger on the whole menu, the small hands pick up the huge sandwich and it literally eclipses the child’s head as the poor kid tries to get even a single bite from every possible angle without success. Stuff starts dripping off, the whole thing starts to fall apart, and frustration builds. Finally, the child drops what’s left of the burger on the plate, starts crying and gives up without ever having had a taste.
This is exactly how many organizations have thus far experienced Big Data.
They look at it, they want it -- they may even go ahead and buy it with the figurative extra cheese and pickles. But once they get it between their hands they have no idea how to action it in their organizations.
So what do they do? Well, as this survey clearly illustrates, they go back to what’s comfortable and what they know is doable, and actually use data and analytics LESS than before to make decisions!
It is critically important to understand how you're going to use Big Data before you just go to the counter and order the biggest hamburger on the menu, and it’s equally important to set the right expectations around deploying a large-scale undertaking.
In many cases, IT departments are months or years into architecting and building out huge data warehouses and integrating disparate corporate systems to store and access the ever-increasing streams of data being generated every minute of every day. However, those that will use this data -- and marketing departments are by no means the only stakeholders here -- have yet to be consulted.
Simply being involved in the early stages of understanding what data is currently available and what is planned to be made available will help you identify gaps early on that are much less costly and faster to include in the very beginning. For example, if you work with third-party media or digital agencies, it's likely that your IT department is unaware of these external data sets that you'll want to bring into your architecture. It's also possible that you don't know what data your agencies are generating on your behalf. The first step is often discovery of what you know you don't know!
Your data should be mined for value -- whether that's identifying customer behavior insights that can provide a better customer experience, gaining operational understanding of processes to drive efficiencies, or optimizing advertising spend and media mix, you have to begin with your business goals. Only then can your Big Data programs bring you the value that they’re capable of.
Progressing your Big Data initiatives from initial monitoring all the way to organizational transformation will take time. But if you base your efforts on a firm understanding of what your goals are, how you'll measure them and what data will drive the foundation of that structure, that big, messy hamburger will become a very manageable and delicious meal that was well worth the wait.