Every now and then I'm confronted by a stat that I absolutely know is going to have deep and widespread implications for the digital marketing industry. I usually begin to feel the ripples within a few months even though it may take years to truly understand the full effect of the change. Whether it was the first time I read that more American internet users had broadband than dial up, the fact that 72 hours of video footage is uploaded every minute to YouTube, or the moment when Facebook reached five hundred million users -- these stats speak for themselves. The game has changed. I'm surely not the first person to uncover every new trend, but I know an earthquake when I feel one.
I read something recently in a USA Today article that set off my industry seismograph, "'Big data' transforms our lives and lifestyles," by Chuck Rassch. In a story around how data has permeated so completely into our daily lives, I read this:
"Welcome to your everyday world of 'Big Data,' the infinite sea of facts, products, books, maps, conversations, references, opinions, trends, videos, advertisements, surveys -- all of the sense and nonsense that is literally at your fingertips, 24/7, everyday from now on. Eric Schmidt, Google's executive chairman, estimates that humans now create in two days the same amount of data that it took from the dawn of civilization until 2003 to create."
Read that last sentence again: "Humans now create in two days the same amount of data that it took from the dawn of civilization until 2003 to create."
OK, whether or not you're on the love or hate side of 2012's buzziest buzzword, "big data," it's time to get on the bandwagon, folks. But should we immediately accept the notion that having more data will mean having better data?
Does more equal better?
I asked this question to my company's co-founder and CTO, Anto Chittilappilly (Chittilappilly speaks data fluently and -- secretly -- I think he might have been born in the Matrix). I wanted to know if one should automatically assume that having more data is actually a good thing, he went to the white board and wrote the following:
"Even though we collect a lot of data and that amount of data is increasing exponentially, the data itself has very little use," Chittilappilly explained. "Data is used to develop what people actually want which is information. Information technology is all about developing information out of data and is used to understand what is going on and to predict what will happen. The ability to predict with different scenarios allows you to alter the future outcomes on your favor. To know exactly the right thing to do at the right time -- that information is utterly invaluable to any business. "
"OK, that's the value in data," I said. "I get that. But what's the value of big data?"
"Having more data is like having more apple trees. The opportunity to find 10 great apples in an orchard of one hundred trees is greater than if you only have a single tree to pick from. The more data we collect, the more chance we will be able to find the nuggets of information and supporting evidence that will lead to more accurate predictions," Chittilappilly said.
As much as the term big data seems to make marketers cringe, we've been working as an industry to more data-driven thinking for the past decade. In fact, 96 percent of U.S. brand marketers and agencies recently responded as being very concerned with being able to understand and drive ROI from big data.
But where do we start?
The rise of data scientists
How are we going to sift through data that is doubling ten thousand years of recorded history every two days? What tools do we need? How is this information going to flow through marketing organizations to even make use of it?
As with many challenges, it's almost always a people problem.
In October 2012, the Harvard Business Review presented a defining article on this subject with "Data Scientist: The Sexiest Job of the 21st Century," by Thomas H. Davenport and D.J. Patil. In this article, Davenport and Patil describe the emerging role of the data scientist:
"More than anything, what data scientists do is make discoveries while swimming in data. It's their preferred method of navigating the world around them. At ease in the digital realm, they are able to bring structure to large quantities of formless data and make analysis possible. They identify rich datasources, join them with other, potentially incomplete data sources, and clean the resulting set. In a competitive landscape where challenges keep changing and data never stop flowing, data scientists help decision makers shift from ad hoc analysis to an ongoing conversation with data."
The article begins by profiling LinkedIn data scientist Jonathan Goldman, a PhD in physics from Stanford, who, in 2006, was able to identify, collect, merge, scrub, analyze, and build insights that helped change the way LinkedIn successfully engaged and built its user base. Guys like Chittilappilly and Goldman see data the way that chefs see ingredients -- the cake is there, it just needs to get put together.
But marketers haven't traditionally had the deep math, coding, and data skill sets to work this way -- and it takes years of training and experience to be able to get to that point. That's a problem for this industry.
"Many marketers even lack the essential understanding of where that data comes from, its implicit presumptions and dependencies, and how to extrapolate from the quantitative to qualitative or business conclusion," said Daniel Jaye, CEO and founder of data powerhouse Korrelate. "Case in point, most advertisers can analyze a marketing campaign and come up with a conclusion, but if they don't know that the data they analyzed is limited to only a specific subset of users, they may misapply the data or present erroneous results to the chief decision makers."
"In ten years, marketing organizations will have embedded in their planning, strategy, and execution teams people who are comfortable with the entire pedigree of the data they leverage, its curation, and how to dig deep and create business insights and conclusions using SQL or whatever tools we have at that point. They won't necessarily be statisticians, but they'll be familiar with things like causal calculus and the difference between training and scoring in machine learning," Jaye said.
Jim Sterne, founder of the Digital Analytics Association, who turned me on to the Harvard Review article agrees this role is an inevitability:
"With enough data and enough smarts, we are more and more capable of anticipating needs and filling them. What's missing is not the data nor the technology, but the educated, creative, inventive Data Scientists who understand granular data and see the big picture at the same time. If you find one, do not let him or her out of your sight!"
Five action items to data driven nirvana
Not only is aligning your operations and processes around data the right thing for your company, but it's the right thing for you, too. The era of data is here whether you like it or not. You can't just bury your head in the sand like an ostrich and hope disruptive change passes over you like an afternoon storm. Even if you're not "in the data department," unless you're four or five years from retirement, you will need to get comfortable in a data setting or find your career track being leapfrogged by younger and more data-friendly colleagues.
The following are five steps to begin the process of getting your organization more data friendly:
Admit the problem
As any good coach will tell you, the first step to solving any problem is to admit you have the problem. Stop wrinkling your nose at big data and embrace it. Yes, we know your skillset in this area is limited and it's not a comfort zone for you. But, instead of poo-pooing the issue, embrace it. You could be the catalyst for change and be remembered as the data hero.
Self-assessment via auditing
Start by getting the smartest folks in your organization together and try to figure out where the gaps are. Are your teams still working in data silos? Does everyone who touches the data understand the reports and use them to make decisions? How are you holding people accountable to bring data into the equation? How are your competitors using data to succeed?
Setting goals and prioritization
Proper project planning should be the easiest bullet point to tackle. Once you have your audit in hand, it should be very clear which are the short term tasks that can be small wins and which long term projects that will virtually change the game for your marketing organization. Set goals, milestones, and check-in points to keep on schedule. You don't have to go at it disruptively in full speed -- even a slow, gradual, and steady change will pay off in the years to come.
The Harvard Business Review article has some good advice for attracting data scientists from posting geek contests to hanging out online where these kinds of folks gather. You'll also need more training for everyone and new tools will probably be in order as well. It's important that the person who signs the checks is completely onboard with this strategy so that these projects will be funded properly. Chances are you won't get everything you ask for, but if you start laying the foundation now, it will be easier to push through these initiatives later.
Learn and adapt
You're going to make mistakes -- that's a given. You may hire a new data guy only to find out he has the personality of a moth and will never click with the team. You also may end up licensing some amazing technology to better help your team visualize the big data sets and realize a few months later that no one is adopting the tool. Don't get discouraged. This is a process. If it was easy, everyone would be doing it. But they aren't. If you can move the needle even slightly for your company towards the data future, it will be worth it.
Times they are a changing…
It's a new era of marketing -- the era of data. Regardless if your data is big, bigger, or biggest, the marketing organizations that embrace this new world will find themselves with a distinct and powerful advantage over their competitors. For so long, marketing has chiefly been a two headed monster of media and creative. It would seem that data is finally taking its seat at the table -- it hasn't been invited, it's literally crashing the party.
As an industry, we've been discussing and pawing at these challenges for some time. Data has been given lip service in a way. No one denies that there's value there, but for most marketers who come from media or creative backgrounds, analytics tends to be more of an afterthought used post-campaign to measure things or to justify opinions already set.
Now, the paper trails that follow every consumer and transaction in the digital world will yield major returns for those that know how to best tap into it and have the courage to let the data lead versus follow. We'll need data scientists to not just answer the questions we need answering, but rather to think of the issues that non-geeks haven't even thought to examine. Many people have the skills to answer difficult questions, but it's the true visionaries that know which difficult questions to ask. You'll need people like this on your team to get you across the finish line.
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"Apples" image via Shutterstock.