Marketers have never had more data at their disposal. The opportunity to glean insights by crunching all of these new sources of data is pretty intoxicating.
Yet having access to the data is only one part of the equation. Making sense of the data is the harder part.
Marketers sometimes forget the Statistics 101 maxim that correlation doesn’t imply causation. I’m also sensing a movement in business to follow data blindly without common sense questioning.
As New Yorker’s Gary Marcus put it, “Big Data is a powerful tool for inferring correlations, not a magic wand for inferring causality.”
There’s an art to data science. Big Data does not necessarily lead to Big Insights. As marketers, we need to ask the right questions to collect the right data, and probe deeply on the implications that we find.
I liked reading this insight from the Guardian that advocates Lean Data over Big Data:
“The dirty secret of big data is that no algorithm can tell you what’s significant, or what it means. Data then becomes another problem for you to solve. A lean data approach suggests starting with questions relevant to your business and finding ways to answer them through data, rather than sifting through countless data sets.
“Furthermore, purely algorithmic extraction of rules from data is prone to creating spurious connections, such as false correlations. Today’s big data hype seems more concerned with indiscriminate hoarding than helping businesses make the right decisions.”
I’d love to hear your thoughts and experiences marketing with Big Data.
(Marketoonist Monday: I’m giving away a signed print of this week’s cartoon. Just share an insightful comment to this week’s post by 5:00 PST on Monday. Thanks!)
12 CommentsJoin the Discussion
You’re being too kind when you say “Marketers sometimes forget the statistics 101 maxim” – the reality is that a scary proportion of marketers don’t understand the first thing about statistics.
Ryan Wallman says
You’ve hit the nail on the head, Tom.
Anyone who doesn’t understand probability – let alone more complex statistical principles – shouldn’t even be talking about data. Rory Sutherland gives a great example of this here: http://www.spectator.co.uk/life/the-wiki-man/9147101/what-the-o-j-simpson-jury-didnt-know-and-schools-should-teach/
And this was my take on the Big Data bluff (in the context of marketing communications): http://www.wellmark.com.au/2013/03/whats-the-big-data-deal/
One of my favorite cartoons:
Geert Verstraeten says
Spot on Tom!! I’ve been working in this area since the time when ‘data mining’ was considered ‘the serendipitous discovery of useful information’. Fortunately, data miners have realized it all starts (and ends) with solving a concrete business problem, and that you don’t stumble upon business value by chance (there’s a process to follow!). The big data buzz brought new attention to the field, but reminds us of the same pitfalls. Big data should not replace analytics methodology – or common sense for that matter.
“Pay no attention to that man behind the curtain!”
Seriously, I believe shining a new light on data, regardless of what it is called, is a good thing. “Big Data” is only half the equation, the other half is the invention of the “Data Scientist” – part statistician, part engineer, part BA, part “Sherlock Holmes”.
Bill Carlson says
I worked on projects with one of the big big-data folks and found it fascinating how the same data could drive so many “insights” which sometimes differed radically person to person. Sometimes polar opposite interpretations of the “causality” and then at the other end, often limited understanding/consideration of the practical actionability of those interpretations.
Data is a tool and like all tools, what gets accomplished is a function of who holds and uses it… And knowing what a tool is *not* good for is important as well.
I loved the comment: “Today’s big data hype seems more concerned with indiscriminate hoarding than helping businesses make the right decisions.”
It is absolutely critical to prioritize gathering / analyzing the data to help you solve your business problems and identify relevant growth opportunities. And, that may not require big data at all.
Mariam Rafique says
I really appreciate the conversations on big data here 🙂 For a data enthusiast, these are very quenching 🙂
A few thoughts though:
1. does big data and too many numbers in a sense undermine the power that qualitative data can produce?
2. Can small realistic opportunities, the kind that lead to a tipping point in real life, be really found through big or otherwise data?
What especially amazes me is the different speeds at which data analysis is growing in different parts of the world. In south asian developing countries especially, researchers are still struggling with collecting, maintaining data and making the idea of sharing it more open. While in the other world, solutions are being developed using data!
Jase Clarke says
If you’re using Big Data to explain the reason for something, you will always find the answer. Statistics can be made to fit any question asked of it.
Mikael Nygren says
Big data is like teenage sex, everyone is talking about it. nobody really knows how to do it. Everyone thinks everyone else is doing it, so everyone claims they are doing it
Alan 'Brand' Williamson says
“Did you know that sales of our branded sun hats – Solar Topee – has a direct correlation with the number of shaved heads?”
Mario Kroll says
Really a great way to also illustrate “how to say anything with statistics;” even if the message is not intentional, blindly following associations and mistaking them for causation is a recipe for disaster. Great cartoon!