Big data emerged in recent years as the shiny new toy of the marketing world.
It was exciting, expensive, and though it had actually been around for quite a while, captivated us with recent strides, creating an allure akin to the mystique of the latest iPhone series.
And not unlike overzealous children, we were quick to jump all over it.
But now that the dust has settled a bit, and a few smudgy thumbprints have emerged, the sheer novelty faded, we can shed some light on the inevitable loopholes.
The logos of Big Data
You know something’s gotten a bit out of hand when it’s formed legitimate groupies. We’re not talking about marketers here. No no, these are the true data nerds. There’s a certain romantic headiness about the idealism of Big data, that it could literally spell the end of theory, obliterating ambiguity and with it risk, gleaning sound businesses decisions with the glorious assurance of sheer, unadulterated facts.
Rather ironically, the idea that Big Data could be unbiased is itself an idealistic theory. The possibility for bias is significant. Combine this with the fact that Big Data is normally attached to Big Budgets, and you’ve got something else that’s oversized: Risk.
It seems that marketers have made the same misstep with big data that has been made with almost every new and exciting marketing technique during its heady rise to fame.
The excitement of what a new tool could mean for business leads to self-involved, backwards thinking. Effective marketers know to begin with the end-user in mind, then work backwards to determine how they can leverage their resources to meet a consumer need.
With unbridled enthusiasm, huge budgets are dumped into collecting vast swathes of data, all predicated on the belief that an expanse of information will provide unparalleled precision and ultimately, return.
And despite generating a ton of what we’ll call ‘information potential’, attempting to tackle it without a clear sense of purpose is a bit like trying to find a needle in a haystack. It’s just not feasible with the current analytical capabilities most companies possess.
Like solar power, there is indeed unbound potential. But it is useless to us until we have the capability to harness it.
And without the proper capabilities to leverage that data and turn it into actionable insights, that investment remains unrealized.
Here’s the thing with Big Data. Sifting through it is like looking through a telescope at the vast and unknowable universe: if you hope to find anything, you need to have some idea of what you’re looking for.
Insight vs. information. Which came first?
As the stealthiest of marketers have shown us, big data is a tool and a resource. It helps to answer the question. It doesn’t pose it.
That’s were small data comes in.
Where big data provides the information, small data provides the insight.
Small data is the critical point where vast information (big data) delivers on a specific, individual need. Small data is where qualified leads and sound investments are determined. By contextualizing information and makes it actionable on the ground level to produce timely, relevant results for the end-user.
Small data is the gateway for big data to become useful.
Amazon Recommendations, Kayak’s “when to book” tool, and Nike’s “Fuelband” are a few oft-cited examples of companies harnessing big data with small data insights. They harness big data about flight pricing or relative consumer purchasing habits, or exercise, to provide a simple, distilled insight.
Buy a flight, or wait.
You will probably enjoy this book. Other people like you did.
You burn more calories walking around the office than you do at the treadmill on the gym.
Small data allows companies the opportunity to be useful, timely, and relevant. In the real-time marketing world that’s emerged with social media and mobile, that’s an absolute must.
Marketing fitness: Get Lean
The emergence of small data in some ways mirrors the sociocultural evolution surrounding the ideas of fitness, which pays close attention to relative value.
It discards the flawed consumerist mentality that more is better. As we work to ‘trim the fat’ in a world bloated by overconsumption of food, of goods, of information we see a strong need to sift through it all and distill it down in hopes of arriving at quality.
This same principle applies to marketing analytics. Turns out, more (or bigger) is not necessarily better.
In the end, the quality of data analysis and the actionable outcome of that analysis are more valuable than the amount of data you have.
That is good news for smaller businesses and smaller budgets. It evens the playing field, and places precedence on connecting with the right people in the right way something that requires capabilities more than budget capacity.
It is all about what’s relevant and applicable to your purpose.
Because while the Milky Way contains some 400 billion stars, you need only spot three of them to get a glimpse of Orion.
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