Lessons from Bear Grylls on Big and Little Data
The thought that ‘hey there’s more data than we can process!’ is dressed up as the latest trend with associated technology must-haves. Big data has become one of the most overused corporate buzzwords for 2014. If your not dropping it into conversations, pretending you know what it is and tweeting about it you might as well not even bother turning up for school. If your not part of the big data movement, forget trying to get into the cool gang.
But what is it? In its most simple explanation, “Big Data represents the ability to process a large amount of complex information to make better-informed business decisions”. Fundamentally big data is comprised of the 3 V’s: Volume, Velocity, and Variety.
The concept is infiltrating every aspect of our lives. However, there is a common misconception that surrounds the movement, and this is that every single business is sitting on big data. This is a distinctly inaccurate observation. The very application of big data to b2b sales industries, like Recruitment, media, technology, property, finance; any industry where you are having human interaction with your customer, is completely misplaced.
The irony is that whilst these industries are all desperately trying to get to grips with this overused, over applied, somewhat vague buzzword, they’re actually in possession of a goldmine more powerful than big data. They hold the keys to unlocking little data.
The data these savvy sales folk sit on isn’t enormous, disconnected or complex, and most importantly their customers aren’t anonymous. Their data is small, identified, and secularised. They know their customer, know their candidate, know the company and know the industry they are targeting. They already have an idea about their social habit, the publications they read, and their customer’s age. By contrast, take Amazon. They are a company with big data. They have millions of customers worldwide. And the only way they can interpret and understand them is through analysing previous purchases.
The rest of us, well we have it easy.
In essence unless you work for Amazon, Target, or Tesco, you are in possession of a substantial amount of data about a comparatively small customer base, which is just waiting to be demystified.
However, the inability to extract this data still poses a massive threat to businesses moving forward and becoming a digitalised enterprise. There are very little sales people within a company actually act on customer insight because they don’t know what they are looking for. For example, if you looked at the telesales data, they would realise that Friday afternoon is the less productive time to call. But because the majority of recruiters, media sales, brokers, estate agents, surveyors are KPI’d 200 calls per day, this simple recognition, which could save you considerable time and energy remains benign.
Unless you were a boy scout in big data, few companies have the wilderness survival skills to tackle big data alone. Do you think Bear Grylls was able to tackle deserts, amazon jungles, the Himalayas all by his self-taught knowledge? Though he likes to make you believe that, it is markedly untrue.
This is where predictive analytics comes in. It extracts your untapped resources, your oil, your little pot of gold and harnesses it. No more can businesses live by the “one ring to rule them all” principle. When unstructured, “small pieces loosely joined” data like emails, phone calls, and meetings are applied, the insights will be so succinct and so tailored that pipeline and forecasting inaccuracy will become a thing of the past.
Through tracking, interacting, mining, applying machine learning and underlying algos to this reserve of little data analytics can reinterpret findings to provide a much clearer picture of what is going on. Each company is different; each company has different factors and will produce different insights. By applying analytics to little data you will be able to home in on verticals are extract what is specific about this vertical, like when to call, frequency and time of calls and meetings, and buzzwords that resonate well in emails.
Bear Grylls was prepared, he received scrupulous expertise and training so he could tackle any challenge that arose. The same principles must be applied when approaching little (and big) data. With planning, outsourcing and understanding vulnerabilities you considerably lower the risk of having to drink your own urine!
“Size in itself doesn’t matter – what matters is having the data, of whatever size, that helps us solve a problem or address the question we have. This next decade belongs to distributed models not centralised ones, to collaboration not control, and to little data not big data.” Rufus Pollock, Founder and Co-Director of the Open Knowledge Foundation.