By Rob Symes, CEO The Outside View
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 in 2014. If you’re 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 you’re 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.
Constantly I read panic stricken articles that comment on ‘recruitment predictions for 2014: big data’ and ‘big data is the next big change to the way recruitment is done’. But they never actually provide any solutions. Why? Because the very application of the word big data to recruitment is a complete misconception.
Recruiters aren’t sitting on big data. The data they sit on isn’t enormous, disconnected, complex and most importantly the customers aren’t anonymous. Take Amazon. They have millions of customers worldwide. And the only way they can interpret and understand their customer is through analysing their previous purchases.
Recruiters, well, they have it easy. Their data is small, identified, and secularised. Recruiters know their customer, know their candidate, know the company and know what industry they are approaching. They already have an idea about their social habit, the publications they read, and their age. Because recruiters possess substantial amounts of knowledge about their small customer base, they’re in possession of a goldmine more powerful than big data they have little data.
However, the inability to extract this data among recruiters still poses a massive threat to them moving forward and becoming, like everyone else, a digitalised enterprise. Very little sales people within a recruitment agency actually act on customer insight because they don’t know what they are looking for. For example if you looked at the data, like we have, you would realise that Friday afternoon is the less productive time to call clients. But because part of your KPI’s demand that you make 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 distinctly untrue.
This is where we come in. We extract untapped resources, your oil, your little pot of gold and harness it. No more can recruiter 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 we help recruiters redirect their attention, and learn about their customers. We recognise that 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.