In 1999, a Berkeley study estimated that the world had produced 1.5 billion gigabytes of information. In 2003, the replicated study found that number to have doubled… in just 3 YEARS!!!!

In 2008, the term Big Data become popular in computer science, where scientists began predicted that ‘Big Data’ would transform the activities of companies, researchers, defense and intelligence operations and more.

The problem is, Big Data’s definition, as defined in the Oxford English Dictionary is “Data of a very large size, typically to the extent that its manipulation and management present significant logistical challenges.

And so fast forward 8 years, and large organisations have invested billions into the research and management of this Big Data. They’ve created tools, algorithms, and other systems to manage this data, in such a way that provides benefit to their company.

And it did, the scale of the impact has still not been fully realized. Data that used to take literally years to analyse, has now come down to mere days.

But what exactly IS Big Data. Well, it’s really anything and everything that is recorded. The data itself isn’t really the issue – working out what to use and what to ignore is. You see, every keystroke on a computer, every phone message, every checkout transaction is recorded. And without getting all tin foil hat, this information is out there in the ether. But there is such vast quantities of it (remember it doubled in just 3 years back in 2003 – Imagine how far it has come now!) That making sense of it all can be an absolute nightmare!

So I’m less interested (though it’s still fascinating) how big business and governments (yes I said governments) are using Big Data, what I am more interested in, is how can small companies work with (Not so) Big Data to the same principles.

Well, the first step is actually gathering the data. Every business has hundreds of data points that they are not recording or analyzing. Think about the lifecycle of a client, from when they first contact you, right up until they die. There’s the basics, such as phone number, address, email etc. But there’s other low hanging fruit that you can start to record – Do they pay by credit card or cash? is it mastercard, visa or AMEX? What’s the average order value? Are our customers male or female?

These are real basic ones. But there are others, when you start to explore the possibilities – How long does a customer spend on your website? What page do they exit on? What areas do they ignore? What device or operating system are they using?. Then deeper – what other websites do they visit? Which social platforms are they on? Are they looking at home or at work?

There are so many tools to assist with these data points, and the more data you have, the better you can analyse, and make decisions based on that information.

For example, imagine, you had 400 data points on 1,000 clients. It’s realistic to assume you would be able to notice some trends or patterns in their behavior or buying choices. It might be working out that 3 bedroom homes are typically purchased by retired couples, or that your acreage properties are being purchased by people aged 45-50.

With this kind of information, you can really laser direct your marketing to a very bespoke audience. And you can invest your marketing dollars in the right place, rather than a scatter gun approach.

We have become used to the ability, thanks to the likes of Facebook, to be able to target a very segmented audience, such as where they live, their location and so on, but if you don’t know which of those audience actually do business with you, if you are simply assuming you know, you are still aiming blindly.

The great thing is, you don’t necessarily have to collect all of this information yourself. Organisations are starting to see that switched on small businesses want to be smarter about their targeting. Organisations like Corelogic now have products specifically designed to assist your efforts. And other companies, like list brokers, are able to take your existing data, and profile it against various sets, to provide you with a deeper understanding of how you are already working with.

Big Data might feel like it is out of reach, but there’s lots of simple things you can do every day to start putting the power of data back in your hands.