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Big Data

Big Data Analytics  - enabling Bill Gates’ vision

In 1999 in his book “Business @ the speed of thought” Gates talks of how future technology would provide a business with a nervous system that allowed it to respond to opportunities and threats.

He envisaged personal devices that didn’t need a wired connection to the internet accessing real time data from the world around us.

Sixteen years later the devices and data access are there for every business, the challenge is how to take competitive advantage by turning the data into actionable information.

As the data warehouses become data lakes the amount of data on which to base decisions becomes a challenge in itself. How much can the nervous system handle without overloading?

This is where Big Data Analytics becomes the differentiator.

Most analytics systems developed over the past 10 years have been targeted at mining structured data to provide reports back against individual business requests.

The speed at which the response or answer is returned has not been key as often the decision to be made is not immediate. This is now changing. A driverless car doesn’t have 5 minutes to make a decision, it needs to be sub-second. The data it is receiving combines variables which change very slowly such as tyre wear, brake wear and weight with information that is constantly changing such as the immediate surroundings, the road surface, the weather and other cars around it.

The data has to allow the car to predict what might happen and be able to compensate – lights change to red, rain on the road ahead an accident closing one lane, all the things we take for granted when driving ourselves. Technology and analytics provides the car with its own nervous system.

So if we can use analytics as this level to drive a car how can we use it to manage our businesses – to become the nervous system that Gates foresaw all those years ago.

Imagine a telecommunications company analysing their customers call data. Today they can tell how many calls you made, how many were dropped, where you moved between cells and when you call them take a view on the level of service you have received. All of this data is historic and might help them with making new and appropriate offers when you call.

Now imagine this as a predictive analytics system. It knows historical data about you, knows your route home each night, knows where you lose a signal and sends a reminder that you will lose it again at 5.45 on your current route. No more complaining about always losing a call half way through a conversation.

A retailer has historic data about what you have purchased over the years. Imagine walking back into the shop in late August and being reminded by a mobile message where their boy’s long school trousers are because the analytics have worked out you are probably looking for boy’s school clothes as that’s what you purchased twelve months ago. And when you do buy them it reminds you of other items you purchased with special offers to keep you in the store.

Any individual example is easy to understand. But how do you provide millions of these service points every minute? That is the challenge we are addressing with Big Data Analytics.