30 September 2014

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Sean Bowen - Push Technology

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2013: The Year of Real Time

21 January 2013  |  3202 views  |  0

A recent blog post from analyst house Forrester has predicted that real-time analytics will be a key focus area within Big Data in 2013. The reason? The ability to quickly take on board new information and react to it is at the heart of business decision-making. To quote Forrester, this means being able to “quickly process incoming data from all digital channels to make business decisions and deliver engaging customer experiences in real time”.

 

Real time.  This is going to be at the core of business communication, make no mistake.  It’s undeniable that predictive analytics is becoming a Big Deal when it comes to Big Data. Failing to use the data which a company collects to effectively inform business decisions is a senseless waste.  Furthermore, using it to anticipate emerging trends and changes in the market is the central way in which organisations get ahead of the competition. Vast quantities of data are now being gathered on an ongoing basis, so the challenge is how to stay on top of it and ensure it is being stored securely so that it can be harnessed for informed business decision-making, as well as being sent efficiently so that resulting decisions can be executed in real time.

 

As Forrester has predicted, this means switching from simply mining Big Data stores.  The emphasis has to be on real time analysis; otherwise the big data loses it’s ‘bigness’ or in other words, its value. Business value is derived from reacting to the most recent information as quickly as possible, meaning timeliness is critical.

 

When it comes to data, the process and effect of data mining and real-time monitoring can be compared to different approaches to fishing! Data mining is like sitting in a boat and dropping your net into the water, hoping to catch a ‘fish’ (a business-valuable piece of data), whereas real –time monitoring (working in real time) is like putting a net across the river that feeds into the lake. With the former you keep having to ‘drop your line’ to catch the valuable data in lots of different places - making it time-consuming and unlikely that you’ll react as quickly as you could - whereas with the latter (real time monitoring) your ‘net across the river’ means you only have to fish in one place and you’ll catch all the freshest data.

 

Anglers aside, enterprises must invest time and apply tools that enable them to identify deeper trends and ongoing patterns in the information they hold and receive, in order to make better informed longer-term business decisions.  These can often be more readily identified by assessing data which has been gathered over a longer period of time, and frequently this is where valuable insight lies. For example, in trading, much of the value of a stock price update is found in the difference between its current value and its value five seconds ago, five minutes ago, or yesterday.  Data becomes stale very quickly, and the window of time in which to respond is continually shrinking. Timely reactions to new data give competitive edge, so adapting in real-time to information is where 2013’s leading financial organisations will be looking to improve.

 

The key technical challenge with Big Data, as the name suggests, is the quantity. With so much information to analyse, using Big Data effectively can seem like an impossible task. But if data is analysed in real time, as it arrives, rather than mined as part of a colossal store, then the amount of data being examined at any one time is significantly reduced, and as a consequence becomes more manageable.

 

Data is the most valuable asset of any business – particularly within Financial Services - and having access to that information in real time, especially when information changes in microseconds, can have a real influence on the success of an organisation.     

 

We are already working with brands and businesses enabling real time solutions, and I have no doubt that 2013 will see an increase in businesses investing more in applying those technologies that can fully deliver on their data assets in line with this continued trend. 

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Sean Bowen

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