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Overcoming the Three Big Data Hurdles: Three ‘Vs’ or Four?

| 16th February 2015 | No Comments

Alex Watson-Jackson identifies the key barriers that continue to prevent businesses from realising the true value of real-time business analysis of big data, and offers tips on how to overcome them.

Big Data Analytics

Big Data Analytics

When it comes to unleashing the full power of real-time business analytics, most commentators refer to three primary hurdles – the three Vs. The argument is that, with proper planning, it is possible to address the three Vs and realise the true worth of big data.

In my view, however, that is only three quarters of the truth. At Logicalis, we think of the road to unleashing big data in terms of four Vs, not three – and that missing V is arguably the most important:

  • Too Much Data – This is the first V, volume. The amount of data companies store continues to grow exponentially. Massive amounts of data are captured as enterprises try to learn all they can about their customers – but data alone tells us nothing without analysis.
  • Too Many Types of Data: The second V is variety. The challenge is not just associated with the amount of data, it is also about making sense of a huge variety of data types, drawn from all kinds of different sources – some of which you control, such as in store RFID tag data, and some you don’t, like social media stats or content. With this in mind, supporting meaningful data analysis means carrying out a well-planned infrastructure transformation, to ensure that information can be managed and aggregated as quickly and easily as it is created, wherever it is created, and converted into usable intelligence.
  • Too Little Time – The last of the traditional big data Vs is velocity. Data is being delivered faster than ever and needs to be turned into usable intelligence just as fast – in retail for instance, there is no advantage in identifying that your customer is price checking using a mobile if you don’t react quickly to offer them the same online discount in store. Implementing a big data strategy that is able to keep pace with data velocity means changing business processes, infrastructure, economics and even the org chart. At least in the short term, turning to a solution provider or analytics consulting firm as additional resource can take the pressure off the internal IT team, while using an expert in the field from the start can make the difference between the success and the failure of a big data project.
  • Not Enough Budget – Big data discussions can lead to conflicts between the information management and technology sides of a business. An integrated approach that provides instant access to data via a unified system may be ideal, but it can be costly. At Logicalis we see the solution to this conflict of this in terms of a missing V in big data – value. The key is to work your business unit leaders to define the value that a project will bring. Making sure they see and understand the business value proposition changes the funding model for your big data project – rather than having to find space in your existing budgets, you are equipped to ask business unit leaders to sponsor fund it.

Every organisation may be different, but the challenges related to big data and real-time analytics are often familiar. So, for help in turning your company’s big data into valuable information that drives business decisions, visit http://ow.ly/CAHaz.

Alex Watson-Jackson

About Alex Watson-Jackson

Alex was Proposition Manager for Agile Data Centres at Logicalis UK between 2013 and 2015. He was responsible for the development and marketing of the data centre products and services for Logicalis in the UK. Prior to joining Logicalis Alex spent 13 years in the Managed Hosting industry in product manager and technical sales roles for Telenor, Cable & Wireless, Global Crossing and Colt. Alex began his career in the British Army where he spent 10 years leading engineering units and he has a BEng in Electronics Systems Engineering.

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