Big data success – what is required

Nov 12
11:43

2015

Innes Donaldson

Innes Donaldson

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Big data success – what is required - and how to go about this.

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Making the most out of big data can be a daunting task for even the largest of businesses,Big data success – what is required Articles and the sheer volume of raw data alone is enough to give IT teams a headache. Collecting, storing and processing data has become more sophisticated in recent years, but turning raw data into insightful information still requires an expert touch. Analysis lies at the heart of the big data trend, and the effort and expense of collecting the data is wasted without a considered approach to using and understanding the information. 

Data analysis has been at the heart of business intelligence for some time, but analysing the vast volume of disparate information that underpins big data renders traditional approaches all but useless. To this end companies wishing to exploit big data need a team with the appropriate skills. With this in mind a successful big data team will need to start with a data scientist. Regardless of their field of expertise the base level of analytics expertise inherent in data scientists gives them the scope to spot trends in data that others might miss.

A big data analysis team will need coding and programming skills, along with familiarity with platforms. With these skills, such tools can be tailored to extract the right datasets from a mass of raw information. This allows analysis that links with business objectives, rather than leaving a data scientist to sift through a deluge of disparate information. Technology by itself is not the silver bullet, and there is no benefit to collecting lots of data just because you can. Big data needs robust analysis that is relevant to the business. Technology is a critical enabler only after you have figured out the first part of the equation. 

A company can extract the most valuable information from big data only by combining an understanding of traditional and business analysis with the appropriate technical skills. However, finding such skills is becoming increasingly difficult, as demand for people with relevant IT and digital skills is outpacing supply. Finding skilled data analysis specialists has become challenging, particularly as market demand makes them costly to hire. But alternatives do exist. It's possible that they may already employ people with existing traditional data analytics skills that can be skilled up. If the big data project is simply a challenge of scale, it is possible that existing team members could manage the workload with help.