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How big data is going from strength to strength

The world of big data is constantly evolving and growing, with vast amounts of data produced every day by the technology that surrounds us. We have information from our phones and PC’s, from our cities and towns, workplaces and government, for everything we buy and everything we experience. Until recently, the focus within the data community has been on understanding and finding the hidden insight within data. Now, as big data shifts from being a buzzword to being the norm, the way we work with data is changing. Let’s take a closer look.

Placing data in context

There is little value in having a large pool of data, unless you know how to ‘read’ it. The real value of data lies in the relationships between data sets. Traditionally, we’ve focused on finding insights within data and others have marvelled at how those of us who can ‘read’ data are able to find meaning. But as business demands on big data grow, and businesses become more dependent on the data they collect, it’s becoming clear that more is needed than pure insight. We need that insight to lead to action and to have a clear impact on improving a business or solving a problem.

From lakes to warehouses

Because of the amount of data available, we’ve historically focused more on being able to collect that data and store it successfully, over trying to find out what the data can tell us. It’s a route which Charles Araujo explains in his article on CIO, “The focus was on building massive data lakes that collected every piece of data imaginable with the mind-set that it would, at some point, be useful. But that approach is proving difficult to sustain.”

Now, he argues, focusing on the mechanics of big data isn’t enough. We need to move away from data-lakes, which simply store an organisation’s data and move towards enterprise data warehouses (EDW). With data lakes, raw data is stored in its native format and the relationship between data sets is lost. While data warehouses store data which has been structured, so the relationships are left intact. Both have their benefits, depending on what you need from your data, but EDW have a much stronger focus on reporting and analysis to discover actionable insights.

Improving data analysis

With this results-driven approach to big data, there is a growing need for IT solutions to help analyse data of this size. While AI might be the new favourite trend of the moment, it’s machine learning which is stepping up to the mark. According to Cynthia Harvey, “Machine learning is a branch of artificial intelligence that focuses on allowing computers to learn new things without being explicitly programmed. In other words, it analyses existing big data stores to come to conclusions which change how the application behaves.”

Along with machine learning’s cousin, predictive analysis we can now not only look at data to discover what has happened and why it’s happened, but we can also begin to predict what might happen next and begin to adapt ourselves. The combined power of machine learning and predictive analysis will pave the way for more actionable results from big data in the future.

The good news is, with an increasing demand on big data comes an increased demand for people who understand and are skilled at analysing data. And at Capita IT Resourcing, we’re no exception. If you’re interested in finding out more about the kind of big data roles we have available, don’t hesitate to send us your CV now!

Read our related blog: How advances in data and analytics are changing the world of work

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