Big data is the word of the moment: every year, the quantity of business data doubles, and nearly 70 percent of firms are investing or planning to invest in analytics to glean business insights from this information. It is thought that better analytics could unlock $100-$200 billion globally.
The question is, where is the big data juggernaut heading? The next phase in big data development is focusing not only on the data and technology, but also on the information, ideas and insights it provides. It’s no surprise that with the exponential increase in data gathering, the search for skills in analytics and data-driven decision making has become a crucial requirement for organisations.
To use big data to its full potential, businesses must consider the entire cycle of creating, translating and consuming analytics. Analytical output has no value on its own accord, it must be converted into key findings, insights and recommendations that the organisation can act upon. This requires best practice processes and the right people to work with the data, to offer what a machine cannot.
Creating a test and learn culture
When done right, analytics will nurture business success by driving innovation and better decision making. We know from the experience of thousands of customers that supporting decisions on data-based insights, and creating a ‘test and learn’ culture is much more fruitful than relying on gut instinct.
To get the most out of data, organisations should consider the following:
There are a number of steps involved in the process of handling important data, and it continues to evolve. Primarily, business problems should be articulated and translated into analytical problems. This will then allow you to solve the analytical problems. Once these analytics solutions have been identified, they need to be translated back into business solutions.
The benefit of these data-driven decisions will only be realised when the business solutions are communicated, implemented and consumed by the whole organisation. This man-machine approach when it comes to decision-sciences is a major shift from the way data analytics has traditionally been handled in the past.
Previously, a data team would consist of mathematicians, IT analysts, engineers and business professionals. But with a mixed team working to reach one conclusion for each data set, the practice becomes time-consuming. This is why demand for individuals with combined maths, IT and business expertise is high.
Such individuals are better equipped to analyse and evaluate the data, as well as create business models based on the information. In addition, the layers of business design thinking and behavioural sciences give the same individual the ability to provide data-driven ideas. That is the final piece of the puzzle. These people are known as decision scientists, and they are even more rare – and increasingly more desirable – than pure data scientists who usually posses a combination of maths and technology skills.
It’s clear that analytics is changing the way business operates, and we’re witnessing a shift in culture towards data-driven decision making. In order to fully reap the benefits of big data, businesses must realise the benefits of a man-machine ecosystem and form models to govern and optimise all aspects of creation, translation and consumption of data. Better decisions will always lead to better business.
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