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What can IT leaders learn from the data analytics driving Formula One?

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Behind the human endeavour that makes top sporting events tick is bleeding-edge technology, and nowhere is this more apparent than in the field of data analytics

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Sport is in the global spotlight: the highly-anticipated Rugby World Cup has kicked off to great excitement, the ever-popular F1 championship is revved up and gaining momentum while golf fans, tennis fans and followers of a huge choice of world-class sports all have engaging competitions to enjoy.

However, behind the human endeavour that makes these top sporting events so compelling there is a fast growing role that technology is playing behind the scenes. Nowhere is this more apparent than in the field of data analytics

It is now possible to leverage the value of the more and more complex data generated in sports – for example the telemetry generated by a F1 racing car or the multifaceted data generated by analysing a professional tennis player’s serve – and to extract useful and actionable intelligence from it. Take for example the application of new analytics techniques from Intel and HP which are helping teams optimise performance in the Cape2Cape 2015 Challenge - the world’s fastest drive from South Africa to Norway; -or, indeed, at the Wimbledon tennis championships.

And it is not just in sport that big data is adding a competitive edge. Enterprises across the globe have been leveraging big data and analytics technology for real world competitive advantage too. For some time structured data has been effectively leveraged for example by sales and marketing teams extracting actionable intelligence and commercial value from structured customer data such as names, addresses, DoBs etc. But the value that lies in unstructured data is still currently a widely untapped resource. As storage costs in the data centre have plummeted companies can now more cost effectively collect increasing volumes of unstructured data including audio recordings of contact centre telephone calls, emails, social media content, video content, text messages and so forth, and find that looking beyond the traditional relational database is extremely rewarding.

One such example of leveraging unstructured data is through the analysis of social media data. This plethora of information can provide valuable insights into both the behaviour and preferences of customers and potential customers, in addition to valuable reputational information about the way in which a company and its competitors are perceived. Unstructured data analytics help companies to gain competitive intelligence and actionable insight into areas including pricing, fraud detection, productivity levels and systems monitoring – an area of particular interest as IoT gains traction and the volume of connected sensors in the enterprise rises.

However, tapping into this mine of information and extracting business intelligence from it creates challenges for IT managers. The massive scale and growth of unstructured data means that organisations need to be prepared for the so-called “Three Vs”: the volumes of data that need to be stored, the variety of this data and the high velocity that analysis of this real-time data requires.

The key is to configure and deploy an agile and elastic cloud and on-premises infrastructure that is constructed to address the distributed, large and data-intensive workloads from applying analytics to unstructured big data. Such infrastructure needs to be capable of spreading the workload across clusters of nodes on a dynamic basis. And of course a blend of cloud and on-premises storage is of critical importance when –handling such large data sets.

Alongside this, intelligent capabilities such as data deduplication have a growing role to play, and network infrastructure should also be up to the task of quickly importing very large data sets and transferring them across nodes as required for analysis. The adoption of such tools as the Apache Hadoop framework is one way that organisations have incorporated a cloud-based implementation of the open-source framework for reliable, scalable, distributed computing, data storage and data analysis.

In the commercial world lessons can be learnt from the sports that are already expanding the datasets collected to enhance the performance of athletes and vehicles. By applying advanced analytics to the resultant data, new levels of understanding can be opened up, decision-making can be improved and commercial differentiation and agility can be driven. Big data if handled intelligently can deliver big opportunities.

This article is brought to you in association with Intel

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