The Uselessness of Big Data… Without Proper Data Analytics!

The Uselessness of Big Data... Without Proper Data Analytics! Image source: Cukier

The Uselessness of Big Data… Without Proper Data Analytics! Image source: Cukier

Big data is of great interest to many organisations as everyone says so… but is it really ? In 2013 eight executives got together to share their perspectives thus far on the biggest challenges in this regard, as reported by Brad Brown, David Court and Tim McGuire for the McKinsey Quarterly. The goal was to review a number of different concepts with regard to big data, such as whether it has been overhyped, if privacy threatens progress, if talent acquisition is slowing up progress, the organisational models that work best, and the most effective ways to assure adoption. Those included in the discussion were from major companies such as American Express, Samsung Mobile and Wal-Mart Stores, among others.

An interesting outcome of the discussion was that rather than data and analytics being overhyped they have been oversimplified. There are many challenges that arise from big data that organisations may not be ready for due to taking an over-simplistic approach. One of the biggest areas of opportunity was found to be in customer-facing activities where prices can be optimised for services in customer life cycles, and marketing spend can be maximised. Improvements can still be made in this area. There are also internal applications for process efficiencies and even ways to balance customers and internal processes to make choices that will best serve customers. However, privacy controls were highlighted to be a challenge that needs to be dealt with. The executives discussed how providing customers control in this regard can be helpful in order to build trust. For example, customers could opt-in or out of data collection and companies need to earn customers’ trust.

Another interesting challenge discussed was that talent is needed in terms of IT talent. Some companies have managed to succeed in this area, finding the right people for open positions. However, one of the other big challenges was found to be hiring those that are able to bridge the gap between those in IT and business decision makers, referred to as “translators”. These people need to understand big data and how it operates as well as how the business operates and what it needs to be able to communicate between the two areas to make sure that the organisation gets its needs met. This particular talent was found to be quite rare.

Of course as Ariker, Breuer and McGuire (2014) point out, simply gathering big data on its own is not enough. They also highlight the great important of translators, and emphasise its critical importance in being able to succeed with big data programmes. The challenge is that the skills needed are hard to find. Such people need to be able to understand a mixture of computer programming, finance, statistics, marketing, psychology and economics. It’s a lot to ask of one person. The reasons that these skills are needed however, are manifold. One example can be seen in business managers who do not really understand what data is available, and they need educating in that regard so that they are able to get the most of big data. Translators are able to create requirements from business discussions that can actually lead to clear information that can be used to create useful systems.  All of this needs to be done, quickly and flexibly to be able to meet business needs.

A recommendation that was made was that a “centre of excellence” is needed. These centres are needed to provide help to business leaders. It was argued that this should be housed in an area of the company where it can have a strategic impact. This could be in a variety of places – maybe sales, marketing or IT, depending on where it can best be utilised. Such centres can be used to build formidable data analytics capabilities and taken on new and more sophisticated projects as well as deliver new insights to the organisation.

To increase adoption it was recommended that either automation or training were needed, but that both would require investment. These areas both help address one of the biggest challenges with big data – which is getting people to use it. Automation can be used to develop end user interfaces that are user friendly and this takes some of the fear out of big data. Training of course increases knowledge and understanding of the importance of big data.

 

 

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