In May 2011, McKinsey Global Institute (MGI) published its report “Big data: The next frontier for innovation, competition, and productivity”. It outlined the great opportunities offered by the exponentially increasing amount of data available in the world. So-called “big data” would become a valuable resource not only for businesses but also for governments and, more generally, for overall society.
Today it seems that companies have learnt to extract only a small percentage of value from them given the levels forecast several years ago by McKinsey, with players struggling to find effective ways to take advantage of this enormous technological disruption.
What are the obstacles preventing companies and other institutions from doing so? What can be done to ease the transition to big data solutions and fully release its disruptive potential?
First of all, the potential of the big data revolution is linked to the information that can be extracted, not the data gathering itself. But there is more: information alone can not make a difference, and its full value can only be exploited if the information gained is used as support to the decision-making process.
CEOs must not assume that they will be able to achieve better performance by putting sensors everywhere in their organisations and by storing the data collected in big databases: data collection needs to be supported by a change in the structure of the organisation at the operational level, so that the information extracted can be immediately used for making faster and better business decisions. It is a matter of architecture: companies need a more flexible organisational structure to embed the lessons learnt from the data as soon as possible in the processes and, in doing so, to obtain an advantage over their competitors.
Of course, this is no easy task. However, the key message is that companies that the first companies to achieve this level of flexibility in their operations will have a very fast learning cycle and will come to fully capture the value offered by data. Until then, that data will remain unused in huge databases, locked up by the structural inertia. The innovation of the big data revolution lies in the usage of the data rather than the mere data collecting activity.
New competencies and capabilities needed
Another obstacle towards the possibility of deeply exploiting the flow of data the world has been flooding in is the fact that companies lack professionals with the right mix of skills required to effectively master the potential opportunities that are at stake.
Data scientists analysing the data and building models are not enough. New figures with quantitative skills and business acumen are needed to transform numbers into business decisions. Currently, they are missing in the organisational charts of existing companies. These professional figures were named by Matt Ariker, Peter Breuer, and Tim McGuire in their article “How to get the most from big data” as “translators” – a reference to the fact that they should be able to translate the messages they get from the data into practice and take an active role in the decision-making process.
Companies are struggling to create suitable roles because it is not clear how executives should insert them in the organisation and what kind of tasks authority they should be assigned. Again, this is due to inflexible organisational structures that do not allow these new professional players to add value to businesses. C-level executives must work from within to create an organisational architecture characterised by procedures and processes that will give translators the possibility to operate without having to deal with rigid structures.
A Gradual Transition
Achieving a similar objective is tough for those companies whose structure and well-defined procedures are seen as the foundation under which a cash-cow business was built.
However, not realising that deep changes are needed, and that competition is moving towards data harnessing, resulting in maintaining the old way of doing business, is riskier than the possibility to harm the existing situation, at least under a long-term perspective. So what are the alternatives for executives when thinking about how to integrate big data roles into their companies?
The most suitable way of managing gradually this kind of transition seems to suggest the usage of M&A transactions. Since big data startups are experiencing a sudden and peak growth in number, driven by the awareness of the unavoidable change in competencies that businesses need, a savvy strategic move could be acquiring an existing big data business rather than implementing a new department from scratch in the actual structure.
This way, the organisation maintains its old processes and procedures (at least in the early stage of the acquisition), giving executives the possibility to leverage on analytics competencies without the risk of dramatically threatening the successful business. Simultaneously studying the interactions between the two entities, with the eventual decision of integrating them in a more rigid and interconnected way if the need should occur. That is the way of acting and the root of the success of Google towards its several investments on new and potential future businesses. It is a gradual way of supporting the structural innovation needed, and the most suitable for a great percentage of incumbents.
The Biggest Risk Is Not Taking One At All
One thing is for sure: companies will have to face this transition in a structured and organised way, since there are a lot of natural and physiological barriers that prevent them from leveraging on such a great opportunity; the ones that will manage to transform themselves in the shortest time possible will benefit from a substantial competitive advantage.
The data flood has begun: will you drown in your data or will you try to build a raft?
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