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Holistic intelligence and the limits of Big Data

Big Data has been heralded as a ground-breaking technology that will transform the way that businesses operate. As such, major organizations have been quick to understand and implement Big Data analytics in a number of different ways. The most common implementations have to date been limited to internally-focused information, through fairly standardized techniques. There are therefore a number of externally-focused areas in which Big Data technologies could be further utilized, as well as the potential for stretching the industry towards ever more complex analytics. Nevertheless, there must also be some limitations to the scope of Big Data, some of which could potentially be found in the limits of computers themselves.

The dominant operational feature of Big Data in recent years has been a restriction to internally-focused data, either from information about the company itself and its processes, or through the interaction of customers with that company. For example, in terms of analyzing customer generated information, Big Data has been used by NASCAR to help it understand the data that can be collected about its 75 million fans through social media, broadcast media, and traditional press outlets. Tying all of this information together through an analytics and visualization package, NASCAR is able to understand public sentiment and react to fan and press impressions of the sport extremely quickly. NASCAR is therefore able to tailor its public interactions based on a real-time understanding of public perceptions.

Externally-focused Big Data analytics could be the next level of competitive advantage for businesses. In short, Big Data is likely to get even bigger, shifting from marketing and operational optimization to a broader understanding of the business climate and therefore the ability to inform which long-term strategies to undertake.

In his 1989 book ‘The Emperor’s New Mind’, mathematical physicist Roger Penrose outlined the potential limitations of computer algorithms as a way to achieve artificial intelligence. In brief, Penrose argued that there are parts to the functioning of human intelligence that we do not understand, these parts may be outside of our understanding of physics, and these parts cannot be replicated by computational methods. Whilst this is somewhat of a digression, it does have some relevance for the Big Data industry. Broadly, once Big Data analysts master a wealth of algorithms that can be applied to large datasets, companies will begin to seek non-computational insights and find those areas of perception that cannot be performed through a computer program. In other words, human understanding and communication will gain more value in terms of the ability to comprehend Big Data output and the ability to understand what questions need to be asked about a business.

The combination of externally-focused Big Data analytics and non-computational insights could be termed ‘holistic business intelligence’.

To explore the Big Data industry in detail, see our latest case study – ‘Big Data: A road map for business intelligence‘, which provides a comprehensive understanding of the core technologies and key players. It also analyzes the uses of Big Data in terms of business intelligence and seeks to identify potential gaps in the market, giving an outline of a conceivable future for the industry.

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