Skip to content

When Is Big Data Analytics A Waste Of Time?

MFG Archive

Is there such a thing as too much data? Can data analytics be a waste of time? We examine Adrian Bridgwater’s views on the topic of wastage


As discussed in a previous article, ‘Internet of Things’ is quite possibly the buzzword of 2014. It’s estimated there will be 30 billion interconnected devices surrounding us as soon as 2020. With this plethora of new devices comes the prospect of a huge influx of big data. In this recent Forbes article Adrian Bridgwater questions how valuable all this data is and the point at which “enough analytics” has been reached.

As with all of our News Roundups, there is much to be learnt wearing our social sector hat. Bridgwater illustrates that the first rule of data usefulness is determined by the “point of diminishing returns where the analytics effort outweighs the potential gain.” He uses a football example to demonstrate “enough analytics” is established when “no further incremental value” can be added from the data to develop the performance of a footballer. For instance, data on possession translates to highly useful statistics but there are many other types of information that are not value adding at all, and it is up to us to make this distinction.

Bridgwater goes on to demonstrate that data is both intriguing and worthwhile when used in the right context. Continuing his footballing theme, he presents a fascinating fact showing that in the last two Football World Cups, Germany have significantly increased the pace of their play, which could be further analyzed to identify if this was their formula for success this time round.

Furthermore, an interesting distinction that comes out of this article relates to how different data is treated before it is analyzed. David Jonker, director of big data at SAP, a multinational software corporation, explains how SAP “work at speed to work on a single copy of data with simplified applications with fewer moving parts.” On the other hand, Bridgwater explains how high cost transactional data would require multiple copies before any analytical tests could be carried out.

Overall Bridgwater provides a strong case proving the need for big data analytics in some very timely practical examples. However, there should be a clear understanding of where the point of diminishing returns is, in order to prevent big data analytics being a waste of time.


Many thanks to Forbes and Adrian Bridgwater for highlighting the importance of determining the type of data that is relevant for adding value to our organizations. Be sure to follow both of them for further insights to this fascinating topic.