Five things in data analytics that defence must learn from industry...

An interesting piece here from one of our experts, Lorna Weir, on data analytics looking at some of the lessons defence organisations can learn from the commercial world’s experience of bringing big data to life.

The majority of big data strategies have emerged in the commercial sector from companies perceiving data analytics as a route to competitive advantage through better decision-making, more accurate predictions, and finding efficiency gains.

In defence, the use of commercial-scale data analytics has not yet taken hold. There are many instances where large volumes of data are being used to achieve operational advantage but due to the enormous variety of the data available, the added complexity of data sensitivity, and the sheer scale of the environment to cover, defence remains a fast follower rather than an early adopter.

That could well turn out to be an important advantage. Companies have made great strides harnessing the value of their data, but along the way they have made plenty of mistakes too. Defence can learn from their experiences and avoid some obvious pitfalls.

In this article Lorna outlines five key lessons defence should learn from commercial’s big data journey so far:

1)     Understand the limits of what you have - Organisations believe that within the data they have amassed over time, there are indications about how to become more effective and efficient. Their starting point is often an assumption. The reality is usually very different.

2)     Don’t underestimate the value of open data – While many organisations want to explore their proprietary data to generate new insights, there is a growing collection of open data which is available for anyone to use. Defence must recognise that it can use this data as easily as anyone else and harness the value of the community that supports it.

3)     Be happy to work in the cloud – An awful lot of big data capability is now moving to the cloud. Defence is not a rapid cloud adopter and moving away from suppliers’ default option can be costly.

4)     Embrace automation - As the amount of available data rises so does the need to use real-time analysis to cope. People cannot work fast enough. An automated approach to some elements of data science is becoming more prevalent.

5)     Accept failure as part of the process – Data science is not a zero sum game. The process of finding useful insight in the swathes of data available to most organisations is a long term commitment that involves succeeding through failure.  

Take a read and let us know your thoughts... https://bit.ly/2n11dZQ