Your organisation probably has a lot more data to hand than it realises – and almost certainly a lot more data than it actually uses. That data can just sit there collecting dust (or whatever the electronic equivalent is of dust on digital data) – or you can mine it to find nuggets of pure gold that can offer surprising solutions to deep-down and dirty problems. Like blocked sewers.

Analysing data ‘before anyone has looked under a manhole’

New York City suffers, like any major city, from blocked sewers. According to a 2012 New York City press release, sixty per cent of all blockages were caused by grease, most of it illegally flushed into the sewage system by restaurants who were supposed to use the city’s licensed grease haulers to dispose of waste fats. Using clever data analysis, the city began to focus in on likely culprits. ‘Before anyone has looked under a manhole or opened a grease trap, our analytics tools are using data from three different agencies to help us choose targets more wisely and enforce the law more effectively,’ said the city’s chief policy advisor.

Geo-spatial data on the sewage system and records from various agencies were combined to identify restaurants situated near recorded blockages that were not using the city’s grease hauling service. An investigation of this shortlist of restaurants resulted in a 95 per cent increase in the identification of grease dumpers.

‘I used to make 6 reports a month – with Mittal, it was 66’

Lakshmi Mittal built a global steel empire, ArcelorMittal, largely by acquiring poorly-run, loss-making and often state-owned steel plants around the world and turning them into profit. Mittal used his plants as a source of invaluable data, noting the most efficient practices and using them as a benchmark to improve performance everywhere else.

When Mittal acquired steelworks in the U.S. as part of his acquisition of International Steel Group (ISG), one ISG manager told Fortune Magazine, ‘I [used] to make 6 reports a month. With Mittal, it was 66 – how much oil were you using, how many units of electricity per hour, how much time for repairs, in minute detail at every step. They’d come back and say, “Why are you using more than the plant in Kazakhstan?’” And we’d try to figure out what they were doing better.’

Amazon: over 500 metrics

Amazon measures its performance against over 500 metrics, eighty per  cent of which relate to customer objectives. These are the metrics that feed the complex algorithms that make Amazon’s ‘recommendations’ so sophisticated. The company drills down to fine levels of detail, discovering, for example, that a 0.1 second delay in delivering a page to customers’ screens results in a 1% drop in sales.

Take-outs

• Data from a variety of sources can be brought together to produce highly valuable analyses
• All data is potentially valuable; compare performances in different parts of the organisation
• The more you know about customers’ behaviour, the more you can offer them what they might want
• Drill down deep; small shifts can turn into significant revenue

‘Mine the Data’ is explored further in 100 More Great Leadership Ideas