In 2010/11, the London Fire Brigade handled a total of 212,657 emergency calls and had an average response time of 5 minutes and 34 seconds.

But what do firemen do in their downtime? Eat bacon sandwiches, lift weights and sleep? Think again.
Public sector services are increasingly moving towards a proactive, preventative service, and the London Fire Brigade is no different. The London Fire Brigade carries out 70,000 home visits each year, educating the public, carrying out safety checks and fitting smoke alarms. But with 3.2m households and a population of 7.5m, how do they know where to send their officers?

Andy Mobbs, Risk Information Manager, has taken a radical new approach to identifying high-risk households. Previously, he explained, analytics relied upon historical modelling. But deaths by fire are relatively small in number, so even minor variations can skew the dataset. “We wanted to move away from historic incidents, seeing where fires happened in the past and instead explore where they might happen,” says Mobbs. He turned to enterprise-grade data analytics software more commonly found in the City’s insurance brokers and fed in over 60 different data inputs, including geographic area, property type, age, economic circumstances and more. “Results help ensure home visits are based on targeted calling rather than random events – to ensure people are in specific districts, knocking on the right doors in the right streets, even down to a postcode level,” says David Wyatt, Head of Information Management, LFB.

This intelligent, joined-up approach has enabled the London Fire Brigade to target their resources where they will be most effective, in areas of highest risk, and it is by no coincidence that deaths caused by fire in the capital have fallen by almost 50% since 2010.

According to the Policy Exchange think-tank, use of big data analytics could save the public sector between £16bn and £33bn a year. Their 2012 report, entitled ‘Making Government faster, smarter and more personal’,  suggests that savings can be realised through increased data-sharing, and points to the existing linkup between the DVLA and the IPS (Identity and Passport Service) as a basic example. By checking the IPS database for a valid passport photo, new applicants with the DVLA need not provide photo ID or a signature, saving time and adding an extra layer of security.

The public sector has long trailed behind the private sector in technology investments, with even basic initiatives like Tesco’s Clubcard (now 22 years old), counting on over 15m active members in the United Kingdom alone. The data is stored on a 40-terabyte database, and Tesco estimates that since its inception, the Clubcard has saved them £350m a year on expensive blanket marketing campaigns. This also allows Tesco visibility over the buying habits of their individual customers, to stock the right products in the right stores, and lure back customers heading for cheaper retailers. Back to the present, insurance provider Vitality offers personalised health insurance premiums and rewards customers for visits to the gym with free cinema tickets, coffee and more. They instantly know which customers are most active, and by extension which customers might cost them more to service longer-term.

It should be clear to see that not only does big data enable insightful data mining – a data-mining program in the US coordinated by the FBI to link up and clean existing population data reduced Medicare fraud by $4bn – but it also opens up the realm of viable predictive analytics. We here at Prestige Network have enlisted James Ambler, a data visualisation specialist working with several NHS Trusts, to help us uncover areas that we could improve on.

– How much money could we save a client if we were able to flexibly schedule non-emergency appointments such that we could provide one interpreter to a client location for the whole day rather than sending three separate linguists?

– When does it make sense for us to offer a client interpreters on a permanent retainer rather than ad-hoc bookings?

– How many fewer appointments would we have to cancel if we could harness the power of census demographic information to stay abreast of shifting patterns of migration around the United Kingdom? How pleased would a client be if we were able to present data showing predicted language requirements and a corresponding list of recruited linguists to fulfil that expected need?

– How many phonecalls does each client make to our staff related to IT support? If we held onsite training on our IT systems, which client(s) should we prioritise? How much time would that save our staff?