BI for the DI?

(Business Intelligence for the Detective Inspector?)

Sean Farrington, UK MD & RVP Northern Europe at QlikTech argues that TV detectives are not too different from business intelligence users after all…
Business Intelligence for the Detective Inspector

In recent weeks, the general public has been captivated by the latest series of the BBC’s Luther, a psychological thriller starring Idris Elba as the title character, featuring no shortage of action and complex characters. Add to this the nation’s recent obsession with shows such as Broadchurch and Sherlock – clearly we as a general public are riveted by these crime-solving figureheads.

We’re enticed by the world of these detectives who inevitably catch the bad guy through a series of calculations (with the help of a loyal sidekick). But are they really that different from the rest of us? I’d like to argue that in this new world of data and business intelligence, everyone from across the business needs to be a bit of a detective. While we’re not hunting out criminals, we need to always be thinking outside of the box and looking for insights and evidence for how we can improve our business.

A detective’s unenviable job brings with it a huge number of obstacles. In their attempts to track down the culprit, detectives need to manage information from any number of sources, reacting quickly and efficiently. To come to the right conclusions, they must consider all of the information available to them, along with the reliability of the sources and any hidden motives.

In the unpredictable world of dealing with criminal activity, multiple sources of information require detectives to be able to quickly acclimatise to new challenges – they must be able to adapt to any new developments brought forward at a moment’s notice, especially since there’s generally pressure to get an answer under a strict time-frame.

Importantly, the most successful detectives are those who can adapt to the modern world. As the methods of their culprits become more advanced, so too must our detectives.

In fact, we’re actually seeing a number of real life police forces moving forward with data analysis, and embracing technology to help them do their jobs. One example is Gwent Police, who are also tasked with finding the bad guys (although perhaps not as dramatically as our TV heroes). The force faces exactly the same challenges that Luther does in terms of managing multiple sources of data, adapting to rapidly changing requirements, and ultimately needing to monitor and prevent crime.

Gwent Police rolled out a business intelligence platform to meet these needs and has, in turn, analysed data to gain a better insight of criminal activity in the area. Having previously relied on Excel spread sheets to organise all data, it has improved its efficiency in day-to-day operations. Gwent Police are able to quickly identify crime hotspots, reallocate staff, and effectively manage resources. Clearly, in both real life and in TV detective world, this is the key to getting the investigation done.

I’d argue that data analysts at any other organisation, although they may not see themselves as being detectives, are in fact similar. On a basic level, their respective job descriptions are essentially the same. Where Luther needs to effectively manage the data available to him to pursue London’s lowlifes, data analysts in large corporations need to use their data to streamline the operations of their business, and drive efficiencies across the board.

Effective management and organisation of data is important to any business, and so data analysts must be able to manage and interpret data from multiple sources, while keeping the broader aims of the business in mind. Data analysts must also be prepared for any client’s demand, such as moving deadlines and changing requirements. Crucially, the business needs to be in-step with modern technology and social media, for both the sake of the company’s reputation, and its ability to conduct its day-to-day business.

If asked about the similarities between their roles and those of a data analyst, Luther and Sherlock would both likely scoff. However, it is clear that given the similarities between the two different jobs, neither would have much trouble acclimatising to the issues facing a data analyst. Indeed, were it not for the mingling with criminals, a data analyst would likely fit seamlessly into the worlds of Luther or Sherlock. On the other hand, given how stressed our detectives constantly seem to be, one might argue that they would benefit from introducing business intelligence platforms – although it might not get us all to tune in every week.

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