In the past few years, the New York City Fire Department (NYFD) turned to big data analytics to see if it could help to prevent fires, save lives and reduce insurance liabilities. In a city where more than 300,000 fires occur a year, the NYFD made a decision to go beyond putting out fires to instead focus time and money on prevention methods – reframing the battleground on city fire safety.
They analysed more than 60 huge data sets, including historical archives, building information and maintenance records and more, to develop a risk profile for every single building in the city. The outcome was the successful reprioritisation of the 50,000 inspections carried out annually and the systematic reduction of serious fire outbreaks and a cut of two-thirds in response times.
Now consider that level of precision applied to the decision-making process at your business.
It’s true that managing and extinguishing fires is part of the job for a chief executive or managing director and, data or no data, there is no way to avoid every challenging situation. But there is now a way to see what was once “unforeseen” and to prevent what you thought unavoidable. In business terms, it’s possible to tell the future with greater clarity than ever before. And this level of next-generation business intelligence used alongside – not instead of – human intellect and intuition, is driving the next frontier of global business.
If you’re still not convinced then consider this: for decades, scientists and academics said earthquake prediction was inherently impossible, because incidents were often triggered by smaller, tiny shifts in plates that resulted in massive quakes. Yet today, big data is saving lives in earthquake-prone countries by predicting where and when a quake is most likely to strike. More than 13,000 people die every year as a consequence of violent earthquakes and nearly 5 million have their lives affected. For them, big data is making a big difference.
In Pakistan, authorities are tackling dengue fever’s potential migration path and outbreak probability using the phone data gathered from 40 million citizens. And in Australia, the emergency response unit on the Gold Coast has reduced bed block, shortened waiting times and lessened its budget burden by A$23 million (Dh60.9m) by crunching five years’ worth of emergency unit records to allow them to predict emergency hotspots and make smarter decisions on how to prepare and deal with them.
Even in business – where it is less about saving lives and more about predicting machine failure, or assessing vulnerabilities, lost efficiencies or poor performance – big data has long since changed the decision-making process for leaders. But it took a long time to get there. Many questions were asked of the limited and narrow data that scientists have taken years and millions of dollars to answer. The opportunity now is for this region to leapfrog that process and integrate big data into corporate decision-making with confidence, and without the time or budget burden associated with big data’s beta technology limitations a decade ago.
To date however, the Middle East is a relatively slow adopter of big data. This region’s economic influence is changing global energy systems, reinventing aviation, underpinning the future of the maritime business and setting new standards in civil engineering. Yet from regional oil and gas to financial services – the role that big data plays in safety, efficiency and competitive decision-making is negligible, exposing our primary industries to the potential of being out-innovated by global counterparts more open to new methods.
The issues are twofold.
The first issue is a chronic lack of trust. Inviting a data scientist into the boardroom risks being met with disapproval from some quarters, and any subsequent attempts to fold data analytics into the decision-making process could be equally dismissed unless such information can be “proven” to be accurate or that it has a track record of informing correct decisions.
The second issue is a natural risk aversion in the region. Modifying the information you use to make big business decisions requires a leap of faith – or a propensity for risk-taking. For data scientists and their insights to be taken seriously, behavioural change at the board level must precede it.
Another catalyst for a positive shift on data would be the cultivation of a national crop of data scientists, all conditioned to focus on outcomes – not just inputs (numbers) – and to better understand real organisational challenges. Organisations must also increase the visibility of captured data, allowing for a clear understanding of the data available to them at any one time, while at the same time balancing this ownership with the democratisation of data – preventing it from falling into silos.
Regional businesses need to do more to empower data practitioners, to believe in their findings and believe in the value of the contribution these complex and revealing insights make to good decisions.
Traditionally, the Middle East has been a region anchored by a rich culture, built on the strength of relationships, a respect for the older and wiser and on the critical importance of human interaction. That’s what makes trusting information processed, analysed and enriched by machines counter-intuitive and something of a cultural departure from how big decisions have been made at big firms for years.
That said, the global competitive landscape is changing. Regional businesses need to do more to empower data practitioners, to believe in their findings and believe in the value of the contribution these complex and revealing insights make to good decisions.
In this new reality, the role big data analytics must play in protecting our primary industries – such as energy, tourism and financial services – to serve national interests and long-term prosperity in the face of global competition, and falling profitability is, well – immeasurable.
Atif Kureishy is a principal for the Middle East and North Africa at Booz Allen Hamilton.
business@thenational.ae
Follow The National's Business section on Twitter
