This one tool will drive the next era in business

Moving to the algorithm

Advanced maths, algorithms and sophisticated modelling are the new tools for managers, directors and finance professionals. Now, business needs people who can use the tools.

Algorithm [‘alguhridhuhm] noun: A formula for automatically solving a problem in a finite number of steps. ORIGIN: Derived from the name of the ninth-century Persian mathematician and scientist al-Khwarizmi.

“There is a revolution going on in business,” says Ram Charan, former Harvard professor and author of seminal business books such as The Attacker’s Advantage, Execution and Boards That Deliver.

“Never before has so much mental power been computerised and made available so broadly. Algorithms and their related sophisticated software, coupled with new tools capable of collecting and storing huge amounts of raw data, can predict patterns and changes in everything from consumer behaviour to the maintenance requirements of machinery.”

Algorithms have long performed business tasks: banks, for instance, use the Luhn algorithm to help protect against errors when you provide your credit card number. But in the past two decades, their business role has exploded. As machine learning expert Pedro Domingos notes, our information is served up through Google’s PageRank algorithm.

Other algorithms are at play in such wide-ranging areas as Facebook posts, book and video recommendations, retail store layouts, online dating partners and deal risk in corporate takeovers. 

Algorithms can now address far more complex phenomena than ever before. Biologists are using machine learning to build models of the cell that are based on data from DNA sequencers, and neuroscientists are using it to build detailed maps of the brain, literally neuron by neuron. 

Social scientists are using it to study large social networks, with millions or billions of people. “[The computer program] Siri uses learning algorithms to understand what you say and predict what you want to do,” notes Domingos. “Machine learning is involved in pretty much everything we do these days.”

Charan believes that the use of algorithms and maths has become a critical source of competitive advantage. “Google, Facebook and Amazon were created as mathematical corporations, communicating with their customers through analytical systems,” he says.

“The advantage for such businesses is that they can deal directly with buyers, without intermediaries, and can personalise the customer’s experience.”

Jodie Sangster

For those organisations not born as mathematical creatures – that is, for most organisations – this algorithmic revolution means sweeping change. It means much flatter structures, with tiers of middle-management jobs simply disappearing.

It also requires a rethinking of what performance metrics are appropriate, what qualifications are desirable for recruits and how expertise can be accessed. For senior managers and directors, it also raises this tough question: How can they make decisions based on advice they might not fully understand?

Bottom line impact The algorithmic revolution began in Silicon Valley in the US and has taken a while to spread elsewhere. Jodie Sangster, CEO of the Association for Data-Driven Marketing & Advertising and the Institute of Analytics Professionals of Australia (IAPA), says Australian usage finally started to rise when the term “big data” began to appear about three years ago. 

“When the term 'big data' began to appear about three years ago, that put a useful tag on the concept.” Jodie Sangster, Association For Data-Driven Marketing And Advertising

“That put a useful tag on the concept,” says angster. “There was a realisation that there was a huge amount of data on consumer behaviour that could be collected, analysed
and utilised.” 

Rachel Edwardes, head of marketing at business analytics consultancy Forethought, also acknowledges that Australian business is, in general, a few years behind US competitors. Nevertheless, she argues that Australia is leading the world in some niches and that Australian companies can move faster than US counterparts to implement strategies once they have solid data.

One of Forethought’s projects involved discount chain Kmart, which not too long ago seemed to be locked into a pattern of decline. Forethought used detailed survey data to model the drivers of acquisition and retention by customers, with the information then being used to design a marketing campaign.

“Using analytics to model consumer behaviour for marketing is the difference between shooting at a target and shooting in the dark,” says Edwardes. 

“Kmart saw clear benefits within six months, and the upward trajectory has continued. We have continued to work with them to model progress and changes.”

Marketing might be the most obvious way for companies to use sophisticated data analysis but there are plenty of other avenues emerging. Consulting firms specialising in maths have begun to appear, offering solutions across a range of activities. 

Ram Charan

The key is to link the expertise of maths experts with real-world business requirements. One organisation to do this is the Centre for Industrial and Applied Mathematics, part of
the University of South Australia.

cMathematical models can show how changes to one variable affect the whole,” says Professor John Boland, director of the centre. “We have advised electricity generators, for example, on the impact of a dropout, how to integrate non-traditional energy sources such as solar and how to allocate load according to changes in consumer demands.”

The centre has also helped the mining sector by showing how to optimise truck traffic in and out of mine sites. It has advised the transport industry on reducing energy costs by adjusting the speed of long-haul freight trains.

“Making these improvements can generate savings of millions, even hundreds of millions,” says Boland.

“A problem with high-level maths is that it can be difficult to explain to people without a maths background – clear cost savings make the senior managers of a company, or the board, take notice.”

“Google, Facebook and Amazon were created as mathematical corporations.” Ram Charan

Slow on the uptake

Many industries are yet to fully grasp the potential of the algorithmic revolution. “There is still a very long way to go,” says Sangster, “and a lot of companies – and C-level people
as well – haven’t even begun to invest in it.”

Sangster nominates the banking and finance sector as the most prominent user of analytics for marketing, followed by the travel industry. The retail sector and the consumer goods sector are lagging, she says, and franchise food chains have also been slow to enter the field.

The construction industry has begun to use maths in a limited way, such as environmental add-ons to architectural designs, but not for fundamental issues such as the best way to
site a building in order to minimise energy use and to maximise solar gain for electricity production. Boland works on this type of problem with practitioners from several disciplines at the Sydney-based Cooperative Research Centre for Low Carbon Living. 

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He points to other areas that could benefit from greater maths input. “Hospitals, for instance, are still doing staff rostering old school, in terms of shift allocations and customer demand, even though there are gains to be made through optimisation,” he says. “The great advantage of modelling is that you can try out a wide range of options and mixes to see what works and what doesn’t.

But some organisations either don’t understand what can be done or are too invested in their existing arrangements. In those cases, the maths industry should do more to communicate the exciting opportunities it can offer.”

Worth their weight in gold

People with expertise in maths and business are already in great demand, with salaries around the A$200,000 mark being offered, according to a 2015 survey by the IAPA. In many cases, companies will provide additional training in analytics to employees who show an aptitude for the field.

“It’s about finding that space between the theory of maths and the practicality of marketing,” says Sangster. “Someone who understands both is worth their weight in gold.”

She is pleased to see the development of specialist master’s degrees in fields such as business analytics and data science – but notes that as they are very new, it is too early to see how they will function in the marketplace.

Pedro Domingos

These degrees, even though expensive, are usually over-subscribed. The next challenge is to expand training for C-suite personnel. “Greater awareness of the value of maths by senior people could really take things to the next level,” says Sangster.

Professor Ujwal Kayande, director of the Centre for Business Analytics and the Master of Business Analytics degree at the Melbourne Business School (MBS), notes that communication is a key element of the course, with one day of each week being devoted to effectiveness development.

“It’s particularly important when you realise that some maths-based conclusions are likely to go against the gut instinct of senior managers,” he says.

“We have tried to teach the students how to conduct difficult conversations, to build a common language. That is something that came out of our early discussions with business leaders.”

“Machine learning involved in pretty much everything we do these days.” Pedro Domingos, University of Washington

On the other side of the equation, Kayande explains that the MBS has already conducted executive education programs customised to particular organisations to show senior managers the potential of business analytics. Those short courses have been very well accepted, he says. One constraint for future courses is simply a lack of people to teach them.

Seizing the opportunity

Like most experts, Kayande sees a field with huge potential. “Even in specialised fields like HR there are gains to be had from maths input,” he says. “The business community has only just begun to understand the benefits that advanced maths can provide.”

Ram Charan puts it with more urgency. “Regardless of how young or old your company is, you must make the use of algorithms part of your vocabulary, as much as profit margins and the supply chain are today,” he says. “A company that is not already doing it, or is unable to tap into it, is already a legacy company.” 

Machine learning is involved in pretty much everything we do these days.” Pedro Domingos, University of Washington

Predicting the vote

Barack Obama is a politician who has grasped the value of advanced maths as a campaigning tool. In the 2012 US presidential election, his chief scientist Rayid Ghani used data and machine learning to predict the answers to four questions for each swing voter. They asked how likely each voter was to:
  • support Obama
  • show up at the polls
  • change their mind about the election based on a conversation about a specific issue
  • respond to reminders

Based on the results of the modelling, they then ran a program called “the Optimizer” to choose which voters to target. In contrast, rival Mitt Romney’s campaign used standard polling and targeted broad demographic categories, such as “suburban middle-aged women”. On election day, Obama carried all but one of the swing states to win the race for the White House.

Opening the black box

Managers need not understand complex mathematics or machine learning, says Pedro Domingos, but they do need to understand what these things can do and how they can be used.

“If algorithms are a black box to you, you have no control over where they will take you,” he says. “Think of a car as an analogy: only engineers and mechanics need to understand exactly how the engine works, but you need to know how to use the steering wheel and pedals.” 

Domingos is a professor of computer science at the University of Washington and one of the key figures at the intersection of maths research and the business world. His most recent contribution to building bridges between the two is the book The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World (Allen Lane, A$50). 

The book is designed as a primer on machine learning – that is, computers learning by themselves by generalising from data instead of having to be programmed by people. The machine learning field encompasses various schools of thought. At the apex lies the concept of the master algorithm, which would in theory combine the strengths of the different approaches, perhaps providing the capability of discovering all knowledge from data. 

“This is an incredibly powerful idea,” says Domingos.

“When will we find it? It’s hard to predict, because scientific progress is not linear. It could happen tomorrow or it could take many decades.”

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