Analytics and the real world: Making the connection, Part 1

  • To get more students interested in STEM, it has to be made relevant to them
  • Why the fact a ‘smart cow’ generates 200MB data a year is important
Analytics and the real world: Making the connection, Part 1

 
ANALYTICS seemingly exists in a realm of quasi-mystery: Business leaders want it for the supposed benefits it can bring, while not fully grasping what it can do. Making it relevant and applicable to the real world is sometimes a challenge.
 
But that relevance is what drew Evan Stubbs (pic above), chief analytics officer at SAS Institute in Australia, to analytics when he was a student.
 
“I was never really good at math in high school; I hated it,” he told Digital News Asia (DNA) when in Singapore recently.
 
“It was only when I did econometrics in university … that was a lightbulb moment, being able to predict the future from raw data,” he said.
 
Econometrics uses statistical and mathematical theories to test economic hypotheses and to forecast trends.
 
Not all students get such an epiphany, which is why many countries and companies are complaining that there is a shortage of science, technology, engineering and math (STEM) students.
 
Stubbs believes the key to attracting students to STEM fields lies in making it relevant.
 
“The problem is relevance. Math wasn’t made relevant to me in high school, nor even through my early years of university,” he said.
 
It was only when Stubbs saw how math could be applied to change lives or run the economy more efficiently did it click for him.
 
“Until I actually saw how it could be applied to help a business run more efficiently … [that was when] I could understand why I would want to do this,” he Stubbs.
 
“The math itself is fascinating, but what you can do with it is what makes it very powerful,” he added.
 
Analytics and the board
 

Analytics and the real world: Making the connection, Part 1

Buzzwords like big data and analytics have been bandied about by analysts, consultants and vendors for some time now, but recently there has been a shift of such conversations to the boardroom.
 
“There has been this big shift in the last three to five years at the board level,” Stubbs said.
 
“Now there is a broad understanding that if you can’t compete in analytics in some way, your long-term sustainability is at risk,” he added.
 
While the realisation that analytics is important is one key battle won, there are two challenges that all companies face, according to Stubbs.
 
“People talk about how 80% of the world’s data has been generated in the last three years – the reality is that this has been happening since the 1960s,” he said.
 
“The Internet of Things (IoT) is driving this massive wave of data and the problem organisations are facing is how do you handle, analyse and do something with that information in real time?” he added.
 
People now expect things to happen in real time regardless of industry or sector.
 
“It doesn’t matter if you’re dealing with the government, where someone is trying to work out his taxes; or the private sector, where the customer is asking why there is a need to wait a month to see insights from data instead of having it right now,” Stubbs said.
 
The second challenge is related to the skills market.
 
“There is a global shortage of people who have the ability to analyse the information and do something meaningful with it,” Stubbs said.
 
“Part of that are technical skills, part of that is leadership and general numerical literacy,” he added.
 
Organisations are struggling to find such people, and just as importantly, figuring out how to keep them and develop their skills when they do find them.
 
“If you go to San Francisco today, it is impossible to find a data scientist – they are all fully employed,” Stubbs said.
 
“We [SAS Institute] have been working with universities such as the Melbourne Business School at the University of South Australia to build higher education programmes to tackle this issue,” he added.
 
Big data + IoT = Smart cow
 

Analytics and the real world: Making the connection, Part 1

 
The IoT, when coupled with big data, has made the challenge keener.
 
“With the amount of data generated, you can’t analyse things after the fact,” Stubbs said. “You can’t wait a day or two to figure out what is going on.”
 
The sheer volume of data requires immediate action in real time, and decisions have to be made when incidents happen and not well after, he argued.
 
“Banks have been welcoming analytics in identifying fraudulent transactions in real time,” Stubbs said.
 
“If you put in place the appropriate real-time processes, you can effectively stop the fraud before it is committed,” he added.
 
The convergence of the IoT and big data is also creating new and interesting sources of data that people won’t expect, according to Stubbs.
 
He cited Australia’s Commonwealth Scientific and Industrial Research Organisation (CSIRO) as an example, saying that it started using sensor technology in agriculture.
 
CSIRO has strapped Fitbit health tracking devices to cows, which allows it to work out when a cow will give birth. It has “allowed farmers to have more calves delivered successfully, and happier cows,” said Stubbs.
 
“Random fact: An average ‘smart cow’ generates 200MB of data a year,” he added.
 
The IoT will continue to create more data streams, which will lead to more opportunities to create meaningful outcomes, Stubbs argued.
 
“It creates all these new opportunities and new ways to look at the world, and really meaningful outcomes,” he added.
 
Up Next: Pitfalls and the future of analytics

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A life in data, a handbook for data scientists
 
Addressing the data scientist glut
 
Big data analytics key to addressing fraud at the root
 

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