Disruption imminent: Making the connection, Part 2

  • Companies can no longer go on without looking at data
  • AI, robotics and skills gap to dominate the future of analytics
Disruption imminent: Making the connection, Part 2

 
ANALYTICS and the Internet of Things (IoT) have been delivering massive streams of data, and insights to go along, such that business leaders have been warming up to the idea.
 
However, there are pitfalls that companies need to avoid when starting on their analytics journey, according to Evan Stubbs, chief analytics officer of SAS Institute in Australia.
 
“It is twofold: One is getting the right people, which is why it is so critical for governments to work out not just how to fill the current skills gap, but also how to create an ‘intergenerational’ capability,” he told Digital News Asia (DNA) on a recent visit to Singapore.
 
He predicts this skills gap issue would run on a 10-year cycle.
 
“The second challenge is how existing companies can retrofit and modernise their analytics capabilities to take advantage of real-time insights,” Stubbs said.
 
“Organisations can’t afford to have their architecture and technology process information after the fact – they have to look at how to modernise their analytical capabilities,” he added.
 
Insights needed
 
Disruption imminent: Making the connection, Part 2Translating data into insights is more than just recognising patterns within a data set, and this comes with thinking about a customer first, according to Stubbs (pic).
 
“Regardless of where you are, you have to think about what the information means to the customer,” he said.
 
Citing the example of how New Zealand’s Ministry of Social Development is using data, he argued that how you look at information is key to making it meaningful.
 
“One way to look at the information is from a financial perspective – the amount of social benefits being paid out to the number of people receiving it, that’s one way of viewing the world,” Stubbs said.
 
“What information and analytics means to a person is that, within those numbers, there is ‘Sue’, a single mother recently unemployed and looking to get back into the workforce.
 
“If you understand where those people and ‘Sue” come from, you’ll understand that if they remain unemployed for three to four years, they will never enter the workforce again,” he added.
 
That carries a social cost for ‘Sue’ and a national cost for the country. Inside that information is what organisations need to know about ‘Sue’ and people like her to help them, Stubbs argued.
 
“That’s really what analytics means in terms of ‘business outcomes’,” he said.
 
“It’s not about the numbers or analysing them, it’s about using those number to help serve your customers better or do something meaningful with them,” he added.
 
Disruption time
 
There are two types of organisations, according to Stubbs: The ones that think they are safe and comfortable where they are, and the ones which will be around in the next 10 years.
 
“Every industry around the world is currently being disrupted because of technological innovation and data-driven disruption,” he said.
 
“Supermarkets are getting banking licences, and companies like Uber are exposing more information than taxi companies are able to, to better serve customers and disrupt incumbents,” he added.
 
Companies which are comfortable with the same approach and markets they have been in for the last 20 years will probably not be around in 10-15 years, according to Stubbs.
 
“The disruption that Kodak went through led to Canon and Nikon taking over the photography industry, to now Canon and Nikon being tiny in comparison with Apple and Samsung,” he cited as examples.
 
“Few organisations now believe that they cannot invest in information and data-driven innovation,” he added.
 
This is one of the reasons why organisations are putting in place chief data officers, chief data scientists, or chief analytics officers, Stubbs argued.
 
“Organisations are recognising this isn’t something that happens in the back-office.
 
“This is something that needs to have leadership representation; it is about how they would run their business more efficiently at an enterprise level,” he added.
 
Humans not needed
 

Disruption imminent: Making the connection, Part 2

 
While organisations continue to grapple with analytics and what it means to them, Stubbs predicts that labour and automation will dominate the field in the future.
 
“One of the trends is the labour market pressure for people with STEM (science, technology, engineering and math) skills,” Stubbs said.
 
“Salaries will keep going up, but organisations need to find ways of attracting talent that are not [based on financial incentives alone],” he said.
 
To attract the best people, organisations need to create an environment that provides intangible motivation as well, ranging from corporate social responsibility initiatives to charity work, he argued.
 
“This trend is only going to keep growing and organisations need to work out how to handle that,” he added.
 
The second trend largely revolves around automation and the ‘robotisation’ of the workforce, according to Stubbs.
 
“Big data, coupled with machine learning and the need to make decisions in real time, means that a lot of the decisions in the day-to-day operations don’t have to be made by humans anymore,” he said.
 
“Application of weak Artificial Intelligence represents a tremendous opportunity for organisations,” he added.
 
A lot of organisations will be automating their decision-making, which has interesting economic implications for the future, according to Stubbs.
 
“To give an idea of the scale in the next 15 years, if you look at US Department of Labour statistics, the roles of somewhere around 40% of the people who are currently employed in the United States will theoretically be automated through the use of big data and machine learning,” he said.
 
“That creates an interesting question that countries around the world are thinking about: What do we do economically if we have large parts of society who are unemployed,” he added.
 
Previous Instalment: Analytics and the real world
 

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