- Data will help decision making but the key to success lies in collaboration
- The internet of things has vast implications for how we will manage our resources and our economy
- Rapid advances in data analytics will change the paradigm in academia, science and business
In our data-hungry world, the volume, variety, frequency and veracity of data is exploding off the chart. The numbers are dizzying; the possibilities unknown; the potential enormous.
As the “oil” of the future, it is data that will help people make better decisions, faster. But the key to success lies in collaboration.
“That is how businesses can unlock the value of their collective data.”
This brave new world is being enabled through the convergence of multiple technologies, including high-speed broadband, wireless communication, embedded systems in computers and wireless sensor networks — known in short as the Internet of Things (IoT). Or as some analysts call it, the Fourth Industrial Revolution.
According to a recent Gartner forecast, 4.9 billion connected things will be in use in 2015. This is up 30 per cent from 2014 and will reach 25 billion by 2020, creating US$263b in service spending.
We meet in the SIRCA office with a rooftop terrace where the jeans-and-sneakers staff gather for barbeques overlooking the Sydney Opera House. Matching its free-range startup vibe, SIRCA has a child innovation company, RoZetta, whose pioneering technology platform does the “heavy-lifting” and makes company data available for analytics.
Briers picks up his mobile smart phone.
“Alongside the advent of the so-called IoT is the rise of the smart device. Already you’ve got all sorts of sensing devices packed into your phone — vibration and accelerometers. There are sensors now on the Harbour Bridge for predictive maintenance. One of our partners, Bosch, now has a strategy that every single device they make is connected to the internet. This means one piece of a car talking to another. Your fridge talking to your oven. Your Fitbit talking to something else.”
As technology speeds up, this vast network of connected “things”, each with their own unique identifiers, will be able to transfer data from thing-thing (without human intervention) or people-thing or people-people.
As Briers and his team know, big data on its own isn’t useful — it’s just big. But when information is shared with the right partners, unanticipated results follow.
“We are sharing data between an oyster farmer and a regulator as part of our work with agribusiness,” he explains.
Their objective: to grow the perfect oyster.
Remote sensors measure the oyster’s heart rate, the health of the water and weather conditions.
“This gives us a much more accurate picture that can be relied upon by multiple stakeholders including farmers, regulators and scientists.”
The Internet of Things (even of oysters) has wide-ranging implications for how we will manage our resources and our economy.
Too often businesses and commercial players lock down data for narrow commercial interests. SIRCA and RoZetta work from the premise that the value of data increases with circulation.
“The term we use is sharing data with trust,” says Briers.
So what does this mean for businesses? According to a 2014 McKinsey report, Sydney has the largest community of data startups outside of the Silicon Valley. Australia is also an early technology adopter, and, according to CISCO, last year was the world’s biggest consumer of cloud services.
“We tend to have a crack in Australia,” Briers chuckles. “Maybe this is the larrikin idea.”
Australia is also in the top three in the OECD for research output.
“That means we’ve got a lot of very clever people in this country for research but we are in the bottom of that table in terms of taking that research output to market and commercialising it.
“We get big ticks on the smarts but we get a D or a failure at actually converting these smarts into something,” he continues.
“We see there is a disconnect between acceptance and collaboration between the university and the business sector, but also within the business sector itself.”
And what does it mean for financial services?
“In the accounting and professional services industry, companies either need to start retraining or they should be hiring people with analytical skills — the data scientists.
“In the past we’ve hired a statistician and they’ve tried to find a solution to a problem. The next generation will be about ‘free form’ data mining. It’s about taking a bunch of data that is analytics ready and looking for insights.”
This innovative approach — based around the idea of failing fast and failing cheaply — is reflected in the trend of big companies collaborating with nimble startups. In 2013 Telstra launched the Muru Digital accelerator program offering technology entrepreneurs A$40,000 each and six months of free office space.
In the accounting and professional services industry, companies either need to start retraining or they should be hiring people with analytical skills — the data scientists.
Collaboration: the way forward
It is on the principle of collaboration that Briers founded the KEi in 2015 in partnership with Bosch and Cisco, RoZetta and Curtin University and the University of Tasmania.
“KEi is offering collaboration as a service. If you are going to build a valuable service for a citizen, a customer and a business, the technology needs to sit in the background but remain a key enabler. It’s the technology that generates the data.”
Bringing different industries together is a hard-earned skill of Briers.
He founded SIRCA in 1997 as a not-for-profit company to provide an intermediary between financial academic researchers wrangling over high volumes of data and the ASX.
From working with a couple of universities, SIRCA now services 40 ANZ universities and has successfully commercialised that service with Thomson Reuters.
Initially it received the financial data, from the New York or the London Stock Exchange, on tapes. It is now delivered through three different pipes, from 2500 sources and they collect two million records per second from every single market in the world.
“With that data we established the world’s first historic financial database which is now the largest database of its type in the world.”
Just as a PhD student would spend 80-90 per cent of their degree unpacking data to get it into a form they could analyse, companies today are facing the same mammoth task.
Banks have legacy data systems that do not “talk” to each other; commercial businesses have consumer data that is not structured. In part this is due to obsolete systems; in part the speed of change.
Then there is the type of data itself. This ranges from structured (consumer data or financial market data) to unstructured (social media, text, video and audio).
On the horizon looms a tsunami of machine-generated data, which Briers believes will dwarf all the rest.
SIRCA’s fortunes changed in the early 2000s with the rise of high-frequency trading and the commercial market’s “growing appetite for time-series stitched-together data”.
This coincided with banks hiring physicists from academia, “putting them in a vault somewhere and feeding them all this data so they could test algorithms and build computer-generated trading models”.
The KEi aims to combine its experience servicing financial market regulators with its expertise working with agriculture, the IoT and sensors. Just as they can help an oyster farmer, they can help other farmers increase the yield of their crop through high-density and hyper-localised sensing.
“The vision is to be able to share the data [collected on the farm] with scientists so they will be able to build a better disease prediction model. We can then translate the data via an app into the hands of the farmer so they can plan for events like frost.”
These rapid advances will change the paradigm in academia, science and business, believes Briers.
Rather than the old-fashioned statistical model, which starts with a hypothesis, the combination of high frequency data in real time and predictive analytics, is creating artificial intelligence.
“If you have enough data you can run it through algorithms, and it actually discovers patterns in the data that conventional methods do not reveal and in many cases improves predictive ability. This is machine-generated insight.”
What about the security issue? A 2014 Forbes article describes how Internet-connected appliances can already “spy on people in their own homes”. Then there is the fear that if someone could hack into your toaster, they can access your bank account.
While Briers acknowledges there are significant technical security challenges ahead, and that privacy laws are not keeping up with the speed of change, he remains an optimist — and a realist.
“If a farmer knows he can get real value from sharing this data, he has less concerns about privacy.
“We cannot predict how data will be used. We hope that it will be used in a way that is positive for society without breaking the planet.
“We hope that society will force business and governments to ensure that we don’t end up with a scenario like in 2001: A Space Odyssey.”
He shakes his head.
“But one thing is clear. In a matter of a few years, the world will look very different.”
This article was first published in the November 2015 issue of Acuity magazine.