More than ever before, Australian businesses are investing in new technologies and harnessing data gathered through the recording of human interactions and machine activity to create new revenues and grow their businesses. In fact, the hype to harness the IoT in our region is so significant that IDC’s Worldwide Internet of Things Market Forecast Update, 2015-2019* revealed that Asia Pacific is maintaining the lion’s share of market spend in The Internet of Things (IoT), representing 38.4 per cent of global spend in 2019.
With the influx of IoT devices, comes massive opportunities for Australian businesses, including SMEs, which are now able to leverage data to improve and personalise the customer experience, as well as automate key processes, both of which ultimately benefit business bottom line.
In the past, the school of thought was that IoT only benefited industry titans like Google, eBay and Amazon, which have been investing in the technology from its very conception.
It’s true, many small businesses still find it challenging to know what to do with the data at their disposal. Being able to understand which data might be relevant, how to extract that data, and how to make a sense of it in the bigger business picture is not without its challenges.
They may also not quite understand what a robust data and analytics strategy is to begin with. Some enterprises will say they have a reporting and analytics strategy, when all they actually have in place is a data visualisation solution like Tableau—or worse, their strategy constitutes some sort of basic spreadsheet reporting.
However, there is a way for SMEs to take full advantage of the data available to them to fuel IoT projects. And no, it doesn’t mean you have to hire costly data scientists or roll out a complex analytics strategy. It’s as simple as automation.
Now, you are probably thinking “isn’t that what the existing tools do already?” To earnestly make advancements in IoT, SMEs need to implement analytics processes capable of self-learning—and not just applying learning to the output of the analytics.
Using meta Learning principles – which is a sub-category of machine learning – can help capture learnings from prior experiments to then be applied for future experiments, and in doing so minimise the costs of running machine-learning experiments. In short, meta-learning can be simply described as skills that improve the ability to learn other skills.
Through meta-learning, businesses will be able to increase the accuracy of their analytic models as time goes on, achieve faster outcomes, and gain better control of their infrastructure.
Democratising analytics and using next generation meta-learning approaches to automate data analysis greatly reduces the need for a high-cost and high-demand resource not available to smaller businesses. In doing so, SMEs will be able to capitalise on the IoT movement at a competitive level to their industry counterparts.