Why Australian companies are still failing miserably at real-time data

Australian executives are quickly realising the opportunities that AI has to offer in terms of improved efficiency, operations, visibility and processes. Investment has followed, with businesses in the ANZ region planning to invest 15% more in generative AI than the global average. 

Most business leaders are also looking to AI tools to take over certain functions or capabilities within the business, with recent research saying 98% of C-suite executives would be confident to let AI tackle operations without human supervision or oversight within the next three years. 

But these observations are all future state. They reflect on the potential of the tools and the future ambitions of business leaders. They do not, unfortunately, reflect what businesses are experiencing today, nor whether the way businesses are operating today is setting them up for future success with AI. 

It's time to get real with real-time data

The difference between real-time data and AI is that one's real, and the other is still a concept for most businesses. The two terms are often used interchangeably, which is incorrect. Unlike AI, generating benefits from the use of real-time data is very achievable today and is, in fact, a necessary precursor to any successful AI implementation.

Despite this, it is still difficult, and most businesses struggle with how to implement or use real-time data effectively. If businesses can get good, reliable, free-flowing, real-time data, they can do much more than just improve their operations. They can transform the way they work, change processes to be significantly more effective, and drive efficiencies in every corner of the organisation. 

The promise and slow adoption of IoT

Not long ago, IoT was considered the "next big thing", much like AI is being discussed today. The hype around IoT was boundless and there was huge promise, but in many instances, it failed to deliver on that promise. This failure was not because IoT was conceptually unsound but because getting good, reliable, free-flowing, real-time data from existing legacy assets was hard. 

While this is still a challenge for most businesses today, it also presents the biggest opportunities to achieve the full range of capabilities of AI. Businesses that can establish interconnectivity by enabling data sources to talk to one another with data flows between them, will outpace businesses settling for simply connecting different systems in the race to AI nirvana. It's the former that will enable businesses to act on what's being seen in the field second-by-second, but this won't be possible until businesses can get real-time visibility and accurate metrics delivered where they need to be. 

Why real-time data should be influencing operations

Real-time data is presently not being used to influence and optimise the operational side of businesses enough. While some industries like cyber and ecommerce are making great progress with real-time data - utilising it to mitigate security threats before they arise and increase profits - many industries are lagging significantly behind, including mining, manufacturing, infrastructure management, construction, agriculture, and facility management.

Providing employees (and interconnected systems and technologies) in the field with up-to-date, accurate insights on what's happening enables them to make better decisions in the moment and make the necessary adjustments before the opportunity to do so is lost. In manufacturing, this could be identifying problems on a production line that would lead to production targets being missed; in farming, it could be used to spot a water leak; in mining, it could be to ensure optimal loading of trucks to prevent haulage inefficiencies and additional costs; or in any number of sectors to identify assets and machinery that's failing that could lead to operational shutdown or serious danger to personnel.

For those business leaders who are feeling like getting the right real-time data is a steep hill to climb, it is important to understand what technologies and tools are available to support organisations through these processes. It is not necessary to change or rebuild systems to make interconnectivity work. The solution also doesn't have to involve a complete replacement of legacy systems, the addition of a network of IoT devices, or significant disruption to how the business is operating. There are platforms that make real-time data achievable by using no- and low-code platforms with toolkits and pre-built schemas that enable businesses to master real-time interconnectivity, ETL, and data analysis. 

Taking a step back and looking at the vast array of disparate data across the business can be overwhelming, but this is exactly why prioritising real-time data is important. The longer these datasets are left disconnected, the larger, more complex, and less usable they are likely to become. Get ahead in the race to AI by making real-time data a core pillar of the business strategy.