As we continue down the road of digital transformation, we’re seeing the lines between the physical and digital worlds become increasingly blurred. IIoT — or the invisible and ambient computing environment comprised of a network of smart sensors, cameras, software and massive data centers — has become the new framework for industrial operations.
And IIoT has already proved to be a disruptive force, shaping product development and paving the way to the next step in digital transformation. In fact, research shows that by 2020 the number of connected industrial devices will grow 285% from 2015. A given company can use dozens of disparate data sources with varying level of details that deliver information related to parts, operations, manufacturing process and parts cost.
The goal of these systems must be to transcend the traditionally siloed ecosystems that have been in place for decades. Doing so will improve efficiency and reduce cost in these industries. We’ve estimated that just single percentage gain in efficiency through the deployment of IIoT could yield $90 billion savings in the oil and gas sector, $66 billion in the power sector and $30 billion in aviation.
As industry moves forward, it’s important for industrial leaders to recognize that it’s not if you need to digitally transform your business, it’s when. And even more fundamentally, how. So what is the best approach? Below are a few to consider.
The quest to be data driven
We’re constantly being told to be “data driven” and to consider a “data-first approach” when it comes to solving problems. By no means is the manufacturing industry an exception. Integrating digital analytics with industrial machinery provides enormous opportunities to collect massive amounts of information in order to generate valuable insights. Taking the data-first approach can allow a company to connect its disparate data sources, stitch the data together and find a way to use all the insights to develop analytics that will extract value.
While data can be valuable as a future hedge, a data-first approach is not always the right approach. Data in and of itself does not impact an outcome on its own, and at times teams can get caught up in the deluge of numbers. Depending on the specific outcome you’re looking to achieve, certain data may be more useful than others.
Driven by the outcome
There’s an old Lewis Carroll quote, “If you don’t know where you’re going, any road can get you there.” Surely Carroll didn’t pen this with an industrial mindset, but it is indicative of a more solid approach that could bring clarity to a company’s transformation goals. Organizations should consider what outcomes they’re looking to achieve, and then map their journey from there so that they can more accurately measure return on investment.
But even with a specific problem identified, a company may not know what type of data or analytics solutions it may need. One solution could be to tackle this through what’s called proof of value, which allows an organization to focus on finding the value within the data and the analytics solutions that leverage the data to drive desired outcomes.
The most effective type of outcome is well defined and can show that an impact can be made and, most importantly, measured. For example, defining an outcome such as “increasing capacity of a manufacturing plant by X percent” is too high level since there are many factors that can influence capacity. A better outcome could instead be, “reducing downtime for a machine valve to 1%.” Here, a data-first approach might sound good at first, but in the end may provide little value.
Using an outcome-first approach to digital transformation allows a company to concretely define a problem and create measurable metrics to map to, and then structure the data to support those outcomes. Not only does this approach save time, but it helps mitigate the risk that comes from uncertainty in data — which all leads to maximized returns.
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