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Don’t drown in a flood of digital data—operationalize your insights with cognitive predictive maintenance and reap a massive competitive advantage for higher machine uptime and efficiency.
Data needs are exploding, with the volume of data generated by businesses growing exponentially. Most businesses today understand the potential business value of data, and many do a good job of collecting and storing it. It’s easier than ever to collect high quality data, but this is not enough. In fact, the sheer volume of data at the disposal of businesses can quickly feel overwhelming.
According to McKinsey, the total IoT market size will have grown from $900 million in 2015 to $3.7 billion in 2020, a compound annual growth rate of 32.6%. This clearly reflects the rapid rate at which our industrial assets are being connected. IHS further forecasts that the IoT market will grow from an installed base of 15.4 billion in 2015 to 30.7 billion devices in 2020, and will keep growing to 75.4 billion in 2025.
Organizations must learn to leverage their data to gain the operational insights that can slash costs and increase efficiency—without that, the enormous potential captured within the data goes to waste. Much of the time, data that is gathered comes from numerous data mining platforms and is funneled into multiple different and siloed analytics systems, further complicating the picture.
Given that the flood of data being generated is growing, and often in real-time, one solution is to hire an army of data scientists to analyze it all. However, this quickly becomes expensive and doesn’t scale well. Through a combination of connected technology and cognitive data science, companies can improve their capacity without such a high-priced investment, particularly by taking advantage of cognitive predictive maintenance.
A common analogy for data in recent years is to compare it with oil, the raw resource that became iconic for powering industry in the 20th century. However, just as with data, it must be refined to realize most of its value. So let’s take a look at this industry as an example, and examine how they might make better use of data.
Oil and gas companies today are generating tremendous amounts of data, with the average offshore production platform utilizing 40,000 data tags. For most organizations, most of this data remains underutilized, even as 40% of new capital projects within the industry fail in terms of budgets and schedules, according to a study published by Booz Allen Hamilton.
It is clearly critical for oil and gas companies to learn how to use this data to their advantage.
Oil and gas exploration requires massive machines operating in extreme conditions, and even with millions of dollars spent on upkeep and repairs, the risk of a spill or breach looms large. Predictive maintenance allows companies to analyze their data and predict when service is genuinely required on a specific part, optimizing efficiency along the way.
Predictive maintenance solves several key challenges for large industrial operations like energy exploration. Equipment value can be maximized, with performance data for drills and pipelines analyzed to spot weaknesses before they become failures and determine the most efficient and safe usage of underperforming parts. Operational efficiency is boosted by the elimination of unexpected delays and downtime—the Department of Energy, in fact, estimates the oil and gas industry could reduce downtime by 45% by integrating connected technology. Crucially, worker safety is also improved by reducing the need for workers to conduct in-person inspections and predicting when situations will be dangerous for humans.
Taken together, these improvements can result in enormous benefits to worker health and a company’s bottom line.
Harnessing the full value of your data is incredibly valuable, whether you’re a large industrial enterprise looking to slash maintenance costs or a small to midsized business trying to capitalize on your predictive analytics. At Progress, we’re democratizing data science to make it easy for companies to build cognitive-first applications and realize these gains more effectively than ever.
Cognitive predictive maintenance is a powerful tool that can improve safety, productivity and cost-effectiveness simultaneously. You can read more about my thoughts on the oil and gas industry in particular here, or head to DataRPM.com to find out more about how cognitive predictive maintenance can work for you.
Sundeep is a highly accomplished Data junkie, innovator and entrepreneur with over twenty years of experience in delivering insights and advanced analytics. Known for his what-if mindset, he co-founded DataRPM, a Progress company, with the goal of providing a platform that delivers hyper-fast cognitive data products to organizations challenged by the volume, velocity and variety of their big data and machine learning.
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