Getting Started
Industrial DataOps admittedly requires you to do some prep work. You need agreement and unity among all data stakeholders in your organization. That kind of cohesion and cooperation needs to go beyond just making data available and allocating resources for that. Your business functions will need to be geared and organized for close involvement in data projects well beyond the resource allocation, proof-of-concept, and scaling phases.
Getting to that point won’t happen overnight. It requires a longer-term view and committed leadership to bridge divisions and align your vision. But the commitment will pay ample dividends and shore up your future success.
Are You Ready?
Getting through this book means there’s a chance you want to be. To assess your readiness for Industrial DataOps, you will first need to assess your organization’s digital maturity. As we said in Chapter 1, digital maturity is a key metric of digital success. This also bears repeating: Getting real ROI from digital initiatives is more likely to come from steady innovation and long-term strategy, than from a quarter-long burst of digital enthusiasm to impress stakeholders (or shareholders).
A sign of digital maturity is that you’re able to naturally focus on the long game, building through tools and processes so that digital ways of working become effortless across a broad range of stakeholders. Another sign is that you’re able to measure, measure, and measure—at both the granular and the holistic levels. We’ll say it again: digital maturity spans people, processes, and data. It is a multidimensional means of change management.
Evaluate on Purpose & Strategy, People & Mindset, Value Capture Process, Information Architecture, and Data Ubiquity, and go from there.
What Industrial DataOps Can Do for You
As we saw in Chapter 4 (Industrial DataOps in Action), some leading organizations have made an early start and are already leveraging Industrial DataOps to AI, data-intensive applications, complex research, and analyses.
Some other great examples come via researchers at MIT, who interviewed a number of companies to determine how they were capitalizing on the new approach. The results revealed a range of highly beneficial use cases from industry.
One of the organizations is “processing and analyzing an extraordinary amount of data using modern data management practices to help automate shipping fleet maintenance”. To do this the company is using predictive analytics on “trillions of data points” to estimate repairs and breakdowns.
Another company is focusing on monetizing data to help reduce shipping fleet downtime, which saves time and costs while keeping the fleet operational. The organization identifies trends toward failure before they become alerts, and uses the data to understand present and future vessel performance.
Autonomous vehicle operation is another area poised to benefit from Industrial DataOps. As major auto companies double down on R&D in autonomous driving, enormous volumes of data, including telemetry and imaging, will be generated by autonomous fleets and hardware (traffic lights, road sensors) to further leverage as a basis for innovation.
On a single asset alone, Aker BP is recording annual savings of $6 million per year in saved time and reduced production losses.
In the world of oil and gas, Norwegian operator Aker BP is deploying an Industrial DataOps framework in its operations to comply with regulations on oil in water and reduce production losses. In essence, they have implemented a DataOps-powered smart monitoring system that visualizes all data relevant for troubleshooting water contamination, and a recommender system with an underlying machine learning model to identify the worst actors related to high oil-in-water concentrations.
The smart monitoring system displays near real-time data from their Industrial DataOps platform and visualizes it in an intuitive dashboard. Additionally, calculations combining sensor values and simulator outputs provide engineers with virtual sensors and physical properties they otherwise would not have had readily available.
On a single asset alone, Aker BP is recording annual savings of $6 million per year in time and reduced production losses, all while protecting the local environment and complying with ever-changingenvironmental regulations.
Imagine this single case, focused entirely on detecting water contamination, scaled up over an entire set of assets or an entire field, across operators. The potential for colossal, positive transformation is evident.
The effects are even more powerful when data sharing is made possible. Aker BP works with Framo, a major supplier of submerged cargo pumps to the oil and gas industry. Their Industrial DataOps platform enabled the secure sharing of selected live data between their two organizations. Framo used Industrial DataOps to access Aker BP’s industrial data, which helped inform their product development, leading to a more sustainable, performance-based business model. The solution cut emissions and waste by reducing maintenance needs by 30 percent and shutdowns by 70 percent, and by increasing pump availability by 40 percent.
The potential for colossal, positive transformation is evident.
Imagine this single case, focused entirely on detecting water contamination, scaled up over an entire set of assets or an entire field, across operators. The potential for colossal, positive transformation is evident.
The effects are even more powerful when data sharing is made possible. Aker BP works with Framo, a major supplier of submerged cargo pumps to the oil and gas industry. Their Industrial DataOps platform enabled the secure sharing of selected live data between their two organizations. Framo used Industrial DataOps to access Aker BP’s industrial data, which helped inform their product development, leading to a more sustainable, performance-based business model. The solution cut emissions and waste by reducing maintenance needs by 30 percent and shutdowns by 70 percent, and by increasing pump availability by 40 percent.The seeds have been planted for solutions like these to scale massively over the coming years. Ever-growing sources of data will intensify the need for smart extraction, liberation, contextualization, and analysis, underlining the need for Industrial DataOps.
Companies who commit to this now will reap the benefits of converting meaningful data into impactful, measurable action. Meanwhile, the sophistication of users will develop and improve, and tools that were once overly technical and confusing will become powerful platforms of change, even for the digital layperson. The ascent of Industrial DataOps is now bringing on a period of unprecedented collaboration between humans and machines. This potential to essentially synchronize human knowledge and creativity with advanced technology is still largely untapped in the industrial world. This is without a doubt one of the most significant big-picture opportunities of Industrial DataOps.
