The time for Industrial DataOps is Now

The Mother of [Re]Invention

The year 2020 forced certain industrial sectors, once seemingly invincible, to grapple with their mortality. The economy was shattered. Oil prices fell below zero for the first time in history. Airline stocks bottomed out. Millions of jobs were lost.

Worst case scenarios were no longer just hypotheticals: they would be distinct possibilities without massive efforts to transform, to address vulnerabilities, build resilience, and further digitalize. In fact, had the pandemic happened just 10 or 20 years earlier, before the advent of the technology that saved lives, jobs, and markets, we could have seen far worse.

Suddenly, once-analog traditional industries like oil and gas, manufacturing, and power and utilities, began adopting digital tools at a faster pace.

Covid-19 forced most workers home, so we tested remote work en masse—and largely with success. We also piloted remote operations and learned more about honing technology to create real value out of industrial data. Necessity was the mother of a wide-scale reinvention.

Meanwhile, these same industries started to address public and financial pressure to own up to environmental impact and unchecked emissions.

After all, unprecedented climate-related disasters were playing out before our eyes, adding insult to the pandemic’s injury, holding our gaze on frightening truths. Australia, California, and Colorado burned. Power grids were compromised. A record number of hurricanes hit the US Gulf Coast. Southeast Asia dealt with record floods. Siberia melted.

Eyes Wide Open

However jarring these realizations, opportunities come from having our eyes wide open. The collective progress that industrial companies made in 2020 came largely out of necessity and a real sense of urgency, but were nonetheless impressive. By and large, digital transformation is no longer seen as an overused PR term, nor a risky CAPEX move.

Adopting industrial software, digital tools, robotics, and new agile ways of work are gaining ground. These are areas where asset-heavy industries had been notoriously behind the curve.

The value of industrial data, liberated and contextualized by a new generation of software solutions, is now widely recognized. And finally, a willingness to share, open and digest such data is catching on across our industries. The time is now to capitalize on this.

The foundation for this necessary, sustainable reinvention is here now. It is built of public and financial incentives, competitive motivation, survival instincts, innovation, and genuine goodwill. Required now, and at scale, are the know-how, the technology, and the tools to do the actual transformation work.

Industrial DataOps: A New Tool in Our Box

One absolutely essential tool is the new, rapidly growing discipline of DataOps, and more specifically, the novel approach of Industrial DataOps.

Just a twinkle in the eye of industry today, we expect that this will become one of the single most important vehicles turning industrial data into tangible value. In doing so, Industrial DataOps will become a driving force in industrial transformation.

Because Industrial DataOps is a truly innovative and transformative new approach, it takes the rest of this six-chapter book to fully unpack what it is, why it’s important to your company and to your industry, why we need it now, and how to begin implementing it. Your 101 into Industrial DataOps starts here.

Industrial DataOps in the 2020s is as new and unknown as DevOps was in the 2010s, as business intelligence was in the 1990s, and as human resources was in the 1890s.

What Industrial DataOps Is

The core principles of Industrial DataOps will be developed in more detail in the following chapters. So here we’ll focus on simply providing a basic outline. Let’s begin by defining what DataOps is. DataOps can be defined as a new discipline which typically includes a team of data experts (think: data scientists, analysts, architects, and the like) who exist to “provide the tools, process and organizational structures to support the data-focused enterprise.”

3 key truths about Industrial DataOps

Industrial DataOps depends on collaboration with domain experts.

Individuals and interactions (far more than processes and tools) are essential to make data valuable and useful for the data consumers across an organization: the domain experts in different fields and departments. It’s important to remember that DataOps is a practice, a way of engaging and collaborating across the organization to both share and reap greater value from the data.

Data is only as valuable as the analytics behind it and the scale of people who use it.

The convergence of data and analytics has made Industrial DataOps an operational necessity. But the data requires context if it is to be used broadly by non-data doctors. Only by automating the data process and creating one central, contextualized source of truth, can we ensure the live data triumphs over its predecessors (the static documentation and reports of yesteryear) in the decision-making process.

Extracting the value of the data requires an agile approach.

Industrial DataOps isn’t about documenting, reporting, or extensive up-front design. It’s a far more agile process in which experimentation, iteration, and feedback are essential. Creating business value isn’t a one-way transaction between data scientist and department. It’s a joint effort that will require both parties to participate, share, and develop solutions that hold transformative potential. Just as the data is alive, so are the means of working with it.

What Industrial DataOps is not

Industrial DataOps is not DevOps. DevOps has been around for some time and, despite some superficial commonalities, DevOps is very different. Both are methodologies used to enhance operational practices, but that’s where the similarity ends.

The focus for DataOps is the delivery of business-ready, trusted, actionable, high-quality data, available to all data consumers or domain experts throughout the organization. One goal is automation efforts, centered on data governance and integration.

Another goal is alignment between IT system support, operations, and the business. The focus for DevOps, on the other hand, is software and application development. Automation efforts center on the development cycle, software delivery processes, and waste elimination. The alignment of developers, operations, and the business is a major aim of DevOps.

The above has been a starting point for exploring how data can enable asset-heavy industries to develop solutions and data products, and to extract value across an entire business.It’s almost impossible to imagine that not long ago, data was a protected commodity, something to be kept to oneself or even used as a bargaining chip. This was, thankfully, a short-lived narrow-mindedness, brushed aside by visionaries who saw the amazing potential of data. Their vision of the future has spread to the rest of us, sparking the emergence of Industrial DataOps as our lifeboat in a vast sea of data.

So why now? Because delaying the deployment of advanced data operations and new ways of working will only delay the transformation of your company, and by extension, your industry, making achieving your end goals slower, harder, and more expensive.

More and more asset-heavy industry companies are waking up to the power of data and its ability to inform decision-making, transform operations, and enhance sustainability.

As this happens, we hope that you come to see this as an essential guide to rolling out Industrial DataOps in your organization.