Are You Ready for Industrial DataOps?

Digital Maturity Is a Key Metric of Digital Success

Any organization that wants to adopt and enjoy the benefits of Industrial DataOps needs to consider the state of its digital maturity. It’s clear that outsized ROI from digital investments is more likely to come out of sustained innovation and long-term digital strategy, rather than just one or two quarters of focused effort. This doesn’t mean that deploying more use cases isn’t important and that short term-value isn’t possible, but rather that digital programs and operational momentum are difficult to get started and can fail quickly for many different reasons.

This is exactly why so many organizations are frustrated right now; they expected outsized short-term payoffs, not fully realizing the longer-term play. To be fair, the broader digital transformation market did overpromise the immediacy of value, but the solutions market remains accurate regarding the significant potential of advanced digital technologies. This is a classic case of “when,” not “if.”

Symptomatically, this disconnect manifests itself in terms of stalled digital transformations, as the evidence from McKinsey (Fig. 1) demonstrates compellingly.

Across industry, stalled progress happens most often during the scaling phase, with 62% of the root cause being factors that are within the organization’s near to medium-term control. Developing an operational proof of concept (POC) for example, has become much simpler in the last two to three years, while scaling still remains a challenge.

This points to a very important parallel, however: POCs are short-term projects while scaling is long(er)-term. What’s missing in this paradigm today is the lack of a proper people, process, and platform flywheel to carry and build digital momentum forward from phase to phase.

Outsized ROI from digital investments is more likely to come out of sustained innovation and long-term digital strategy, rather than just one or two quarters of focused effort.

The measures and KPIs used in decision-making today are so entrenched in showing short-term value that they neglect to incentivize the next phases of digital transformation that support scaling and beyond.

Here, new long-term metrics around operational adoption, cost of analytics, and digital spend as a percentage of operations become a much more important part of the equation. They can be bundled into a new and arguably more critical measure: digital maturity.

Digital maturity, when codified as a practice and quantified as a metric, represents a sustainable way of measuring progress in aggregate, and a better measure of overall industrial digital transformation health.

Defining Digital Maturity

The first shift that must occur involves realizing that the digital maturity curve is exponential, not linear. In broad terms, early maturity consists of many linked or unlinked data “pieces” or building blocks that, when combined with high-cost internal or third-party services, start to create silos of opportunity and kick-start small-scale projects and POCs. As the organization matures, internal or vendor-delivered application frameworks start to create some repeatability, especially when combined with a data analytics platform.

This platform serves as a bridge to high maturity, because it carries forward the momentum and infrastructure to develop data analytics catalogs and libraries that can then be deployed with fewer services and at lower marginal costs. It is during this transition that outsized ROI starts to develop.

What Does High Digital Maturity Look Like?

While building organizations in support of digital maturity can look different at a company level or within an individual line of business, high-maturity organizations share some common attributes, segmented by internal versus external factors (Fig. 3).

When an individual organization aspires for and invests in achieving digital maturity as a strategy, organizational performance improves.

Recent research by Deloitte clearly associated more digitally mature organizations with greater efficiency, revenue growth and net profits above the industry average, as well as higher product and service quality, better customer satisfaction, and stronger employee engagement.4

Digital maturity therefore paves the organization’s way for success, but this potential may not be realized if the digital competencies of one department are significantly more evolved than those of the rest of the company.

Contrast this with a company-wide mandate to invest in digital maturity, and the resulting momentum shifts from focusing on the nuance and noise of every digital project—whether successful or not—to addressing root causes.

Digital Traps to Avoid

Now let’s unpack a few examples of the traps organizations can fall into on the quest for digital maturity.

Digital Trap 1: Skunkworks Projects

Even in low-maturity organizations, business problems or opportunities will eventually get addressed. This typically happens in an ad hoc manner either once they become frustrating enough or it becomes impossible to ignore the potential perceived value. Here, opportunistic employees are likely to take the initiative and try to solve the problem given their existing skills and tools.

Take for example, an operator who is frustrated because they can’t easily compare historical events with the time series data for a particular asset. If they had access to the asset’s history and a record of time-stamped events, they could create a spreadsheet-based report to link the data and visualize it in a simple table.

Many of the problems being solved through these skunkworks (informal innovation) projects are highly valuable. But unless the organization’s digital maturity supports an intentional, low-friction path to productionalize the application, only a fraction of value might be realized because the report stays within its individual silo. This points to opportunities to improve digital processes and platforms.

Digital Trap 2: Vanity Projects

On another side of the spectrum, the organization may get involved in projects that seem innovative and groundbreaking but never materialize into tangible, traceable business value. Either the project costs significantly more than the value delivered or it introduces a new technology that creates additional downstream issues in the workflow, thus negating any significant benefit.

As new technologies such as augmented reality and industrial robotics are increasingly deployed in operational settings, it’s easy to see the challenges of this balancing act. On the one hand, the real-world use cases are tangible and valuable, delivering remote access, visibility, and safety to human operators. On the other hand, they serve as clear symbols to customers, markets, investors, and competitors that the company is aggressively pursuing digitalization.

If the question is “What’s needed to transform these vanity projects into long-term value?,” the answer is once again a higher maturity organization. In this case, the opportunity exists around leadership and process. Leadership must be able to balance needs for signaling with a tight, methodical value assessment and capture process that drives investment and progress on specific operational goals and KPIs.

Digital Trap 3: Elephant Projects

A third circumstance involves projects that are quite sophisticated and solve a real business problem, but require a massive amount of resources and maintenance to sustain over the life cycle. Certain artificial intelligence (AI) and machine learning (ML) projects fall into this category, because many digital teams underestimate the effort it takes to fully productionalize some ML-based predictive analytics.

Predictive maintenance on critical industrial assets is certainly a high-impact business problem that is worth solving. To a skilled data scientist, the process for creating initial models and showing predictive results is becoming less complex and more straightforward. But the problems shift as the models get closer to production. Once deployed, they still require maintenance, training, and integrations on an ongoing basis.

Unless the team has already accounted for that long-term overhead, the project costs must come down. Here, high-maturity organizations have been able to build in economies of scale at a data modeling level through investment in platforms, people, and processes. It becomes much less of a burden to carry the remaining overhead from multiple deployed projects, and resources can be shifted toward the next high-value project.

The Risks of Inadequate Digital Maturity

Over 100 oil and gas vendors went bankrupt in 2020 due to a number of factors such as the price of oil, shrinking demand, and a record surplus of reserves. This is obviously an extreme example with many variables, but it speaks to the risk of not fully leveraging data to identify hidden opportunities, change business models, or adapt to rapidly changing economic scenarios.

Pacific Gas and Electric (PG&E), California’s largest utility provider, provides a cautionary tale of what can happen when an organization fails to use data fully to its advantage. In 2018, transmission line faults sparked a deadly, large-scale fire, one of several blamed on the company’s equipment.

But better data and digital systems could have alerted engineers to the risk, preventing the loss of life and billions of dollars of damages.

Although the organization was known for embracing innovation and spearheading new technologies, PG&E’s motivations and digital infrastructure were not necessarily designed to enable these types of operational changes at the required pace. A higher level of digital maturity (and subsequent value from better simulation and condition monitoring) could have helped implement the digital systems needed to mitigate wildfire risks from areas where data is highly available.

At a very practical level, these are the key risks associated with failure to adopt a strategy of increasing digital maturity:

Moreover, these risks compound on each other, causing less-than-ideal strategy and decision-making that further destroy any momentum gained from digital programs.

The Rewards of Higher Digital Maturity

So what are the rewards of pursuing digital maturity? Well, in addition to offsetting the risks above, digitally mature organizations enjoy a host of tangible benefits:

Organizations Reaping the Rewards of Digital Maturity

There are many examples of industrial organizations embracing the journey to higher digital maturity and reaping the rewards. In the energy sector, BP continues to move at lightning speed with a strong digital program reflected from leadership, all the way into each individual business unit.

BP continues to expand its capacity to make informed strategic bets, fail fast, and quickly iterate on and develop solutions that can be shared across teams.

“You need to build your foundation so you can add the technologies that are much more advanced. You need to define the problems you want to solve and then implement those technologies.”

Robert Sentz, Senior Engineering Specialist, 3M

In manufacturing, 3M leads the charge with a realistic attitude toward digital maturity and summarizes well the challenges in this space. As Robert Sentz, Senior Engineering Specialist at 3M, said, “It’s very difficult to just dive right into digital transformation. You must have a solid and robust foundation in place to transform.

“To start the journey, step one is an assessment of what you have today and where you want to go in the future—a road map. Then you need to build your foundation so you can add the technologies that are much more advanced. You need to define the problems you want to solve and then implement those technologies.”

A third example comes from power and utilities, a sector known for cautious and late technology adoption due to the high stakes nature of its business. But the New York Power Authority (NYPA) is widely considered to be a model of digital innovation. NYPA has been aggressively pursuing innovation in both IT and operational technology (OT) as a strategy, choosing to risk small failures in service of developing a culture that supports next-generation thinking and technology implementation. The overall mission? To “lead the transition to a carbon-free, economically vibrant New York through customer partnerships, innovative energy solutions, and the responsible supply of affordable, clean, and reliable electricity.”

How Do You Measure?

At this point, we must start to convert theory into practice. Let’s refer back to the five key factors of digital maturity discussed earlier (Fig. 3). Generated after evaluating a number of organizations in asset-heavy industries and combining the findings with third-party research, these form a sophisticated framework for discussion and evaluation. 

Here, we can further segment the key factors influencing digital maturity into additional parameters that can be measured and weighed together to transform the concept of digital maturity into a diagnostic framework.

To achieve the best results and to truly have a living, breathing measure of this digital maturity journey, we recommend reviewing the questions with stakeholders of various related disciplines at least twice a year. This way, initiative and progress can be tracked over time across the maturity spectrum (Fig. 4).

Industrial Digital Maturity in Practice

Tactics and Execution

If there’s a single truism that spans just about every major industry, it’s that the industrial reality is far more complex than it seems on the surface. This is especially true when it comes to the future of data and its quickly evolving role in digital maturity.

As we conclude this chapter, hopefully we’ve equipped you with a new means to evaluate and measure progress throughout your journey. A serious consideration of digital maturity is an essential step on the road to implementing and realizing the benefits of Industrial DataOps.

The following chapters will move us into that field, starting with a look at DataOps as a discipline.