Driving agile digital transformation in a complex world

Digital transformation insights

• Investment in digital transformation is growing at a compound annual growth rate of 15.5% from 2020 to 2023. By 2023, 75% of organizations will have “comprehensive digital transformation implementation roadmaps,” up from only 27% in 2020.
• The concept of the Trojan Mouse holds that return on investment should not be used as a measure of success when the investment is small because well-placed, small experiments help implementors “learn by doing,” while leaving space to increase value by scaling lessons to grander systems.
• To provide the speed, flexibility and agility required for successful digital transformation in today’s paradigms, it is essential to bring cloud, edge and software-as-a-service (SaaS) strategies together to improve data availability and access.

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Many have been pitching and working to achieve elements of Industry 4.0 and digital transformation strategies for the last 15 years or more. They can feel vindicated. A recent IDC Report (Figure 1) stated and predicted:

Investment in digital transformation is growing at a compound annual growth rate of 15.5% from 2020 to 2023. By 2023, 75% of organizations will have “comprehensive digital transformation implementation roadmaps,” up from only 27% in 2020.

Fast forward to 2023, and nearly all CEO agendas contain some elements of digital transformation. This is great news for engineers. After all, doesn’t the CEO have the greatest influence on the necessary scale of cultural and sustained change required for successful digital transformation efforts? Perhaps, but then again, perhaps not. Leadership commitment is one piece of the jigsaw.

有一个路线图挖ital transformation that does not include huge, scary or costly projects. There is an agile route to digital transformation, innovation and empowerment that minimizes risk while still maintaining value.

Wheel in the trojan mice

Back in 2012, the digital thought leader, Euan Semple, talked about the concept of the Trojan Mouse in an organization’s digital journey. In this concept, he postulated return on investment should not be used as a measure of success when the investment is small because well-placed, small experiments help implementors “learn by doing,” while leaving space to increase value by scaling lessons to grander systems.

The Trojan Mouse concept has since been picked up by others, including Alex Blanes in a 2019 paper “Why trojan mice are the best-kept secret of successful teams.” The Trojan Mouse has its place in systems theory as a successful approach to break down complex, as opposed to complicated, problems. This classification demands a certain type of emergent “sense and respond” approach to succeed.

Applying big-bang digital technology across a large, complex organization to change employee behavior and processes is, according to systems theory, the wrong approach. We witness this too often in dismal success metrics of large digital projects.

However, a “platform” approach, facilitates and reduces execution risks of digital Trojan Mice from within the system, is a positive step, provided an organization is set up to embrace it. By democratizing data access, making data manipulation and calculations easier and facilitating easy collaboration for knowledge sharing and reporting through innovative technologies, companies are more likely to roll out successful Trojan Mice and larger digital transformation projects down the road. In fact, many large consulting firms are adapting these methods throughout internal and client-focused digital transformation programs.

在过程行业,这种方法是成功的sful when applied to self-service advanced analytics solutions. The value proposition is an ability to serve data from almost any data source, providing a simple but powerful interface for working with small problems in collaborative teams. This sort of software architecture, training and knowledge framework empowers teams to scale successful Trojan Mice organization-wide.

As workplace collaboration thought leader Harold Jarche said, “Collectively, a few Trojan Mice will change more than one Trojan Horse ever could!”

Data is the new oil

Clive Humby, the data science entrepreneur, coined the phrase “Data is the new oil” in 2006. While there may be some problems with this analogy, it holds up well for the most part.

Data is the new oil. Like oil, data is valuable, but it cannot really be used if it’s unrefined. Oil has to be changed into gas, plastic, chemicals, etc., to create a valuable entity that drives profitable activity. Data must be broken down and analyzed for it to have value.

About two-thirds of the world’s data is unused, and while data is not a finite resource like oil, it does have a carbon footprint. With data centers expected to account for 8% of world energy usage by 2030, that two-thirds of digital waste is hurting our environment.

人们必须仔细考虑数据的决策:How to generate it, how much to generate, how to store it and how to create insights. Thinking about a data strategy, architecture and governance is now the most important aspect of creating new assets. Comprehensive data taxonomies and attribute models for enterprise assets not only speed project delivery and handover, but they also are critical steps for further digital transformation.

Many process engineers are managing a legacy of poor, antiquated data structures and governance, and making appropriate corrections is expensive and time-consuming. Herein lies yet another source of complexity that too frequently proves to be the death of digitalization, digital twins and large business intelligence projects. For these types of efforts to succeed, excellent data architectures and governance are required.

To provide the speed, flexibility and agility required for successful digital transformation in today’s paradigms, it is essential to bring cloud, edge and software-as-a-service (SaaS) strategies into the mix. This calls back to the Trojan Mouse approach of starting small and scaling rapidly by beginning complex processes with bite-size chunks with the primary objective of learning.

The best initial approach often is improving organizational data access with a cloud-based data platform. This hooks into source data, but only pulls what it needs to generate insights. This data storage is often a short-term cache to execute calculations and deliver visual insight. All data tagging, hierarchies and governance can be built specific to high priority data, serving steady growth and problem solving, as opposed to creating huge corporate taxonomies off the bat.

例如,软件可能代理连接real-time data, pulling in what it needs to a short-term cloud cache on demand. This introduces no added data management complexity, so an organization can extract value and democratize the most important data to drive a parallel, agile approach to data governance.

Quoting Sol Rashidi, chief analytics officer at Estée Lauder, organizations need to “know what data matters most, prioritize it, build the discipline to protect and govern it, then democratize it to enable data specialists and end-users to extract the insights they need to innovate.”

Building a digital world for humans

Andrew Keen, a digital commentator, posits “Being human in the digital world is about building a digital world for humans.” In other words, the answer to technology overload is not less technology, but more humanity.

This mantra is important for digital transformation projects, but it is especially critical now in the world of challenging demographics for engineering and manufacturing job roles (Figure 2). Multiple surveys agree around 90% of manufacturing companies are suffering a skills gap, estimating a 2.1 to 2.4 million job shortage within the next decade in the U.S. alone, with an economic impact of around $2 trillion.

Strategies for dealing with these problems include hiring more people, training and reskilling and attracting more young workers and students, but these approaches take time to refine and may not succeed, especially as many required skills in manufacturing rapidly change simultaneously.

There are no silver bullets for this problem and the answer, like everything else in this discussion, is complex. For organizations to effectively ride out the storm, they must create cultures of knowledge sharing, collaboration, openness and employee engagement. When these strategies are combined with reskilling and automating tasks where practical, the skills gap’s impact is reduced.

While visionary leadership makes a significant contribution in these areas, but technology is fundamental, too. In a successful digital workplace, workers must be able to access all of the data required to do their jobs, empowered to deliver improvements with systems and processes in place to foster efficiency and effectiveness. These include elements like:

1. Self-documenting software, with the ability to share work with the push of a button in a knowledge sharing framework.

2. Automation to speed business processes and break down bureaucracy frustrations.

3. Collaboration tools to remove hierarchical barriers, making work engaging and satisfying.

4. These sorts of touches make workplaces feel more “human,” helping attract and retain the skills companies need to make digital transformation successful. In the words of Michael Jordan, “Talent wins games, but teamwork and intelligence win championships.”

There is more to overcoming the skills gap than training. A culture of engagement, collaboration and speed-to-insight is needed to navigate the skills transformation over the coming years.

Getting digital transformation right

Digital transformation victories are difficult to attain. It takes great leadership, courage and trust to successfully change a culture, foster democratization and empower staff with a simple, collective access to trustworthy data and insightful information. This challenge must be approached with enthusiasm and optimism — especially when initiatives do not go as planned.

For process manufacturers, embracing an advanced analytics platform is critical for providing staff with the right tools to contextualize, analyze and make the right data decisions.

Powerful technology itself does not ensure success, but software tools with wide-reaching connectivity, automated data cleansing, cloud access, capable algorithms and intuitive collaboration means cannot be overlooked in organizational digital transformation initiatives.

Companies must nurture, grow and support their “intrapreneurs” with systems and training to allow data-driven digital transformation to blossom, and patience, clear leadership, strategy and flexibility along the way help usher in these results.

As Aristotle proclaimed, while undoubtedly contemplating digital transformation in the Parthenon at Athens, “The whole is greater than the sum of the parts.”

Consider what MIT’s George Westerman has to add on the topic: “When digital transformation is done right, it’s like a caterpillar turning into a butterfly, but when done wrong, all you have is a really fast caterpillar.”

Digital transformation is not just digitalization for the sake of advancement. However, it is meant to elevate human stakeholders.

Begin with a plan. Enable and empower. Start small — think Trojan Mouse — and make scaling easy. Build on successes and learn from shortcomings. Don’t just create a faster caterpillar.

Paul Ettridge, digital applications consultant, Seeq. Edited by David Miller, Content Manager,Control Engineering, CFE Media and Technology,dmiller@cfemedia.com.

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