In a world where even our refrigerators can prioritize ingredients for us—prompting us to use the red pepper over the cucumber based on freshness—many organizations still struggle to unlock the full potential of data. This paradox is stark, especially when we consider companies like Amazon or Meta who use data to optimize customer experiences, personalize content and even drive consumer behaviors. Yet despite such powerful examples, a vast number of companies continue to fall short in unlocking data-driven insights that could revolutionize their operations and asset management practices.

For asset intensive industries, the opportunities for companies to make more insightful decisions are vast. Data can inform asset health, streamline maintenance, and optimize investment, yet these benefits remain mostly untapped. Having seen the transformative power of data in my own work, I can attest that data-driven decisions are integral to predictive maintenance, operational optimization, and intelligent investment prioritization. A well-ran operation is not only safer but also more profitable. And what better way to manage an operation than by understanding real-time indicators and risks?

When you consider the rapidly decreasing costs of technology, the growing awareness and the knowledge of how technology can support day to day operations and the ability to connect almost seamlessly across different platforms on a global scale, it is clear that we live in a new world. Today, transitioning from reactive or time-based maintenance to a predictive, data driven approach is more achievable than ever. So why, despite all these advancements, do I find myself repeatedly encountering skepticism in boardrooms and on shop floors, facing teams that have “heard it all before”?

In one recent example, a team informed me that they’d seen three similar data-driven initiatives in the past decade- each one touted as transformative, each one ultimately falling short. Understandably, they expected this attempt to fail as well. Ironically, some of these same employees likely returned home that night to check their smart fridge, following its advice to use the red pepper first and order some more in next week’s shopping! Why do we readily accept technology in our personal lives while remaining reluctant to trust it in our professional environments?

The answer lies not in technology itself but in how we structure, manage, and communicate the transformation process. Shifting to a data-driven model isn’t simply about implementing dashboards, installing sensors, or adopting software; it requires a deeper, more holistic change that integrates seamlessly into the organization’s fabric. Here’s how we can finally make the leap into a data-powered future.

1. Reframe project outcomes to focus on transformation
The outcome of a data-driven project shouldn’t be viewed as the successful installation of a dashboard or completion of a new data model. Instead, it should be about establishing a new way of working. This shifts emphasis away from technology to process improvement, business integration, and organizational alignment.

Successful data transformation requires new business processes, employee training, and even shifts in organizational structure and skills. Instead of focusing on the technology alone, we must understand why the current practices exist, learn what gaps they leave, and design a future state that truly adds value. When the project is framed as a reimagining of workflows rather than just a technical upgrade, we’re better positioned to understand the “how” and “why” of the transformation, thus setting a more meaningful path forward.

2. Prioritize design to envision the future state
Given the scale of the transformation, time spent designing the target operating model is crucial. Too often, projects skip this essential step in the rush to show quick wins. But a truly sustainable transformation requires thoughtful design. The goal is to create a model that contemplates each facet of the desired future, including both positive impacts and potential drawbacks.

Virtual or physical brainstorming sessions can help the team consider the operational and functional requirements. This design phase allows organizations to clarify project scope, define requirements, and anticipate adjustments, reducing the risk of later disruptions. In short, building a resilient foundation ensures the system we construct will support the organization’s evolving needs.

3. Secure the right sponsor to drive holistic change
The importance of a strong executive sponsor cannot be overstated. A data transformation project impacts multiple departments, including IT, operations, maintenance, asset management, investment planning, and HR. The sponsor must have both the authority and the influence to drive change across these areas.

Ideally, this sponsor is not only empowered but also invested in the long-term success of the project. A strong sponsor can advocate for resources, remove obstacles, and keep the transformation on track despite inevitable challenges. By positioning a sponsor who understands both the strategic vision and the operational realities, the project gains a champion capable of navigating the complexities of cross-functional change.

4. Embrace resilience and iteration
Even with a well-defined outcome, a strong design, and a committed sponsor, achieving lasting transformation requires resilience. The shift to a data-driven operating model won’t happen overnight, nor will the first iteration be flawless. It takes patience to refine the approach, collect insights from each stage, and iteratively improve processes and tools. However, by embracing an iterative mindset, we open the door to gradual yet substantial progress.

Over time, the transformation can become embedded within the organization, supported by a continuous feedback loop that fuels further enhancements. With this approach, organizations are more likely to sustain their new way of working and reap the full benefits of data-driven decision-making.

Final Thoughts: Be prepared for a future of proactive asset management
Ultimately, a successful data transformation enables an organization to operate proactively rather than reactively. Just as our smart fridges help us make better choices by guiding us to the freshest ingredients, data-driven insights can guide our operations to reduce risks, improve efficiency, and support growth.

By focusing on real transformation, securing the right sponsorship, and embracing resilience, organizations can finally overcome the skepticism and fatigue that have plagued past attempts. It is possible to create an environment where asset management and operations are empowered by real-time data insights—where we “eat the red pepper” instead of letting it go to waste. The organizations that achieve this will not only gain a competitive edge but will also set a new standard in asset management and operational efficiency