Organisations today are drowning in data. The relentless pursuit of metrics and measurement has led to an overwhelming flood of information, driven by the belief that more data naturally results in better decisions. This data deluge often has the opposite effect: slowing down decision-making, obscuring critical insights, and leading to analysis paralysis. Instead of enabling clarity, excessive data can bury key details, strip away essential context, and leave decision-makers fatigued, uncertain, and unable to act with confidence. In complex operational environments, where precision and speed are crucial, the challenge is no longer about having enough data — it’s about transforming it into meaningful, actionable insights.
Business leaders are increasingly overwhelmed by the sheer volume of data they must process. According to Oracle, 74% of executives feel inundated by data, leading to decision paralysis and poor outcomes including reduced operational performance. Celerdata reports that 40% of executives have made poor decisions due to data overload, costing organisations millions each year. Gartner estimates this loss at approximately £11 million per organisation annually, underscoring the significant financial impact of unmanaged data being presented up through organisations.
Decision-makers throughout organisations should be demanding insights from their teams rather than raw data. The shift from data-driven decision-making to insights-led leadership is about leveraging advanced analytics to extract meaningful, contextual intelligence. Instead of presenting isolated metrics, organisations use AI and machine learning to analyse historical trends, identify patterns, and predict future outcomes within the current business landscape. This approach enables leaders to see not just the data, but its implications: what issues need attention, what decisions are available, and the likely impact of each choice. By turning complex data into clear, actionable insights, organisations empower decision-makers to act with confidence, improving efficiency, reducing risk, and driving pace.
Consider a decision-maker overseeing an industrial process who receives raw temperature readings. Rather than presenting those numbers in isolation and requiring them to interpret the data themselves, an insights-driven approach would highlight that recent trends, combined with operational logs, event records, maintenance reports, and weather conditions, indicate a potential risk of equipment overheating. That level of contextual analysis enables leaders to take proactive measures, mitigating risks before an equipment failure occurs and preventing costly downtime.
With advancements in analytical methods, particularly advanced analytics and artificial intelligence models, organisations can now demand insights rather than data. By feeding analytical models with data and applying guiding principles, organisations can sift through and interpret information to generate meaningful intelligence — revealing what the data indicates, its potential impact, where decisions need to be made, and the available options.
Not even the most effective Portfolio Management Office could produce this at the scale that analytical models make possible. The equivalent would be a PMO printing a thousand sheets of paper, pinning them across the wall, and spending a week walking the room connecting the dots. Modern data computing methods can do all of this in seconds.
Just as Henry Ford recognised that people would ask for a faster horse rather than envisioning the motor car, many organisations today are still demanding more data, unaware that insights are the real gamechanger. Industry leaders like BP and EDF Energy have already made this leap, moving away from data overload in favour of AI-driven insights that drive efficiency, safety, and strategic decision-making. The challenge is not the technology; it’s understanding that a more powerful way forward already exists and is delivering value for those willing to use it.
BP: Predictive Maintenance in Oil and Gas
BP operates in the highly regulated, high-risk oil and gas industry, where complex operations and significant safety considerations make effective maintenance and timely decision-making critical to operational safety and minimising downtime.
BP implemented an advanced predictive solution to transform its maintenance activities. Using an analytical model incorporating AI and machine learning algorithms connected to sensors, data loggers, and operational shift logs, it could predict equipment problems or failures before they occurred. By centralising data into a single data lake from multiple sources, BP could analyse and visualise its data at pace, with algorithms highlighting poor performance and predicting asset failure in advance. The results were significant: a 30% reduction in unplanned downtime, a 15% improvement in safety through reduced likelihood of serious incidents, and savings of approximately £2M per year through minimised downtime and prevented costly repairs.
EDF Energy: Real-Time Monitoring Across Reactor Stations
EDF Energy has faced challenges with unplanned outages and has invested in advanced analytical methods to address them. The business deployed an advanced analytical platform to process real-time data across reactor stations and operating systems. Using algorithms, it could interpret the combined data landscape, identify forming trends, highlight anomalies, and begin predicting equipment failures before they occurred. Real-time monitoring and analysis enabled faster response throughout. The demonstrated benefits included a 25% reduction in unplanned downtime, measurably more stable and reliable facilities, and cost savings of approximately £1.5M per year through reduced unplanned downtime and enhanced operational efficiency.
The People Challenge
Many other organisations have followed the path laid out by EDF and BP, shifting their relationship with data and moving towards insights. It is not a secret, but it requires the right thought leaders, a clear demand from the business, and an investment in time and people to enable the transition.
Investment in technology and data architecture that enables advanced analytics is essential, but — dare I say — this is the easy part. For organisations to succeed, they must understand that this represents a significant change for their people: how they work, think, and make decisions.
Enabling large-scale analytics through data lakes and enterprise storage requires more than technology; it demands a shift in how people work. This means moving away from fragmented spreadsheets on personal drives and adopting centralised, standardised data solutions. Many organisations resist this, believing their processes are unique, but true transformation requires consistency. Once these hurdles are cleared, the next step is trust: leaders must embrace insights over raw data, replacing seventy-slide reports with focused, decision-ready intelligence.
Bringing people on this journey is critical. McKinsey reports that 70% of digital transformations fail due to neglecting the people side of change. Success depends on strong leadership, clear communication, and active employee engagement. Training and support are essential, but the real difference comes from building a culture where teams understand the value of insights and champion the shift. Those who align their people, processes, and technology will unlock the full potential of insights-led decision-making.
Conclusion
Shifting from data to insights isn’t just a technological upgrade; it’s a fundamental people challenge. Industry leaders like BP and EDF Energy have demonstrated that AI and advanced analytics can transform decision-making, boosting efficiency, safety, and performance. This transformation must be approached with care, ensuring cyber-secure methods are used, rigorous assurance processes are developed and deployed, and data integrity is maintained to ensure trust in insights is established. The key to success lies in strong change management: aligning technology with culture, fostering continuous learning, and ensuring users experience the benefits of the shift directly.
by Stuart Gorman
