Business
Measuring What Matters
In 2017, Amelia Denniss flew to the Solomon Islands for her Doctor of Medicine Project, completing a five-week placement at a remote hospital where she donated walking aids. This experience highlighted a common challenge in global health: measuring whether well-intentioned efforts actually work.
Without systematic data collection, it’s difficult to assess the real impact of donations and interventions. That challenge – turning good intentions into measurable outcomes – carries a lesson for every decision-maker.
Across boardrooms, clinics, and government offices, people still make decisions based on gut feelings and tradition. That approach leads to stagnation. Without structured feedback, even brilliant policies can fail spectacularly. Organisations getting ahead are those weaving proof into every stage of their operations. They’re moving from raw metrics to national research priorities. They’re earning stakeholder trust and delivering results you can actually measure.
This shift is reshaping decisions in business, government, and healthcare. Organisations now face a decision: can they adopt the practices the data revolution requires?
Data Lakes to Decision Intelligence
Organisations today are drowning in data – terabytes pile up daily, yet true insight remains scarce. At its core, having data isn’t the same as having answers. Companies accumulate information faster than they can process. Analysis paralysis sets in. Data exists, but actionable intelligence doesn’t.
Dr Nate Jones, an assistant professor of biological sciences at The University of Alabama, researches how to turn complex environmental data into practical guidance. He said, “Through that experience, I’m realising that decision makers don’t just need good data—they need actionable intelligence. I’m really excited about exploring this more with GWSC (the Global Water Security Consortium) and learning how to work more effectively with decision makers, especially in parts of the world that have less data.” The gap between raw numbers and clear guidance represents the first hurdle in making data truly useful.
Healthcare technology systems face this challenge of converting complex data streams into actionable insights for improved patient outcomes. Solutions like data aggregation platforms represent a generic approach to this problem.
Gerry McCarthy’s work at Merative, a healthcare data analytics firm, shows how aggregation platforms function in practice. Solutions such as MarketScan, which aggregates commercial claims data for population-level analysis, and Merge Cardio, which manages cardiology imaging workflows, show how this functions in practice. By aligning MarketScan usage analytics with client outcomes, Merative achieved a 25% increase in Merge Cardio adoption. Under McCarthy’s guidance, client retention has remained above 90%, and satisfaction scores have consistently exceeded 85%. This alignment reduced development waste and sharpened product roadmaps.
The lesson applies beyond healthcare. Organisations that convert metrics into decisive action gain competitive advantage.
Government agencies that ignore this trend risk wasting public resources and losing citizen trust.
And nowhere are the stakes higher than in public policy, where data gaps can cost lives and livelihoods.
Policy Meets Proof
Evidence-based policymaking works. We’ve got decades of proof. The Clean Air and Clean Water Acts in the United States show exactly what happens when policy follows evidence. Lead levels in waterways dropped 80%. Sulphur dioxide fell 70%. Mercury emissions decreased 60%. Clear ecological and health benefits followed.
Yet politicians sometimes decide to cut funding anyway. The Trump Administration’s 2024 proposal to slash $12 billion from the Department of Education shows what happens when evidence gets sidelined. Over seven decades, that department built comprehensive research infrastructure. The Institute of Education Sciences, established in 2002, centralised research functions. Programmes like No Child Left Behind made rigorous impact evaluations standard practice in the early 2000s.
Dr David Canales Garcia is an assistant professor in Embry-Riddle Aeronautical University’s Department of Aerospace Engineering. He also took part in the Christine Mirzayan Science and Technology Policy Fellowship Program. He said, “A lot of policy is not informed by science or engineering. The National Academies play a vital role by offering expert, evidence-based recommendations on the most pressing issues of our time.”
Proposed cuts of this scale threaten ongoing studies and the capacity to generate actionable insights. They undermine reforms that depend on systematic data collection. Without institutional safeguards that embed evidence into policy design, progress remains vulnerable to ideological swings.
One way to bake evidence into government is by standing up units that turn pilots into policy in real time.
Policy Labs as Feedback Engines
Policy labs solve a specific problem: how do you get evidence from pilot projects into actual policy implementation? They design randomised trials, collect data, and feed evidence directly into decision-making cycles.
These government units share evidence, develop impact evaluations, and align learning with core priorities. They operate within existing ministries, which means they can actually influence outcomes rather than just writing reports that go unread.
Political resistance and budget constraints create real challenges. Yet policy labs like the White House’s and Britain’s Behavioural Insights Team navigate these by partnering with universities and NGOs. They’ve proven that embedding evaluation units within government shortens the feedback loop.
The same iterative mindset that drives high-level program improvements transforms outcomes on hospital wards, too.
Clinical Audits and the Practitioner’s Cycle
Healthcare systems constantly wrestle with ensuring clinical practices match best-practice standards. Patient outcomes depend on it. Clinical audits represent a systematic approach to reviewing care processes against established benchmarks.
Quality-improvement methods that combine data analysis with outcome measurement tackle this challenge across healthcare settings.
Amelia Denniss applies this approach in her work across New South Wales hospitals. She contributes to clinical audits and hospital standard of care guidelines – the kind of systematic work that identifies variations in practice and drives protocol improvements. Her publications in peer-reviewed journals and presentations at scientific meetings show how this evidence feeds back into practice. It’s not research for research’s sake. It’s structured feedback creating measurable improvements.
This is how practitioners embed new evidence into everyday workflows. They don’t treat research as separate from practice. They integrate it into daily decision-making.
Individual clinicians drive incremental gains through these methods. But national research institutions set the strategic agenda for tackling challenges at scale.
Research Ecosystems Driving Challenges
Aligning research funding with societal needs isn’t straightforward. Universities want academic freedom. Governments want practical solutions. Industry wants commercial applications.
Creating ecosystems that balance these competing demands while maximising impact requires careful orchestration. Integrated research strategies that combine funding mechanisms with policy objectives represent one approach to this challenge.
Doug Hilton’s CSIRO projects offer another take on how balanced research portfolios drive real-world impact. He directed the GenCost electricity modelling report, which assessed future generation costs across different technologies to inform policy discussions. He’s overseen industry trials for net-zero technologies, partnering with sector bodies to test solutions such as carbon capture pilots and grid-scale battery storage under real-world conditions.
Hilton advocates for balanced research-funding portfolios and National Science Priorities that direct investment toward areas where evidence can deliver significant returns. Pandemic response, biodiversity protection, and ageing research represent key focus areas. His emphasis on diversity in science and cross-sector collaborations ensures evidence systems draw on broad perspectives and avoid blind spots.
These principles work consistently across different settings. From boardrooms to government units, from clinics to research institutes, they underpin successful evidence-based decision-making.
Six Enablers of the Proof Revolution
Six enablers consistently appear in successful evidence-based organisations: data governance, iterative pilots, cross-functional teams, capacity building, cultural buy-in, and leadership sponsorship.
Data governance isn’t just bureaucracy – it’s clarity about who owns what, quality controls that actually work, and privacy safeguards people can trust. Merative’s patient-data protocols show this working in practice, as does CSIRO’s modelling transparency.
Iterative pilots start small and test assumptions. Policy labs run experimental programmes. Denniss conducts clinical audits. The scale differs, but they’re all testing before committing.
Cross-functional teams matter because insights die in isolation. You need analysts who can crunch numbers, subject-matter experts who understand context, and operational staff who’ll actually implement changes. Without this mix, brilliant analysis sits unused in spreadsheets.
The final two enablers work together. Capacity building means investing in training your people. Cultural buy-in requires leadership sponsorship that’s visible and consistent. CSIRO’s diversity initiatives demonstrate both approaches. So do hospitals that embed audit duties directly into job descriptions.
With these enablers in place, proof moves from a one-off project to a sustainable organisational capability.
Making Proof the Default
Building evidence into every organisational decision represents both the challenge and opportunity of our time. The payoff is real: data-driven policies work better, clinical audits save lives, and research priorities aligned with societal needs deliver measurable impact.
Organisations ignoring this trend risk falling behind. The choice isn’t whether to embrace evidence-based decision-making – it’s how quickly you can make it your default setting.
So ask yourself not if you’ll embrace proof – but how swiftly you’ll make it your reflex, before good intentions go unmeasured.
Amelia Denniss’s Solomon Islands experience reminds us that even the most well-intentioned efforts need structured feedback to truly improve lives. Without it, you’re operating without clear direction, as many decision-making processes still do.