What your grandmother knew that we've forgotten
The difference between data and rigour
Data is not rigour. Knowing this is important given we are all drowning in unfiltered data and information. The simple days are over…
Times have changed.
Your grandmother probably got her health advice from one doctor, her news from one newspaper, and her weather forecast from looking at the sky. If three sources agreed on something, it was a given.
Today, you can find 50,000 studies on coffee's health effects and get a Chatbot to summarise them in under 30 seconds. Half will tell you coffee is killing you, half will claim it's the elixir of life.
Meanwhile, your weather app pulls from seventeen different models, your news feed shows you 847 stories before breakfast, and your fitness tracker generates more data about your body than NASA collected about the moon in the 1960s.
We've gone from information scarcity to information overload in a generation.
What has happened in this deluge is that we've confused having data with understanding it. We mistake the ability to Google or AI everything for the wisdom to interpret the results (for the record, AI is good but, for the moment, not that good). Because we can access thousands of studies, we automatically believe we are more informed than previous generations.
But here's what your grandmother understood intuitively. She and Auntie Maud knew that the source matters more than the statistic.
She knew her doctor had spent years learning to interpret symptoms. She trusted her newspaper because she knew its reputation down the pub. She read the sky because she'd watched its patterns for decades. And she knew that when Auntie Maud said “Our Harriet is on the sauce again,” it was gospel.
And it used to be that in those ancient times of information trickle, if someone claimed, "Studies show," they'd better have had a compelling study to back it up. Today, anyone can cherry-pick from millions of data points to support virtually any position.
Climate deniers have data. Anti-vaxxers have data. Flat-earthers have data. They just have to leave out the bits that don’t fit their agenda.
So, how do we navigate this flood?
How do we transform endless streams of numbers into reliable knowledge we can use to make decisions?
The answer lies in understanding a crucial distinction—data is what you have, rigour is what you do with it.
"...what we learn from professionals in the real world is that data is not necessarily rigour."
Nassim Nicholas Taleb, ‘Skin in the Game: Hidden Asymmetries in Daily Life’
But first, a short primer.
The DIKW hierarchy
Data → Information → Knowledge → Wisdom
In a world overflowing with data but starving for wisdom, the DIKW hierarchy offers a powerful lens for understanding how raw facts are transformed into meaningful insight and responsible action.
Data are the raw, unprocessed facts and figures collected from observation or measurement. Think of them as signals without context or meaning. For instance, recording on a thermometer 64.3°C is data; it's a solitary point that has not yet been situated within a framework. Data is foundational but inert, requiring processing before it becomes useful.
Information emerges when data is organised, contextualised, or structured to convey meaning. When you state that "the internal temperature of the roast chicken just out of the oven is 64.3°C and the safe temperature is 74°C", you are transforming data into information. Information answers questions like who, what, where, and when, and serves as the groundwork for deeper interpretation or action.
Knowledge builds upon information by adding understanding, patterns, and mental models formed through interpretation, analysis, and often experience. It represents a meaningful synthesis of information that allows someone to comprehend how or why something happens. For example, understanding how temperature increases affect weather patterns and agricultural cycles reflects more than just knowing the data or observing a trend; it's imagining the underlying mechanisms at work.
Unlike information, which can be descriptive and static, knowledge is relational and often applied. It includes not only facts but also the ability to connect those facts in coherent frameworks. Knowledge may be explicit, such as documented theories and formulas, or tacit, residing in personal expertise and skills that are difficult to formalise but critical in real-world application. It enables someone to not just recognise that something is happening, but to explain, predict, or influence outcomes.
In the broader DIKW hierarchy (Data → Information → Knowledge → Wisdom), knowledge is the stage where insight begins to form and action becomes more strategic.
Wisdom goes beyond the accumulation of facts and theories as the capacity to make sound judgments by integrating knowledge, experience, ethics, and long-term perspective. A wise interpretation might involve understanding that climate change is happening (evidence) and recognising how best to respond to it in a way that balances ecological, social, and economic factors over time.
And one last term that sometimes comes up.
Evidence is a subset of information that supports or refutes a claim, hypothesis, or belief. It carries inferential weight and is typically evaluated for quality, relevance, and reliability. For example, if rising average temperatures over several decades support the climate change hypothesis, then that temperature data, now structured, validated, and correlated with models, constitutes scientific evidence.
Back in the 1970s
When I was at school, there were no personal computers, the Internet, or Google, yet I somehow figured out what was happening.
Back then, if the teacher said it, the preacher confirmed it, and the library had a book about it, we generally accepted it as true. Today, with infinite information at our fingertips, how do we know what to believe?
The 1970s in the UK were iconic for their mix of economic turmoil, political unrest, and radical cultural change. The country faced high inflation, frequent strikes, and widespread dissatisfaction, culminating in the "Winter of Discontent" and paving the way for Margaret Thatcher's rise. Amid this instability, a wave of creativity took hold. Punk rock exploded with bands like the Sex Pistols and The Clash, expressing anger and defiance through raw sound and rebellious fashion.
Of course, I was too young and sheltered for punk. But I managed bold patterns, flared trousers, and platform shoes, and watched TV shows like Monty Python push social and comedic boundaries.
All my information came from the preacher, the teacher and the library, where knowledge was written and printed into substantial volumes.
Then there was the occasional newspaper and the television news that reported current events. I remember my father watching the latter religiously at 9 p.m. every weekday.
Data was few and far between, present only in the world of nerds. We didn’t study statistics or the scientific method in school, just content.
It all felt much simpler back then.
Today is the information age; you can find almost anything with a few clicks.
Streams of numbers are available on nearly anything that can be measured. There is growing concern about the carbon emissions associated with the energy consumption of data centres (often called server farms), which are essential for powering the internet, cloud computing, and artificial intelligence. Estimates suggest that data centres contribute approximately 1–2% of global electricity consumption, and their share of global carbon emissions is often cited in the 1–2% range. However, this varies depending on factors such as energy sources and regional practices.
And this is only the beginning.
Here are a few more things...
Humanity generates over 328 million terabytes of data daily, the digital equivalent of 100 million HD movies. We love generating and storing data and will go to great lengths to keep it.
Meta's Q1 2024 financial report confirms that Facebook had 3.05 billion monthly active users as of March 2024, making it the most-used social media platform globally.
Satellites that take images across a dozen wavelengths at 10 m resolution pass over your head every five days.
Online health records of all the people in a country, millions of them, provide a mine of information on the prevalence and geographic distribution of diseases and ailments.
Billions of online purchasing records create information on people's rational and not-so-rational behaviour in markets.
All this big data has many uses, some good, some bad, and a few ugly.
Retailers expect that all this big data will tell them enough about us to stop their slide out of business. They should be worried about new players who will use the data to tailor products and services to their customers' needs, not the retailers'.
There is data everywhere.
In my field of ecology, the data types, availability, and utility are unimaginable from when I went to university in 1979, when we were still using punch cards in readers connected to a mainframe computer. Thanks to the satellites, drones, and sensors connected to the internet and the data centres that can store colossal volumes of information, we now have more details about the natural world and what humans do to it than ever.
I can easily access numbers on farming systems, yields, water use, fertiliser application, and the future for business resilience and reliability over time. The ecology of production systems and landscapes can be understood at scales of time and space that ecologists of previous generations could only dream about.
Anyone can do it. Just glancing at the satellite image of NSW below is a boon for those who can make an ecological reading.
Again, the data possibilities are endless.
So, how do we transform this flood of data into reliable knowledge?
The answer lies in understanding the difference between having and using information rigorously. Let me show you what this looks like in practice by using an example of how big data has made a massive difference to our level of knowledge, planetary boundaries.
Planetary Boundaries Rigour
Imagine trying to assess the health of a patient who weighs 6 trillion tonnes, breathes through countless forests, and has a circulatory system spanning six continents. Scientists attempt this when monitoring Earth's planetary boundaries—nine critical thresholds determining whether our planet remains stable and resilient.
The beauty of the planetary boundaries approach lies in its practical specificity. Rather than vague assertions about environmental health, there are quantifiable indicators for each critical Earth system and vast amounts of data for each indicator, simplified as follows…
The data for these indicators comes from an impressive integration of technologies and institutions.
Satellite networks continuously monitor everything from forest cover to ocean colour. Ground-based stations like Hawaii's Mauna Loa Observatory have tracked atmospheric CO₂ since 1958. Oceanographic monitoring networks measure pH changes across our acidifying seas.
But no single data source tells the complete story.
The planetary boundaries assessment requires synthesising information from NASA satellites, IUCN species databases, FAO agricultural statistics, and dozens of other sources. Truth emerges from multiple lines of evidence, not single studies.
Here is what it looks like…
The evolution of the planetary boundaries framework. Licenced under CC BY-NC-ND 3.0 (Credit: Azote for Stockholm Resilience Centre, Stockholm University. Based on Richardson et al. 2023, Steffen et al. 2015, and Rockström et al. 2009)
In 2023, an international team of scientists conducted the most comprehensive assessment of the planetary boundaries framework to date, revealing that six out of nine critical Earth system boundaries have been breached. These transgressed boundaries include climate change, biosphere integrity, land-system change, freshwater use, biogeochemical flows (nitrogen and phosphorus cycles), and novel entities (such as chemical pollution).
Human activities push Earth's systems beyond their safe operating limits, increasing the risk of triggering irreversible environmental changes. While some boundaries, like stratospheric ozone, have shown signs of recovery, the overall trend indicates a pressing need for systemic global action to restore planetary stability and resilience.
The planetary boundaries analysis is grounded in a rigorous, multidisciplinary approach that combines Earth system science, environmental monitoring, and complex modelling to identify and quantify thresholds within which humanity can safely operate. Each boundary—such as climate change, biosphere integrity, and biochemical flows—is defined through scientifically peer-reviewed indicators based on globally aggregated datasets, often spanning decades, and validated through multiple modelling approaches, including system dynamics, earth observation data, and paleoecological reconstructions. The framework relies on control variables with defined "safe" zones, informed by extensive empirical research and expert consensus, and is periodically updated as new data and methodologies emerge. This robustness ensures that while uncertainty is inherent in each threshold, the boundaries reflect the best available synthesis of planetary-scale environmental science.
The planetary boundaries framework does not claim absolute precision; it offers scientifically robust estimates with openly acknowledged uncertainties. It is peer-reviewed, internationally coordinated, and continuously refined. Annual "planetary health checks," now supported by institutions such as the Potsdam Institute for Climate Impact Research, ensure the framework evolves with emerging knowledge.
More than a set of metrics, the framework embodies a powerful way to approach complex global challenges. It balances intellectual humility by recognising what is uncertain, with practical ambition, by offering measurable targets for action. Neither alarmist nor complacent, it is grounded in evidence and designed to adapt.
Crucially, the planetary boundaries approach demonstrates that even seemingly intractable measurement challenges can be addressed through international cooperation, technological innovation, and rigorous scientific methodology. It provides a snapshot of planetary health and a model for how humanity can responsibly navigate an era of unprecedented environmental change.
What does all this data mean?
So what? We have access to loads of data, the cynic might say. And fair enough, as Nassim Taleb reminds us, having data, even information, is not enough, not even if it is vast amounts of it.
What you do with it matters.
You use the data to generate information that can be used to understand and bound likelihoods. This is the key difference between data and rigour that Nassim Taleb outlines.
Rigour is about being thorough, methodical, and careful when working with information, especially in data and analysis contexts. It involves a disciplined approach to understanding where the data comes from, how it was collected, what variables it includes, and any limitations or uncertainties it may contain.
This means scrutinising the data’s source, structure, completeness, consistency, and relevance to the problem. Rigour also requires attention to the assumptions built into the data and the analytical methods chosen.
By understanding the data’s origins and characteristics in detail—its provenance, granularity, accuracy, and possible biases—analysts are better equipped to select appropriate models or statistical techniques.
Rigour helps guard against spurious conclusions by ensuring that the data supports the inferences being drawn. It prompts critical questions: Is this data suitable for answering this question? Are the analytical tools being used consistent with the nature and limits of the dataset?
Rigour provides a foundation for making valid, transparent, and reproducible conclusions. When rigorous methods are applied, any predictions or inferences can be accompanied by a clear statement of their probability or level of confidence. This not only strengthens the credibility of the analysis but also ensures that decision-makers understand the risks and uncertainties involved. This is essential for evidence-based thinking in both science and policy.
Rigour is asking the hard questions about data. Where did it come from? How was it collected? What are its limitations? Who benefits from this interpretation? These questions transform raw numbers into reliable knowledge.
All this is jargon for ‘no bullshit’ and no ‘cherry-picking’ the good bits.
In other words, having vast data banks is not even the start, for alone, data is meaningless.
Data must be treated very carefully.
A few synonyms for rigour might make it clearer…
meticulousness, thoroughness, carefulness, attention to detail, diligence, scrupulousness, exactness, exactitude, precision, accuracy, correctness, strictness, punctiliousness, conscientiousness
Why data rigour matters
Rigour in handling data and information involves careful collection, transparent methodology, accurate interpretation, and critical evaluation of sources and assumptions. This is fundamental to producing results that are reproducible, meaningful, and resilient to bias.
Without such rigour, even large volumes of data can lead to misleading conclusions, false correlations, or policy missteps, especially when uncertainty is ignored or models are not properly validated.
In scientific, environmental, and social contexts alike, rigorous data practices ensure that information serves not as noise or manipulation, but as a reliable foundation for action. For instance, climate models that predict future warming are trusted not because they are perfect, but because they are grounded in scrupulous data handling, peer review, and iterative refinement. This allows policymakers to weigh risks, benefits, and trade-offs with greater confidence.
Moreover, in an era of misinformation and rapid digital dissemination, rigour becomes a safeguard against distortion and oversimplification. It fosters transparency and accountability, enabling citizens, researchers, and leaders alike to critically assess claims and collaborate on solutions based on shared, trustworthy evidence rather than ideology or anecdote.
Just to say it again… Data is not rigour.
Data on its own is meaningless. It needs context, attribute description, and interpretation of trends and patterns, all completed with great care and diligence. This is the domain of statistics, mathematics, and the philosophical aspects of the scientific method.
But anyone can learn the difference between data and rigour with simple rules of thumb. Here are some core rules of thumb for each stage of the DIKW hierarchy and for applying rigour throughout:
Data
If it has no context, it’s just noise.
Raw numbers, symbols, or facts alone don't mean much until we ask where, when, and how they were collected. Always question the source, precision, and collection method before treating data as reliable.
Information
Context turns data into meaning.
When data is organised or grouped in a way that answers a fundamental question (like what happened, where, or when), it becomes information. Be wary of patterns that appear meaningful but aren't statistically significant, and always check for scale, units, and trends.
Knowledge
Understanding is built from patterns and connections.
Knowledge arises when information is interpreted with experience or models that explain why or how. Ask: Does this conclusion hold in different contexts? What are the underlying assumptions? Is there a causal link or just correlation?
Evidence
Claimed facts require support and scrutiny.
Not all information is evidence. For something to count as evidence, it must be relevant, reliable, and verifiable. Always ask: what is this being used to prove? Is it repeatable? What’s the quality of the study or source?
Wisdom
Long-term value and ethical judgment define wise choices.
Wisdom goes beyond knowing what works because it asks what should be done. Consider broader impacts, unintended consequences, and historical lessons. Wisdom often recognises limits, trade-offs, and the humility of not knowing.
One more thing
Rigour is sometimes mischaracterised as authoritarian or overly technocratic, but when properly practised, it is the opposite.
Proper rigour in data, evidence, and reasoning involves transparency, reproducibility, and a commitment to questioning assumptions. These are qualities that strengthen democratic processes rather than threaten them. Rigour protects individuals and societies from arbitrary rule, ideological distortion, and misinformation by subjecting claims, policies, and decisions to scrutiny. It enables citizens to demand evidence for authority, not just accept it, reinforcing civic freedoms and informed consent.
Moreover, effective decision-making in public health, environmental policy, education, or justice depends on clear, well-substantiated reasoning that can withstand public debate and independent review. Without rigour, decisions risk being driven by bias, short-term gain, or populist pressure, ultimately undermining trust and long-term societal resilience.
Rigour ensures that choices are efficient and equitable because they are based on shared standards of proof and responsibility.
Far from threatening liberty, rigour is a safeguard for it. It upholds the conditions under which pluralism, dialogue, and diverse perspectives can flourish—so long as it is used ethically, with humility, and with an awareness of its limits. When rigour is inclusive, communicative, and grounded in public interest, it becomes a cornerstone of personal autonomy and collective wisdom.
More big benefits of rigour
Rigour is what transforms creativity into credible problem-solving.
Whether the issue is climate change, community health, or a personal decision about finance or wellbeing, the capacity to generate and test solutions depends on a systematic and disciplined approach.
Rigour ensures that potential responses are not just imaginative but also feasible, measurable, and grounded in real-world data. It brings clarity to complexity, allowing competing options to be assessed on their merits rather than on intuition, ideology, or guesswork.
At the global level, rigour enables scientific consensus and international collaboration, without which responses to planetary crises like biodiversity loss, pandemics, or energy transitions would lack coordination or coherence.
At the local level, it helps policymakers, educators, and community groups tailor interventions to specific needs, using pilot programs, feedback loops, and impact assessments to refine what works.
Even in personal life, rigour allows individuals to think critically about habits, choices, and values, relying on self-awareness, evidence, and reflective practice rather than impulse.
Crucially, rigour liberates innovation. By offering structured ways to test hypotheses, assess risks, and learn from failure, rigorous methods allow bold experimentation while minimising harm. It keeps the door open to multiple possible futures while protecting against costly errors and false promises.
In short, rigour is not a barrier to creative problem-solving; it makes creativity matter.
The Wisdom of Sufficient Evidence
Rigour, in its truest sense, isn't about drowning in endless data points, pursuing methodological perfection, or even trying to climb the DIKW pyramid. It is about applying systematic thinking with intellectual honesty, core mindful sceptic skills.
A mindful sceptic understands that rigour means asking the right questions at the right time, not questioning everything equally.
A mindful sceptic also recognises that our complex, interconnected world often requires decisions based on incomplete information. Tempted as we might be to abandon standards or accept sloppy thinking, we should practise rigour to develop the wisdom to know when we have sufficient evidence to act and when further investigation is warranted.
But here is the thing.
Values, context, and practical constraints shape how evidence translates into real-world solutions. A rigorous approach doesn't just evaluate the quality of data; it considers the appropriateness of the question being asked, the relevance of the methodology to actual decision-making needs, and the potential consequences of both action and inaction.
This is where rigour transcends mere academic exercise and becomes a practical tool for navigating complexity with intellectual integrity and human wisdom.
Just ask Aunt Maude.
Mindful Momentum
The Grandmother Test
This Week's Challenge is to explain one complex topic you care about using only the information sources your grandmother would have trusted.
Choose an environmental or social issue that matters to you. Now imagine explaining it to someone who gets their news from one newspaper, one TV channel, and trusted local experts.
What would you actually be able to prove? What assumptions would you have to admit you're making?
The twist…
This isn't about dumbing things down because you still need to convince. Instead, it's about identifying which parts of your understanding rest on solid ground versus trendy talking points.
Often, the core truths are simpler and more powerful than the complex narratives we've constructed around them.
Key Points
Modern society confuses data abundance with genuine understanding, creating information paralysis rather than clarity. While previous generations relied on fewer but more trusted sources—like a doctor's advice or a reputable newspaper—today's endless streams of statistics and studies often overwhelm rather than inform, with data centres now consuming significant global electricity just to store our information excess.
The Data-Information-Knowledge-Wisdom hierarchy reveals how raw numbers transform into actionable understanding through rigorous interpretation. True rigour involves systematic scrutiny of sources, methodology, and limitations, ensuring that statistics become reliable foundations for decisions rather than isolated, potentially misleading fragments floating in the digital noise.
Planetary boundaries research exemplifies rigorous data application in practice, demonstrating how scientists synthesise satellite observations, ground measurements, and multiple databases to assess Earth's critical thresholds. This framework shows that even seemingly intractable global challenges become manageable through international cooperation, technological innovation, and methodical approaches that acknowledge uncertainties while providing clear, actionable insights.
Effective rigour focuses on practical wisdom rather than perfectionism, developing the judgement to know when sufficient evidence exists for confident action. Mindful scepticism means applying critical thinking strategically—cutting through information overload by focusing analytical energy where it generates the most beneficial outcomes, whether for personal decisions or collective challenges facing humanity's future.
Curiosity Corner
This issue of the newsletter is all about…
Your grandmother knew something we've forgotten. The source matters more than the statistic, and it’s rigorous thinking, not endless data, that transforms numbers into reliable knowledge.
5 Better Questions from this issue of the newsletter…
Where did this number come from, and who benefits from me believing it? This question cuts through the noise by forcing you to trace data back to its source and consider potential motivations, essential skills when navigating an information landscape where even data centres contribute to carbon emissions.
What context am I missing that would change how I interpret this information? This moves beyond accepting isolated facts to understanding systems and relationships, helping you avoid the trap of mistaking correlation for causation or treating a single data point as definitive truth.
Am I confusing having access to information with actually understanding it? This question strikes at the heart of our modern predicament. Just because you can Google 50,000 coffee studies doesn't mean you're more informed than your grandmother, who trusted her doctor's advice.
What would I need to see to change my mind about this claim? Establishing clear criteria for evidence evaluation embodies intellectual humility. It prevents you from cherry-picking data that confirms existing beliefs while remaining genuinely open to new information.
Is this question worth the energy it would take to answer rigorously? This practical wisdom question helps you focus your sceptical faculties where they matter most, recognising that mindful scepticism isn't about questioning everything equally but about knowing when rigorous analysis will genuinely improve your decisions.
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Thank you for being part of a community that values intellectual honesty over comfortable narratives.
Whether you agree or find yourself productively disagreeing, your reading and comments help build the evidence-based discourse our environmental challenges demand.
A coffee contribution helps keep this rigorous analysis flowing.