The Data Deflection: Unpacking The Myth Behind Corporate Inaction
- Emma Latham-Jones,
Nature & Climate Lead, Verdentum
With New York Climate Week wrapped up, and as we all rush to prepare for November’s COP in Baku, this cartoon feels depressingly relevant when I discuss the environmental crisis with certain corporate stakeholders. After years of engaging with them, I’ve realised that the supposed dichotomy of climate activists versus denialists is misleading. The latter group often isn’t loud or crass; they’re polite, well-dressed, often charming. Yet, whether dressed up or dressed down, their rhetoric has equally concerning implications for us all.
I’m going to divide these stakeholders into categories, and expose the one group that I find most absurd – akin to the father in the cartoon.
First up, we have outright denialists. Outspoken climate-deniers don’t typically land roles like "Head of ESG" or "Stewardship Lead." If nothing else, it would damage their street cred. I don’t, therefore, often have the pleasure of conversing with them. Then there are the natural variability advocates, conspiracy theorists, and ideological denialists. Am I going to wade through their arguments here? No. A simple Google search will do the job. Next, there’s the New Yorker cartoon crowd. These are the people who understand the problem but operate as if their bonuses or the short-term ROI are worth more than the planet’s future. This worldview requires a level of recklessness that makes me wonder if hard drugs are next on their to-do lists. But here’s where my real frustration lies – the final category: the
we’d-love-to-do-something-but-we-don’t-have-the-data crowd.
Not enough data? Hold that thought.
Amazon doesn’t need me to announce my obsession with gardening – they already know. They recommend the perfect secateurs to go with my slip-free waterproof gardening gloves. Netflix hasn’t exposed my guilty pleasure for trashy US TV shows to my peers, yet they tailor my recommendations perfectly. But when it comes to companies assessing their environmental impact, somehow, they claim a lack of data?
Let’s not just focus on the tech giants and online platforms. What about good old-fashioned in-person retail shopping? It’s no great surprise that another American company is a wizard at supply chain optimisation: Walmart processes terabytes of data daily to predict product demand and optimise inventory. But it’s not just the Americans. Have you ever shopped at Zara? If you haven’t made the switch to second-hand shopping yet, when you go to the cashier to pay for your three-quarter length dark denim jeans with their carefully located rips, Zara is using your sales data in real-time to inform its production. They are able to pivot production within weeks based on what’s selling, allowing for rapid response to fashion trends. Wow. If these companies can perform data miracles, how can anyone claim they lack the tools to assess their environmental impacts?
Okay, I hear you. Not all companies are data-powerhouses like Amazon or even Zara. What about other organisations?
Challenge accepted. I’m going to run through examples as diverse as football teams and city planners, to the military to get across a simple point: there’s more than enough data.
Liverpool FC (yes, the best football team this country has ever seen – Nottingham Forest, who?) uses SciSports to track player movements and optimise strategies. And it works, well, most of the time (I’m choosing to ignore Callum Hudson-Odoi’s recent fluke!). Data helped head coach Jürgen Klopp make decisions on player positioning, substitutions, and game strategy, a key part of their 2019 Champions League victory. Not a football fan? The NBA uses data to enhance performance too. I’m making it sound like a drug now. Every NBA arena is equipped with cameras that track players’ movements on the court. Sounds addictive. The data is analysed to optimise strategies, predict player fatigue, and improve team performance.
City planners? Singapore loves a sensor. They track traffic to reduce congestion and emissions. But what about closer to home? Step aside Singapore, Barcelona raises you: sensors in its water pipes! These sensors detect leaks, saving millions of litres annually. IBM Watson once diagnosed a rare leukaemia in minutes, using vast amounts of medical data that had stumped doctors for months. And the military? DARPA’s Project Maven uses AI to process drone footage, identifying targets in real time.
I used to work for the UK’s Met Office, and I can tell you this: ‘We’d love to, but we don’t have enough data’ were words never uttered. If you’d have come up with a response like this, you’d have been laughed out of the room. It’s not just the Met Office that crunches huge quantities of data. Every single meteorological organisation does. The European Centre for Medium-Range Weather Forecasts (ECMWF) processes over 100 terabytes of data every day. Honestly, I can’t begin to get my head around 100 terabytes. If you can, I salute you. Where on earth does this data come from? It’s collected from satellites, weather stations, and ocean buoys. This data feeds into models that predict weather with high accuracy, up to two weeks in advance, helping governments prepare for extreme weather events.
Now, I do sympathise with those who argue that reporting against frameworks like TNFD, TCFD, ISSB, and SASB presents challenges. Nature-related data, for example – particularly in biodiversity – is often fragmented or inconsistent. Collecting data across global supply chains can be complex, especially for smaller businesses. And let’s face it, standardisation varies greatly across regions. But none of these obstacles are insurmountable.
Data science methods like data augmentation can fill in the gaps by generating synthetic data where direct measurements are lacking. Primary data collection methods like satellite imagery, IoT sensors, and supply chain audits are improving every day. Take, for instance, John Deere’s tractors, which use sensors and GPS to monitor soil conditions, or The Climate Corporation, which provides farmers with data-driven insights to decide when to plant, irrigate, and harvest. Businesses can also collaborate with platforms like Global Forest Watch or The Nature Conservancy for granular environmental data. So, to those in the we-don’t-have-enough-data crowd: your tools are out there. The problem isn’t the data – it’s the will to act.
Data science methods like data augmentation can fill in the gaps by generating synthetic data where direct measurements are lacking. Primary data collection methods like satellite imagery, IoT sensors, and supply chain audits are improving every day. Take, for instance, John Deere’s tractors, which use sensors and GPS to monitor soil conditions, or The Climate Corporation, which provides farmers with data-driven insights to decide when to plant, irrigate, and harvest. Businesses can also collaborate with platforms like Global Forest Watch or The Nature Conservancy for granular environmental data. So, to those in the we-don’t-have-enough-data crowd: your tools are out there. The problem isn’t the data – it’s the will to act.
Let’s take a quick look at (a fraction of) the data financial institutions have at their disposal. Financial market trading is a goldmine, in the literal and metaphorical sense, of data. That’s why Goldman Sachs is able to process millions of trades in milliseconds. Read that again. Yes, corporations are able to process millions of trades in milliseconds. How does Goldman do it? The same way other corporations do it: with machine learning. Machine learning models can optimise high-frequency trading decisions, based on historical data, market conditions, and social media sentiment analysis.
Goldman Sachs aren’t the only ones. Lots of financial institutions do this. The hedge fund Renaissance Technologies (Medallion Fund) employs data-driven trading strategies, using complex algorithms and massive data sets to predict market trends. Their proprietary algorithms analyse historical and real-time data, leading to an average annual return of over 60% from 1988 to 2018. And there I was, being satisfied with a meagre 5% return on my investments... Rapidly checks Google for eligibility for an investment with Medallion Fund.
If they can do that, how can financial institutions plausibly say there’s ‘not enough data’ to measure their nature and climate impacts?
Yet, I constantly hear this ‘lack of data’ excuse from stakeholders, particularly in financial institutions. To be clear, not all financial institutions are closet climate deniers. Many professionals work long hours to shake things up, whether in small progressive funds or within colossal multinationals. But let’s be honest: financial institutions already possess a data goldmine. Their ability to assess climate risks is not hindered by a lack of information, but rather by how they choose to use it.
Let me bring this back to what I know best. I work day-in-day-out with data scientists and chemical engineers – professional nerds, if you will. These people are smart. They know how to leverage data to help companies and organisations monitor and mitigate their environmental impact on a host of sustainable development goals. But even if they weren’t smart, at this point it’s not rocket science.
In my interim role, I currently lead the nature and climate efforts of a New York-based organisation called Verdentum. Verdentum assesses impact and dependencies on nature and climate by tracking deforestation using satellite imagery, analysing carbon emissions across supply chains, and much more. We enable companies and financial organisations to use data to assess biodiversity, manage carbon sequestration projects, and collaborate with Indigenous Peoples and Local Communities. And Verdentum isn’t alone: platforms like Global Forest Watch and The Nature Conservancy’s Resilience Atlas offer a treasure trove of environmental data.
In fact, there are so many organisations offering data services to assess your impacts and dependencies on nature and climate, that if I were to list them all, I’d be here all day. You’re spoilt for choice.
So, to the we-don’t-have-enough-data crowd: it’s time to admit that the problem isn’t the data – it’s the will to act.
Full disclosure, I don’t hold any equity in Verdentum and I don’t make a commission - whether you use the platform or not makes no material difference to me. But it does matter if you’re still claiming there’s not enough data. As a human being, I have a personal stake in the health of this planet.
Are you still not convinced or want to learn more? Verdentum is hosting a series of workshops on how to use data to assess impacts and dependencies in supply chains, helping companies meet reporting requirements. Come and join the conversation. I’m on emma@verdentum.org.