Data disclosure regulations and the effect on social impact

Gilad Tanay at the ERI Institute describes how to navigate the rising seas of climate change data

 

In the race to turn the tide on climate change, the private sector has a colossal role to play. Some of the world’s biggest firms have the power to help reverse the decline of fragile ecosystems – or fuel their destruction. So, it’s encouraging to see companies committing to publish comprehensive data about their impact on the planet. 

 

But I’m starting to see a worrying trend. The drive for more data is stopping firms from interrogating the environmental data that will really turn the dial.

 

Just last month, a record number of companies committed to adopt the Taskforce on Nature-related Financial Disclosures’ (TNFD) guidelines on corporate sustainability reporting. The number of TNFD Adopters hit 500 for the first time.

 

The TNFD guidelines lay out best practice for disclosing climate and nature-based risks as well the metrics that companies should measure, monitor, and track. It’s a credit to the TNFD that it has managed to sign up financial institutions with a massive $17.7 trillion AuM.

 

This is a laudable achievement, and yet I am about to criticize it. You might ask, how can more data be a bad thing? In theory, it isn’t, especially for data scientists like us. In practice, the assumption that more data is always, by default, better, just doesn’t stand. 

 

There comes a point where we become so focused on collecting (and disclosing) as much raw data as possible, that we have lost sight of the ultimate problem that we were trying to solve in the first place.

 

Constantly collecting, collating, restructuring, rearranging, and representing data can give us an illusory sense of momentum on a problem, whereas we are just reinterrogating the problem repeatedly without changing the underlying facts.

 

Focusing on just collecting as much data as possible can distract us from the more important, intellectually demanding process of stepping back from the challenge and interrogating whether we are asking the right questions to begin with.

 

This requires a bit more explanation. In my experience, once you start looking at a big, intractable problem – whether it is climate change or global inequality – you will eventually find that there are perhaps two or three intervention points that might have an outsized impact on solving or, at the very least, making progress against this problem. In other words, if you have a limited budget, you should focus that resource on those critical points.

 

In ERI’s lingo, we say that every challenge has ‘pressure points’. These pressure points are the central nodes in the causal network spinning around the cardinal challenge that determine its size, intensity, and scale. If you tackle these pressure points, you can make some substantial, efficient headway on the core challenge. 

 

Take the TFND’s reporting requirements for example. Companies are required to track dozens of metrics. However, when it comes to loss of biodiversity, there are only a handful of major pressure points that account for the lion’s share of the problem, chief amongst them: land-use change. We would be much better served by a smart, precise and in-depth measurement for this one indicator than by a flood of superficial reports.

 

Now, do not get me wrong. I am not saying that it is easy to identify these core pressure points or that they are obvious and clear to see. In fact, it is very much the opposite. Identifying these pressure points can be a difficult, time-consuming, demanding, and intensive project – engaging extensive research, study, and analysis.

 

Which brings us back to data. Data is essential in at least two core ways: on one hand, it provides us with the raw grist to laser in on these pressure points. If this data is interrogated in the right way, it can provide us with the inputs to identify – or at least see the silhouette of – these core pressure points.

 

On the other hand, having identified these pressure points, we can, then, use data effectively to track whether we are managing to apply pressure on these nodes in a way that genuinely moves the dial on the underlying challenge. And, importantly, given that we have pinpointed very specific pressure points, this might not require us to track reams of data. It might just be three, two, or even one core important data point that we need to track.

 

And that gets us to the final reason why mass data collection worries me: it is an abdication of responsibility. It’s an abdication of our responsibility to identify the things that really matter for solving an important problem – the pressure points. And it’s an abdication of our responsibility to laser in on the data that really matters.

 

Rather than go through a process of truly understanding a problem at its foundational level to pinpoint the data that matters, we throw up our arms in resignation and just say that we will collect it all. Ultimately, asking companies and governments to collect and report ever-more data may be taking us further away – rather than closer to – solving our important problems.

 

The upshot is that I fear we may wake up in a few years’ time struggling to stay afloat in ever larger data lakes – while our problems have only got bigger. If only we had focused on the data that really mattered rather than collecting it all, we may have made quicker progress. Hopefully this will happen soon. 

 

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Gilad Tanay (LinkedIn) is founder & chairperson of ERI Institute -- a research firm specialising in social impact

 

Main image courtesy of iStockPhoto.com and da-kuk