Sustainability acceleration: Technology acumen should be the domain of everyone

Sustainability needs to be a data problem (so we can give it equal consideration in decision-making)

‘Sustainability’ is way more than a data problem, but within the context of business decision-making frameworks, it needs to be a data problem. Risk, accounting/finance, procurement, design, HR, IT, operations and overall strategy need decision-grade data (or knowledge that is derived from data) so that the specialists working in those functions can include ‘sustainability’ alongside all their other traditional business metrics – confidently, accurately and timely.

There seems to be quite a few studies and research published this year from media outlets such as FT & EDIE that suggest that whilst majority of large organisations have sustainability targets, many less have credible transition plans. Which means organisations don't know how they are going to get there. Which means they either don’t have the data, or haven’t organised the data so that it can help drive the decision-making that goes beyond the ‘low hanging fruit’.

As data (and digital technology to manage that data) will be a huge driver, progress will be accelerated if more people developed more knowledge on these topics to move away as quickly as possible from collecting and reporting to getting it into decision-making.

Knowing more here could also stop unintended greenwashing - as more people who understand data, the methodologies used to calculate data, do some modelling and derive meaning – the less errors I see occurring by using the wrong data, or building the wrong solution….

Let’s actually break down ‘it’s a data problem’:

I’d expect most of you have heard this before, but just take a moment to stop and think what it means to bring ‘sustainability’ into decision-making?- the magnitude and breadth and depth of it all….

Sustainability is a colossal word, not just in terms of all the many subcategories that sit within it from a framework perspective, but just what is means for how we think about the place of business in the world and what that means to us as individuals, communities, societies and economies. It is hugely complex, not binary, not linear, it represents a system.

When you granulate what Sustainability both represents and how to categorise it that’s both a lot of data and a lot of different things you need to do with that data (in a multi-dimensional way).

For example:

• Nothing within the E, S or G of sustainability is the same – which equates to different types, quality and sources of data – the data you need is multimodal
• What needs to be disclosed and how you also analyse your impacts and dependencies (think CSRD and double materiality) is vast. That will require digital technology to collect, cleanse, calculate (there are many types of calculations you need to run), automate, model and visualise results in a digestible way
• Once you have that data, it needs to be ready for different use-cases – disclosures are different from a democratised LCA tool or include in your risk calculations – so you will need different business applications to create or adapt business-ready tools/apps/dashboards to include sustainability data in whatever decision-making interfaces your workforce use.
• For many types of decision-making you will need to model the data to create find patterns, predictions, scenarios as a system to truly understand impacts, dependencies and trade-offs.

So what can you do: PART 1) My story, if that helps…

For about 80% of my Microsoft career I said ‘But I am not technical’ in many conversations. What I actually meant was that in the ocean of skilled and brilliant technology professionals (and everyone company has a thriving tech/IT division) I always felt like I didn’t have the right to say I was one of them.. I was hired in the first instance as a business advisor who could translate the enabling function of technology to solve business problems…. I felt intimidated by what I thought I did not know. But that was all my own perception – I actually played an very important role with our customers: that to facilitate progress, being the bridge between the technical consultants/architects, data scientists and developers and what a business person needed to know to achieve their objectives.

Over the years I’ve gone up the scale (you would hope ??) and what I now know about data strategy and digital technology is something I think everyone should have a little more of to accelerate progress against sustainability ambitions.

But it’s not that you’re a 1-3 OR a 10, you can be somewhere in between and that is highly achievable.

So what can you do: PART 2) What would being a 3-7 actually mean:

First things first, this scale is purely illustrative. I haven’t spent time working out what a 3 vs a 4 is, but hopefully the point is clear.

As insinuated in part 1, digital technology & data as a topic does get incredibly technical – loads of jargon, complexity in how you execute infrastructure, platforms, software and applications to achieve any IT/Tech/Data ambition.?

But do not let that stop you.

You don’t have to know it all, you just have to know enough to have an informed discussion with your technology/IT/data colleagues and vendors who can do all the really technical work – OR you need to know enough that once the business-ready software/tools/apps/dashboards are in place you can use them meaningfully to achieve your tasks and derive decisions.

To keep things simple, I think for me there are two things:

1. The data
2. What you need to do with that data i.e. the diagram: get, cleanse, store, transport/track, calculate/model, analyse, present - and that would require different systems/tools/apps to facilitate any given use-case from disclosures to purchasing carbon offsets to modelling nature to assigning value to your results for business decision-making

So what can you do: PART 3) Learning ideas and questions to ponder…

1. Talk to your own tech teams - they know loads and might have some easily digestible material already created
2. Interactive dashboards: as the maturity builds to get insights from complex/multidisciplinary data representing a system, interactive dashboards will be highly valuable. More traditional charts are likely to simple to use for multi-factor insights. You don’t have to build them, you just know how to drive them.
3. Principles of data strategy: If you are more hands-on/operational get going with this. There are so many courses (and detailed books i.e. Data Strategy for Dummies), but I found this one really accessible: The building blocks of data strategy (linkedin.com) . You can go deeper on any part of the diagram or anything you uncover as you need to
4. ?Introduction to machine learning and artificial intelligence (machine learning is the overall term): Given the power of AI to solve, likely an intro course here would be helpful, and from there you could go deeper into deep learning, generative AI as you so wish…

Equally here are some things you could think about:

1. What is the quality of the data and how much data do we need to make a decision (as this will vary)
2. How many different use-cases i.e. disclosures vs procurement decisions do we need this data for
3. How do we collect data we don’t have in the business and create a methodology to collect, model and draw insight from
4. What level of automation do we need (at any stage on the diagram) to to move resources from collection to action
5. Where am I getting this data from primary and secondary and what is the mix we need now, and going forward
6. How accurate does it need to be (and you can define accuracy, this is linked to point 2)
7. At what level shall should we run our calculations – by each row of data or at the aggregate
8. What methodology will we use per sustainability category and how can we get that into my system or tool
9. What ‘tools’ and ‘dashboards’ do we need (and what do our colleagues need to know) to democratise decision-making
10. Do we need a new system/tool or can we expand an existing system/tool within the business
11. If we procure a tool, can that tool ingest and extract data into other systems (i.e via APIs) so any data collected/calculated here can be used for a different use-case
12. we need to share this data up and down my value chain, what’s the right level of security and how do we ease friction of sharing data (so that might be an approach, or a system)
13. What is the provenance of that data (linked to point 11)
14. How do we maintain fidelity of data (accuracy, completeness, consistency, and timeliness of data)
15. Where is this data stored (and is it only stored once where it needs to be)
16. Where is this data resided (if you use a cloud provider)
17. When building tools for people – how is the experience for people - we need the data, but not all data will be automated - this use-experience
18. And finally, as more people will need to use more tech from dashboards - to tools - you create, how can you help them effectively adopt it so they be a multiplier to progress.

I know I will have missed something important to someone, or you have a different view, or you have a comment on the approach or something you want to go deeper on. Would love to hear from you so please comment!

Thanks for reading.