What are some ways to use data to drive sustainability decisions in a team?

Begin by identifying clear and achievable sustainability goals that align with your organization's mission. For example, aim for a specific percentage reduction in carbon emissions within a set timeframe but also go granular in goal setting and tracking. This focused approach guides teams towards effective and impactful sustainability actions.

Management, as we know by definition, is the planning, leading, organising and controlling (PLOC) of organisational resources, to meet their goals (short, medium or long) in an efficient and effective manner. The key part of said management, is setting of goals. They define the success metric against which one can determine how management is in their PLOC of resources is. It defines the ground work for defining the difference between a job well done or poorly. An example is the SMART goals model. Data plays a critical role in management and hence goals need to be set to utilise data properly. Lloyds, for example, after the financial crisis of ‘07, rebuilt themselves, using 13 KPIs and backed by using data, to rise again from the ashes.

The initial step in leveraging data for informed sustainability decisions within a team is to articulate clear goals and objectives. Define what you aim to achieve with your sustainability efforts and establish the connection to your team's mission, vision, and values. Determine how success and impact will be measured. Setting precise and transparent goals fosters a shared direction and purpose within the team, guiding the identification of relevant data sources and indicators essential for tracking performance.

One of the most important things to consider is whether or not you're measuring the thing you think you're measuring. Define your goals clearly, crystal clearly, and pick a set up practices which maximize those goals. For example, If the goal is to reduce carbon emissions, consider which methods are used to measure emissions, try to find methodological errors, guesses, hypotheses, and repeatable practices. Consider whether these practices actual measure what you want. If they don't then you're not helping yourself or your company. It's tantamount to flipping a coin as a measuring device.

In my opinion, ESG data is a great way to drive decision making in your team. ESG metrics and KPIs will enable your team to have a clearer view of where you have to take actions and will enable you to improve your performance.

Following goal definition, the subsequent step is to gather and analyze data to comprehend your current situation, pinpoint gaps and opportunities, and evaluate potential courses of action. Depending on your objectives, data collection may involve diverse sources—surveys, audits, reports, sensors, or external databases. Utilize various methods and tools for data analysis, such as spreadsheets, dashboards, graphs, or statistical models. Ensuring the accuracy, reliability, and relevance of your data is crucial. Additionally, the ability to interpret the data correctly and extract meaningful insights is key to effective decision-making.

Generally from statistics, and specifically in ESG, beware of small samples. If there are only 30 data points from one location, you know nothing other than some recent information about one place. Also beware of too much data. There is a sweet spot in data collection, and if you're outside of that, then you get the "What you see is all there is" effect or the "everything is noise" effect. In order to avoid noise, you need to collect data at about this rate. Number of rows > number of classes ^ number features. For example in a ternary classification (good, needs improvement, bad) with 15 columns you'll need more than 14.35M (3^15) observations to avoid "everything is noise" or you have highly correlated data.

In my view, effectively using data to drive sustainability decisions in a team involves a structured approach. we could do this in step by step process, Starting by identifying crucial sustainability themes and establishing measurable KPIs. Implementing clear, accurate, and compliant data collection processes is crucial, especially in regulated industries. Centralising and securing this data is crucial. It's also important to involve stakeholders and educate them about the sustainability strategies. This approach ensures that sustainability is not just a departmental effort but a strategic, company-wide initiative, supported at the highest levels and integrated across the organisation.

In the third step, foster communication and collaboration within your team to share data insights, discuss findings, and determine actionable steps. Data enables the creation of compelling stories and narratives that illuminate sustainability challenges and opportunities, motivating the team to take action. It also aids in cultivating a culture of transparency and accountability, where progress can be monitored, achievements celebrated, and lessons learned from mistakes. Effective communication and collaboration ensure everyone is aligned, allowing the team to leverage collective strengths and skills to achieve sustainability goals.

Effective sustainability requires collaboration. Share data-driven insights with your team to make complex issues more understandable and actionable. Present data in an engaging way to encourage diverse viewpoints and collaborative solutions. This fosters a culture of shared responsibility and innovation.

I've found it's safest to assume your team knows much less than much more. I went into a discussion assuming my team knew mean, median, and standard deviation, and I wanted to explain why we should consider skewness and kurtosis too. Turns out my team didn't understand the effect of standard deviation, so I had to reevaluate whether my work was valuableto them. I ended up going all the way down to, here are four prices to consider. In many businesses (ESG and data especially) there are many "red personality types" meaning they want a bottom line and care less about the details. Best practice is to have that info ready, "we will capture X CO2" and also the more nuanced statistical information. "This factoryis leptokurtic in output"

Data-driven decision-making provides a framework for sustainable actions, but such an approach is not always foolproof as it depends on various factors of the organisation. Therefore, generalising the concept is not accurate. In my view, the effectiveness of decisions is directly tied to the quality and comprehensiveness of data. High-quality data can lead to better decisions. However, collecting, analyzing, and interpreting data can be resource-intensive, posing challenges, especially for smaller teams or those with limited resources. An exclusive focus on quantitative data risks overlooking crucial qualitative aspects of sustainability, like community impact or employee well-being, which are less tangible but equally important.

Sustainability efforts don’t end with implementation. Continuously review the effectiveness of your actions, using data to assess progress and impact. This ongoing evaluation helps to ensure that sustainability initiatives remain relevant and impactful over time.

The final step involves implementing and reviewing actions based on data-driven decisions. Utilize data for more effective and efficient action planning, providing clear guidelines, benchmarks, and feedback. Objectively and systematically review and evaluate actions by measuring outcomes, impacts, and return on investment. Implementation and review ensure a positive difference for the team, organization, and the environment. Data stands as a valuable asset for teams seeking informed, impactful, and value-aligned sustainability decisions. Following these steps allows teams to use data to drive sustainability decisions collectively, working towards shared sustainability goals.

When you're making choices about sustainability, it's really important to think about what's right and fair, follow the rules, and listen to what others have to say. Also, keep an eye on the newest and coolest ways of doing things in this area. This helps make sure that your plans are not only super cool today but will also work great for whatever comes up in the future!

Gamify the data to drive behavioural change. Human psychology is fairly simple - we like to win and compete. So use the sustainability data to create a positive feedback loop on driving positive change. Companies have done this with challenges for teams to reduce their carbon footprints or reduce the impact of commuting by getting more steps in. There is a clear goal, a clear target and some kind of reward. And all of a sudden, engagement goes up!