Laura King suggests five ways to turn a spreadsheet of numbers into action.
Data is a valuable tool to instigate change, keep an eye on operations, and identify glitches before they become a problem.
However, data should not merely be used as a prop to support decisions already made, which although essential, does not necessarily promote change. Instead, the real power of data lies in its ability to challenge assumptions, provide new insights, and drive action.
The sheer amount of data potentially available to sustainability professionals means that now — more than ever — there are endless possibilities to use data to support and influence business decisions. However, while few would dispute that there are plenty of opportunities for collecting data, getting the benefits from that data can be easier said than done.
Here are some ways to turn data insight into action.
One: Understand the difference between reporting and analysis
Any data collected will provide a picture of what is happening. This is known as data reporting and often looks like a dashboard with simple interpretation, perhaps using a Red-Amber-Green (RAG) rating or a smiley/sad emoji.
This use of data acts as a good barometer or health check but does not truly answer any questions as to the reasons behind the graphic. For example, it will not explain why energy consumption is trending up or down, or why a department has met its target.
Data analysis instead answers strategic questions — the “why”. Data analysis builds on reporting by establishing a deeper understanding and creating new knowledge. Once the why is known, recommendations can be made and action can be taken.
Two: Understand the context
Regardless of how much data is collected, reporting and analysis will only drive change if it is aligned with the organisation’s long-term vision. Understanding the bigger picture will mean that any deep-dive into the data is much more likely to be effective at instigating change.
First, this means being able to identify how the data analysis sits within the context of the organisation and its overall strategy. Second, this means understanding why there is a need to use the data more intelligently. Do you want to improve efficiency or highlight the benefits of a particular policy? To get the most out of any data reporting process, you need to be clear on why that report is necessary.
Here, it is also essential to make sure that anyone providing the data is also aware of the reasons why the information is being requested (for example, if it is sourced from other departments). It might alter any filters applied, how the data is collated, and they are also likely to be able to highlight any pertinent limitations.
Three: Ask the right question at the right level
To get clear recommendations, it is imperative to define the question that needs to be answered. With so much data available, it is easy to collect the wrong thing if the ask is not clear enough. A focused set of questions from day one will determine precisely what needs to be collected and how to use the data already available.
The question also needs to be pitched at the right level. Often a high-level question, for example, “how much waste are different teams producing” will lead to a generic answer with no specific actions — often this can simply result in data reporting (see point one). Instead, more specific questions will lead to data that has a clear action, for example: “how much waste are different teams producing compared to the same time last year and does this relate to whether there is an environmental champion within the team?”.
Four: Getting the balance right
Striving for perfection in data collection is a good thing, and sometimes it will be of the utmost importance that the data is accurate, accountable and of the highest quality — for example when producing corporate reports. However, there will be cases where demanding perfect, multi-faceted data can lead to stagnation, or reporting on information that is out of date.
In such instances, it might be acceptable to provide information based on a smaller dataset that can help instigate change quickly. This approach might be appropriate in a culture that accepts a try-and-learn methodology or in low-cost or low-risk situations.
Five: Tell the story well
Any analysis aims to share knowledge and to implement recommendations. As such, when reporting on data, remember that:
not everyone is going to understand all the jargon that comes with knowing an industry or sector well
a large proportion of the population learn visually
people need to connect with the data if an argument is to be persuasive.
Using plain English is a must and jargon should only be used when appropriate — consider the audience and ask whether or not they are likely to understand the language used in the report. Also, be careful that you say what you mean — words and data can be powerful and a blasé approach could lead to misinterpretation.
If you want the data to drive action, then it is crucially important for it to be presented in such a way that allows your key stakeholders to interpret the data, quickly draw conclusions and understand what needs to be done. This means:
providing context and a logical flow to the data to guide readers through the report
using good, clear graphics that bring the data to life
avoiding too much detail where it is not needed.
A good narrative is also paramount — and this narrative needs to connect with the audience if it is to be persuasive and engaging. To create an emotional connection, try to consider the values of the audience. These will change depending on who the data is being pitched at: customers, for example, might put a high value on climate change, whereas a finance manager might be more concerned about the bottom line. Understanding the different values of the audience and providing a connection to the data will make the argument much more persuasive.
Make the distinction between data reporting and analysis — the latter dives into the “why” which can be used to drive action, whereas data reporting will provide a health check.
Understand what is to be achieved and how this fits into the organisation’s goals and vision. Make sure the people providing the data know why the analysis is being conducted.
Ask the right questions at the right level to ensure that the data collected is relevant and can be used to create new knowledge that drives specific actions.
Consider how robust the data needs to be — sometimes using a smaller dataset that is more timely will help quick decision-making, particularly in low-risk or test-and-learn situations.
Think carefully about the presentation and the story the data needs to tell. Make sure the information can be easily interpreted and that the language used is appropriate to the audience.
Last reviewed 20 November 2018