March 2026 Brown Bag Webinar Recap

Overview

In our March 2026 Brown Bag Webinar, Tess Konnovitch shares her journey from scientific researcher to data visualization specialist to  marketing manager, tackling one of sustainability science's most persistent challenges: the gap between producing rigorous LCA analyses and actually getting decision makers to act on them. Drawing on her daily experience reviewing and designing LCA reports, Tess walks through a three-part framework — designing the message, designing the visuals, and designing your workflow — giving practitioners concrete tools to make their work land with any audience, from fellow scientists to politicians to the general public.

Key Points

• Science alone doesn't create impact. Accurate analysis is necessary but not sufficient. Decision makers act on their understanding of the data — not the data itself. Scientists have a responsibility to bridge that gap.
• Greenwashing isn't the only communication risk. Greenwashing gets the attention, but "data dumping" is just as dangerous. Overwhelming audiences with nuance, uncertainty, and raw numbers often leads to confusion, inaction, and indecision.
• Message before visual. Always define your take-home message before designing any chart or layout. Know your audience, know your format, and know what tradeoffs and context are truly relevant to them.
• Let pre-attentive attributes do the heavy lifting. Color, position, size, and typeface are processed instantly. Use them intentionally to guide where your audience looks first — and plaster your key message like a billboard on every chart.
• Dashboards over page-flipping. Grouping multiple impact categories into a single data grid keeps reports readable, reduces cognitive load, and lets patterns emerge across categories at a glance.
• The best tool is the one your team will use. Advanced tools like Adobe and R can create bottlenecks. Microsoft Office — especially with linked Excel charts and PowerPoint templates — enables fast, collaborative, and maintainable communication for most teams.

"The challenge isn't just producing accurate analyses. It's ensuring the insights are clear enough to guide action."

— Tess Konnovich, Scientific Marketing Manager, EarthShift Global

About the Speaker:

Tess Konnovitch, Scientific Marketing Manager

Tess Konnovitch is the Scientific Marketing Manager at EarthShift Global, where she leads strategic communication of life cycle assessment (LCA) insights across global audiences. She joined EarthShift Global in 2022 as a Data Visualization Specialist and was promoted into marketing leadership, blending scientific rigor, visual design, and clear messaging to ensure complex sustainability analyses resonate with decision-makers.

Tess holds a B.S. in Environmental Science and Biology from La Salle University and a M.S. in Computational Biology from Rutgers University, where her research focused on how organisms to ecosystems respond to human-driven environmental change such as warming and eutrophication. Her background bridges ecology, quantitative analysis, and communication — allowing her to translate dense technical data into meaningful, accessible insight.

Edited Webinar Transcript:

All right, everybody. Some people are still joining, so I'm going to start with a title and introduction. For those of you who don't know me, my name is Tess Konnovitch and I'm the Scientific Marketing Manager here at EarthShift Global. Typically, I moderate our brown bag webinars — introducing the speakers and giving some background — but today I'm actually presenting. So, I'll introduce myself and then we'll get into my slides.

Today I'll be speaking on communicating life cycle assessment, and the goal is to help you make your complex LCA data clear, visual, and actionable.

0:44

A little bit about me — and I like to spend a little bit of time here because I think it's really important so you know what kinds of questions I can answer at the end.

I got my bachelor's in environmental science and biology from La Salle University, where I dual majored and also had a minor in education. I originally wanted to be a high school science teacher. When I got to university, I realized the college professor schedule and content was more aligned with what I could see myself doing, so I pursued a PhD in Computational Biology where I worked in an ecology lab and continued that environmental science research.

During my PhD program, I realized I really loved presenting my research results more than actually sitting at the lab doing the research. So, I eventually left with my master's and got hired at EarthShift Global as a Data Visualization Specialist — my dream role at the time. I got to visualize other people's data on a weekly basis, which was really fun.

I've recently been promoted to scientific marketing manager. I've taken on content, some design, and worked on the UI/UX of our software, Matter PD. I've attended a number of conferences focused on Adobe, data storytelling, and Figma, and I've had the privilege of speaking at several LCA and sustainability-focused conferences like ACLCA, the LCA Institute, and ISSST. I'm also a course instructor here at Earth Shift Global — my course is coming up very soon next month, so feel free to scan the QR code if you want more after this talk.

2:53

So I want to talk about why communicating LCA matters in science generally, why we need to communicate it, and then I want to get into a three-part process where we talk about how to design the take-home message, then the visuals, and then different processes that can help you be more efficient in doing those two things. We'll end with a short summary.

3:17

Why does communication matter in science and LCA specifically? Because science doesn't create impact on its own. Impact happens when we can pull those scientific insights from the data — making it so that science is understandable and used.

As scientists, we generate knowledge, but the people — the decision makers — act on their understanding and interpretation of that knowledge. There's always been a little gap between those two, and that's what I really want to focus on today.

As scientists, we do our research for a number of reasons. Probably number one is to get paid. Number two, you have this deep feeling that this is what you were meant to do and maybe you really enjoy it. But I think there's also a fourth component — especially in the sustainability sphere — we want the research we're doing to have impact, maybe change legislation and make an impact on the world. Most of us aren't always able to make that impact directly. We do the research, but there are those decision makers who can actually act on it. That's where I feel really called to work on making decision makers understand.

The challenge isn't just producing accurate analyses — I'm sure you all do that very effectively. It's ensuring the insights are clear enough to guide action. That's what we'll be focusing on today.

4:51

Let's talk about communication risks. Most of us in the sustainability world know about greenwashing — the risk that arises when people make really broad claims like 'this is green' or 'we're sustainable' that aren't necessarily backed by data. Greenwashing over time has started to carry real consequences: regulatory actions, lawsuits, public scrutiny. People like to do their research now, so greenwashing is starting to have consequences.

Scientists rarely greenwash, but sometimes we create the opposite problem. There's a different communication risk that arises from us as scientists. We tend to take the opposite approach — dumping all of the data, emphasizing nuance, showing assumptions, talking about uncertainty, wanting to show all of this data, and being comfortable saying 'it depends.' There are tradeoffs. Look at the context.

This is good scientific practice — and I will emphasize this a few times in this talk — there is nothing wrong with this. But it creates a different kind of communication risk, especially for non-scientists.

When we think about the user from a design perspective: with greenwashing, the user will either say 'great, this product is sustainable!' and move on — even if it's wrong — or, more and more, they'll be confused. What does sustainable mean? What does green mean? And when we data dump, we might throw up all these charts, and we get the same response: 'I don't know what to do with this data.'

There's a really great article by Valentina Prado and Gianni Guglielmi from 2020 where they talk about the risks of data visualization and communicating LCA — specifically inaction, misinterpretation, and indecision. A lot of times when we throw all this data at someone, they feel overwhelmed and intimidated, and it results in indecision.

What I really want to emphasize today is trying to get to that insightful interpretation. We can take all that data — you can dump it in the appendix — but we as the scientists who work closely with this project can create an insightful interpretation that less scientific audiences can actually absorb, understand, and act on.

8:28

I want to say again — because I imagine most of our audience today is scientists — there is nothing wrong with data dumping. There is nothing wrong with detailed data. Don't come for me. I'm not saying that at all. We as scientists love it. It's important for ISO standards, transparency, reproducibility, all of the things we really value.

We need the data. Decision makers need the insight. They can't always cross that bridge from data to insight on their own — and that's where I feel strongly that scientists have a responsibility to help.

9:17

Okay, so we're going to get into designing the message. Anytime I look at some data and I'm personally overwhelmed, I'll reach out to a teammate and say, 'What is the message? What is the take-home here? What do you want people to take away from your results?'

I really encourage you to develop your message first — before your visual, before your report. Choosing your format is really important because it's going to determine the length of your message, but there are also pros and cons to each type. How many messages can you have in a report? A ton. A poster? You can only have a few visuals. A presentation is different again. I want to talk about these different formats and how I think about the design process when considering my message.

10:25

Reports are built for documentation — designed to document the analysis thoroughly and transparently. I feel strongly: put as much in the appendix as you can. It becomes really chunky and clunky when you're trying to read through. I review LCA reports every day, and you want it to read like a story. Even though it's a report, you want people to be able to flow through it.

Posters, on the other hand, are designed for drive-by reading. Make your message clear without you needing to stand there and explain it. Someone should get the key takeaway just walking by.

And then presentations — you want to tell a story. Think of your slides as a visual cue for the brain, not a written report for the eyes. If they're reading, they're not listening to you.

These different formats will help you think about your message. The message won't really change, but the size, impact, and scope of it will be dictated by the format.

11:45

The design timeline remains the same. I feel strongly about establishing a color scheme early on. If you're at a university or company, you probably have templates available — just make those your starting spots. The colors, charts, font, styles, headings — everything's already done for you. The reason to establish that early: I can think of myself in grad school collecting data and then sometimes having to present the next day.

Don't add extra steps if you're already working in Excel, Microsoft, and PowerPoint formatted to your color scheme or branding. That takes a step out. You can just drag that figure — it's already formatted and ready to go. If your color scheme is done early, you know what stock photos you can include. I really recommend this.

Then you can start creating content chunks — deciding what has to be included — and then arrange those chunks. For a poster, you can decide where each thing goes depending on how many visuals and how big they are. And then finally, my favorite stage: refining and polishing. This happens at the very end.

I actually start everything in PowerPoint just because I like the layout — even a report. I'll say, 'Okay, here's first page, here's executive summary, here's what I want to say in each section.' Creating an organized workspace takes a little bit of time at first, but then it becomes part of your workflow, and your design timeline becomes really organized and efficient.

14:13

The other thing to consider for your message is your audience — their wants and their biases. LCA practitioners want clear, accurate, transparent results. There may also be some pressure from clients. Designers or marketing people want aesthetic appeal and client satisfaction — and there can be biases toward aesthetics over accuracy. Clients really want actionable insights and want to be able to communicate what they paid for. You have to be careful of some of those biases too.

Then there are consumers, marketing teams, government, the public. When you get to the public, you have to consider not just biases but limitations. As LCA practitioners, we love data, we love statistics. But when we're thinking about clients and the public, we don't know what level of math or statistics or understanding of life cycle assessment they'll have.

I find this a real reality check: most people don't know what mean, median, mode, uncertainty, and standard deviation really mean. Those words we throw around like jargon might need to be defined depending on who you're presenting to. So, think about your take-home message, think about your audience, and adjust how you word things accordingly.

16:24

I only have one slide on accessibility today — it deserves a lot more, and my course covers it in depth. I'll just briefly introduce it here.

If data visualization and communication aren't accessible, key insights could be lost. The ADA requires that digital information is accessible, and there are resources at the bottom of this slide that list recommendations — they can be applied to visuals and digital communications. Accessible visualizations ensure that all stakeholders can engage with our information, which increases the likelihood that we can actually make change.

I really like this quote from the ADA's website: an inaccessible website or data visual can exclude people just as much as steps at the entrance to a physical location. My one caveat: there are hundreds of engineering and building plans for accessible staircases. We're seeing more of that in the digital world too, but it's not as cut and dry.

There are a lot of things to consider: color contrast, font size, hierarchy. I feel really passionate about hierarchy. A lot of people look at accessibility and stop at colors, but cognitive overload is another piece that people don't always consider. Always design with accessibility in mind.

18:28

The before-you-visualize checklist. I encourage you to go through this before you start visualizing — it really helps you solidify whether you're ready.

What is the core message? What decision or insight should the audience walk away with? What's the nuance — the 'it depends' factors, the tradeoffs, the uncertainties and context that are relevant? Do you know your audience? Who's interpreting the results, and what level of detail do they need? What contextual factors, limitations, or biases might exist? What is the format, and how will the audience interact with this information — will they print it? Will it be displayed on a huge conference screen? And have you set up your visual system? Do you have an accessible color palette, a consistent visual theme, and guidelines from your institution to follow?

Having all of this will make everything easier when it's time to visualize.

19:39

Now we're moving on to designing the visuals. We're not going to get into choosing the right chart today — I've given a few talks on this and my course covers it in depth. But each chart type has pros and cons. There's some research that suggests you shouldn't use radar charts, though I'll admit they look really pretty.

Probably most LCA practitioners default to a bar chart, though I encourage you to branch out. So now what do you do with those charts? You get your Excel output, hit insert chart, and then you get something. I want to talk about pre-attentive attributes — the visual cues our brains process instantly, which means less mental load for your users or audience.

I encourage you to put as many as you can on your chart without cluttering it. Anything that can be automatically understood without you having to explain it is really important: color, position, size, text, font size, typeface — italics versus bold versus regular. There's a number of different design elements that give your audience a vibe without needing an explanation.

21:32

Here's an Excel output. We're looking at sales. Immediately, even for me looking at numbers all day, it takes a second to know: is that 4 million? Is it 450,000? There are ways we can get around this. Too many zeros. Try not to repeat words — it's just clutter on your chart. Add an informative subtitle — we have the room.

What is our takeaway message? If I was presenting this data to my boss, I would want to emphasize how much money we made in quarter four. Get that takeaway message and then plaster it like a billboard on your data visual.

Here's that same data changed in a way that now I'm almost guiding the way people read my chart. If I close my eyes and then look at it, I see 'grew 25%' in green. I immediately get that key message and then I can go back for context. You want to try and control how people are navigating your visual because you're not going to always be there to explain it.

22:50

Let's try some with LCA data. Our branding colors at Earth Shift Global are green and teal. When it comes to a direct one-versus-one comparison, I don't like to use green — people associate green with 'good.' Similarly, they associate blue with good too. Once I have to use a ton of colors, green definitely comes in. But for a one-on-one comparison, if I made one bar gray or red, people will automatically assume it's worse or better before they even read it — and then they'll have to do some mental work to correct that impression. So use greens sparingly in comparisons — not a hard rule, but something to consider.

I also want to show you how I prefer to show baselines. And here's a cleaner version of the same cluttered data. We have an informative title, the key message plastered at the top — my eyes go there — and then this orange box indicating the baseline. Back in the original, you might not have known anything was really different about the baseline. Here, it's very obvious. My products are both blue, so I know they're comparable. The axis is cleaned up. It just feels cleaner, and I can get the take-home immediately because it's right there.

In PowerPoint, you can use transitions and animations to direct where your users are looking. In a report, you don't have as much flexibility controlling the narrative. In PowerPoint, I can force you to look at things. I encourage you to use transitions and animations to direct focus — and they should pair with your voice.

25:30

Let's talk about designing for multiple impact categories — something we face a lot in life cycle assessment. I'm often displaying 18 categories at once. They have similar x-axes but don't share the same y-axis, and they often have similar legends. What can be really overwhelming is having a graph, a paragraph, a graph, a paragraph in a report — that's 20 pages.

Instead, when possible, I like to create what I call dashboards — or data grids, which is probably the most accurate term. I can still see everything. I'm not comparing across categories since they each have their own axis, but I know the units. The legend is collapsed. It's cleaner. This takes up one page in a report, one slide.

This works really well when a product follows the same trend across impact categories — then you can talk about the exceptions. If every graph is totally different, that might warrant splitting them up. But instead of repeating the same thing over and over, we can group them and then add a summary: 'Actually, in marine eutrophication this is different, but otherwise they follow this trend.'

27:05

Another thing I find confusing in reports is when breaking things down. In a report, this could be a page of graph, a paragraph, flip, a graph, a paragraph — by the time I get to the last breakdown, I don't even know where I am anymore. Instead, I encourage you to use arrows and make it one figure.

If you can't fit it all on one page, you could make a little key at the top of each graph that shows all the parts — product part one, part two, part three — and then bold where you are. Help ground people in your process. Show them where they are.

Here's one I really like that we did. The breakdown was complex — we went from five things to one thing and then broke down three, some of which had two sub-parts. Over separate pages, by the time you get to the end you don't know where you are. Finding a way to establish hierarchy to help orient people is really important. And it makes it more accessible — we talked about cognitive overload. Help people know where they are.

28:50

You can also help establish emphasis and connection with pre-attentive attributes. I love a transparent rectangle — that's all these are. I grouped elements by facility using these little design elements to help connect related items.

29:16

This is something I hear a lot: disproportionate data. We've all been there — you hit insert chart and one thing has a huge impact and nothing else registers. Think about your take-home message. If I look at this chart as is, I'm taking home that only the teal part matters. I'm also assuming all eight of these parts are the same amount. If that's true, you can probably just leave it and maybe add a note. If it's not true, it's time to get creative.

You can do dual axes — though if you do, be very clear about which axis is which. You can do axis breaks — and I encourage you to put the value at the top of the bar that breaks the axis, just so people know something different is going on. You don't want them to misinterpret.

But if you have the time and ability, I encourage you to get creative. My favorite approach lets you see that there's a super impactful part and still shows the differences between the other parts. You could color those parts differently or just label each one — there's room. I know exactly what's going on, which is the goal.

And then when there's just too much data and none of those approaches work, I want to talk about removing invisible data. The words 'removing data' feel really wrong — so let me explain. If you insert something with too many categories, it becomes overwhelming. A sunburst is a mess. A treemap usually has more space but still gets cluttered.

What I would do is remove anything 2% and below. Again, that feels a little wrong, but hear me out — you're not removing data. You're making invisible data visible. Now I can actually see what each segment is. I had no idea some of those labels even existed before. The result is crisp, clear, and I don't have to make any guesses. The exact values can be added in parentheses if you really want them.

Strive for simplicity while maintaining nuance and detail. Limit colors and patterns. With many pie chart slices using different colors, they start looking the same — and if someone has a color vision impairment, they might look identical. Separate chunks with lines between them, or add borders. Avoid cognitive overload.

33:08

I want to also talk about designing for more technical audiences. I love the approach of adding a visual cue to a data table. You can do this right in Excel: highlight, go to conditional formatting, and fill cells relatively. Now I can start comparing at a glance. Adding percentage columns and totals makes it even cleaner — and it's not overwhelming even to a non-technical audience.

You can also do a heat map using that same conditional formatting in Excel and then copy it as a photo. With LCA data specifically, I think it's really important to remind people that higher is actually worse — high is not good. Make sure your legend reflects that, so people don't interpret it backward.

You can also add interactivity with the Morph feature in PowerPoint, Excel slicers and pivot tables, or Tableau. With Morph and a 3D object — and PowerPoint has a whole 3D library — you can actually walk people through different parts. The theme today is really guiding people through your chosen narrative, and interactivity gives you tremendous control over what they see.

35:42

After you visualize, here's the checklist. Is the main takeaway immediately clear? Do the visuals highlight the important patterns and comparisons? Does the title guide interpretation? Your slide title could be your key message — instead of just saying 'Results,' it could say 'Product A performs better across these three categories.' Any text on your slide or figure can help guide interpretation.

Did you remove unnecessary complexity — repeated words, excessive labels? Is the visual readable in its intended format? And are the color and design choices consistent? That's really important to me. We have clients with hundreds of charts, and I'll go through to make sure that if a particular part is blue in one chart, it's blue throughout the entire report.

Something I like to do: for a contribution analysis, I might use a stacked bar chart one way, and then when we move to a sensitivity analysis, I'll switch the orientation — just so the user registers that something changed. Little things to consider.

37:12

So those are the two main things: the message and the visuals. Now I want to talk a little about how you can design your process to make those two steps easier.

I encourage you to build a process for your team and your audience. Your visualization tools should align with your team workflow. When I was first hired in a more design-level role, I was coming from grad school and had taught myself Adobe Illustrator, Photoshop, Lightroom, and InDesign. I felt really confident with it. When I started at Earth Shift Global, I was forcing myself to use it because it felt like the industry standard.

Over time, I realized it was really inefficient for my team. We have to update visuals. Clients might change data. When I was going into Adobe, I created this bottleneck where only I could change something. Similarly, not everyone knows a coding language — that turned into a bottleneck where only I could update results.

When choosing a workflow, consider: Who will create and update the visuals? What tools does your team already know? Will clients need to edit or reuse the outputs? How often will results need to be updated? The goal is not to use the most advanced tool. I've been to design conferences where people are amazed by what I can do in Microsoft PowerPoint. It's not the most advanced tool for designers, but it is the best tool for my team — one that enables clear, efficient, and maintainable communication.

39:03

Let's talk about the three tools I personally use.

Coding-based visualization: objectively, probably the best tool. There's so much flexibility, and once you set it up, figures can be updated with a click of a button. However, if everyone on your team doesn't know the language, it creates a bottleneck. And if you're embedding into PowerPoint, coded charts aren't linked anymore — that's an additional step. In grad school, I used this often because I had the same figure every month with new data — I just updated the Excel sheet and all figures updated. But then I still had to move them into presentations.

Adobe: objectively a really powerful tool. You have so much control and no constraints. But it would be a joke to suggest my team switch entirely to Adobe for figures. We have reports with 30-plus figures, 30-plus dashboards. If we had to design each one in Adobe, we would need a hundred of me. That said, if you work for a government entity or similar and only make a high-impact visual once a week or once a month, Adobe might be right for you.

Microsoft Office: this is what I use all day, every day. You get fast creation and iteration, easy collaboration across teams and clients, and direct editing with no coding experience. Just the other day, I was out of office and a teammate needed one thing updated. I was able to send the PowerPoint from my phone and say 'just edit the text right here.' No bottleneck. The linked data and templates I can't do without — if a client changes their data, everything updates automatically. It's easy for teams and clients to generate, update, and reuse.

42:20

I also encourage you to use RAWGraphs — it's a free tool online. It has almost everything I need when I want to get a little creative with a chart. You download it as an SVG, then insert the SVG into PowerPoint, right-click, click ungroup, and all those lines and circles become editable shapes. I can change the color, change everything. I love RAWGraphs. It seamlessly goes into Adobe or PowerPoint, and for me it's really removed the need to go into RStudio or Python.

43:01

I want to talk about Bright Slide a bit. In Microsoft generally, you can insert icons, convert them to shapes, and then edit them — using shape format, editing points, etc. This is something I used to go to Adobe for that you can actually do right in PowerPoint.

Bright Slide specifically lets you align things, match sizes, and distribute objects — tools I used to do manually by dragging. I also really encourage you to have the Selection Pane open. You can see every single object on your page, click on them, and control their layering. Grid lines are also invaluable — I have them on for every title to make sure everything's aligned.

And here's something people don't always realize: Microsoft treats charts just like objects. Everything I just described, you can do with charts too. You can also create chart templates — apply a template and all of your charts reformat into the design you want. This is critical for my dashboards. I can create a dashboard in about two minutes — click all of them, match size, align, distribute, put them in a grid. This has completely changed my ability to quickly generate dashboards.

45:40

All right. Let's get to key takeaways and then questions.

Clear communication starts before visualization — that's why I started with the message today. Know your message, your audience, your format, your tradeoffs, and what's important.

Avoid both extremes. I don't think anyone here is going to greenwash. But also avoid the data dump. Take the time to think about what insights you want to share — especially for clients who may be relying on your scientific knowledge to help them understand.

Design your visuals to reveal patterns. Use pre-attentive attributes and design elements to guide interpretation. Write your title and structure to help audiences understand what matters.

And make it efficient — because then you're more likely to actually do it. I used to dread making dashboards. Now it's seamless. I've created a workflow that's really organized. I never open a blank PowerPoint — I open a template because everything's already where I need it. It takes out that annoyance and I'm more likely to do it well.

Q&A - 48:15

Are there any special considerations when presenting data and analyses to politicians?

Absolutely. Language is really important. I'd say the less scientific jargon and the less complex the visuals, the better. I'm sure some politicians are interested in science — but familiarity with scientific reports and visuals varies widely, and if you don't know your audience, simpler is better.

Most of America reads at roughly an elementary or middle school level. If you don't know your audience, you can run things through AI or online tools — 'Is this at a fifth grade reading level? Do I have any jargon here?' When it comes to politicians, I would not assume they all understand scientific data. Start simple.

Are the graphs you're presenting all created in Excel, or do you use other programs?

I would say 99% of the graphs I've visualized in the last four years are in Excel. It's just what works for my team. We paste into PowerPoint, and because the charts are linked, it's literally just a copy-paste — high quality, and if something changes, it updates. If I add something like the baseline box you saw earlier, I literally draw a shape in PowerPoint and send it to the back or use the selection pane to align it. Most of my design work happens right in PowerPoint.

The other tool I'll use is RAWGraphs — mainly for Sankey diagrams. You paste your data, choose your chart type, adjust variables, download as an SVG, insert into PowerPoint, ungroup, and it becomes editable shapes. It's a great workflow.

Is there regulation guidance you refer to for accessible graphs?

Great question, and something I'm going to make a note of. When I developed the guidance document and my course, there really wasn't much. I reference the WCAG and ADA resources listed at the bottom of my accessibility slide. As far as I know, there are regulations about physical accessibility, but the digital space is less defined — at least as of when I last updated these materials a few months ago. If anyone knows of specific regulation guidance, or is in a position to push for it, please share! I'd love to have it.

Any success stories where a client initially misunderstood results but changes in visuals or dashboards helped them make better decisions?

That's a great question. I'm more on the quiet design side and don't often interact directly with clients, so I can't speak to specific examples firsthand. But I know we receive really positive feedback on dashboards. People love not having to flip through pages — the insights just jump out at them. Whether that led to better decisions I can't always attest to, but the experience is clearly better. I should ask my team about specific examples!

Q: Could you speak a bit on how you create dashboards in Microsoft Office, or is that a separate training?

I can show you a bit behind the scenes. These dashboards are literally just individual charts, all still linked. On a large report with 30-plus dashboards, when the client changed their data, I just clicked each chart and hit 'refresh data' and everything updated. The legend is actually a saved photo of the legend in the order I liked — I removed the individual legends from all the charts and placed one shared legend at the bottom. Titles and labels are just text boxes. Tables work great for some elements too.

It's all things you probably already know how to use — just being a little creative with arrangement. If you want a full training on creating linked dashboards, my course covers this in depth.

Any recommendations on representing data in appendices with respect to aggregation?

I feel strongly that anything you can put in the appendix, you should — and if possible, make the appendix a separate supplemental document. My dream LCA report is a concise 30-page document with an executive summary, a dashboard, a table, and insights — and then a separate 300-page supplemental document with all the ISO-required detail.

When people open a PDF and see 300 pages, a lot of them just close it. That's why white papers and separate marketing documents exist. My main recommendation: the report should read like a story. I shouldn't have to flip to the appendix to follow along. If context is needed to get through the story, put it in the report. If it's 'and if you'd like more detail,' put it in the appendix.

Closing

Thank you all so much for joining and being so engaged today. There are a ton of resources in the chat now, along with my email and recommended readings. If this wasn't enough, I hope to see some of you at my course in two weeks — you can send me your visuals ahead of time and we can work through them together.

We'll also have a webinar focused on food and agriculture in LCA in late April, so I hope to see you there. Sign up for our newsletter if you haven't yet, and we'll be in touch. Have a great Thursday. Bye!