What is Ecosystem Mapping?

While knowing how ecosystems are structured is helpful for understanding how they work, the big question most people have is how they can understand what’s happening in the ecosystems around them.

That’s the job of Ecosystem Mapping, the process of systemically identifying, categorizing, and collecting information on the different assets within a given ecosystem.

Despite the name, ‘Mapping’ an ecosystem doesn’t mean plotting all the different assets on an actual map (although that can be helpful in some cases). Rather, it means identifying all the different assets in an ecosystem, categorizing those assets into buckets using Keywords, collecting information about what those assets are & who they serve, and finally displaying this data in a way that makes it easy to understand, analyze, and navigate it.

At the end of a good Ecosystem Mapping process, you have a detailed database of what exists in the ecosystem, what those assets are up to, and who they are connected to, a platform that allows you to explore that data, and a process for keeping it updated. When done successfully, the process answers the fundamental questions of “who is doing what?” and “what is going on?” within your ecosystem.

Why is Ecosystem Mapping important? There are three main reasons:

1. If you want to build or grow an ecosystem, you need to understand what already exists in the ecosystem, what those assets do, who they serve, and who they are connected to.
2. Without a robust dataset of what exists in your ecosystem, it’s hard to accurately identify gaps within the ecosystem, and even harder to create new programs if you don’t know what’s already out there.
3. A good ecosystem map enables ecosystem stakeholders to navigate the ecosystem to find what they need, whether that’s a small business owner looking for resources or an individual searching for an expert in an industry.

Even for small ecosystems, Ecosystem Mapping is a difficult task. While Ecosystem Mapping is fundamentally important to any ecosystem building or systems-level change effort, there is no way to get around the fact that it’s an incredibly costly and complex process - you have to create Data Paradigm that allows you to collect important information about each asset, devise a way to identify these assets reliably at scale, actually collect and clean all the data, and after all of that, you have to keep the dataset continuously up to date - otherwise it becomes worthless for analysis and navigation.

We summarize the process of ecosystem mapping at a very high-level below, but before we go further, an important disclaimer -

Ecosystem Mapping is what EcoMap Tech, the company that provides the Ecosystem Information Center, specializes in. We created our technology precisely because the process of Ecosystem Mapping is really hard, time consuming, and expensive, and after years of studying the problem, it ends up that fancy algorithms & data processes are the best way to bring down the cost & complexity of it.

Because EIC is an informational site, we won't shove our products in your face in every paragraph. However, if you areplanning to do Ecosystem Mapping for your community, and the process below does sound hard, you can set up a free consultation with us to get targeted advice for your community, and learn more about how dozens of ecosystems trust EcoMap to save them a ton of time and money with ecosystem mapping. Okay! That’s it! Ad over!

There are three main steps to the ecosystem mapping process:

1. Create a Data Paradigm

A Data Paradigm is the array of different types of information you want to collect about each Asset in your ecosystem. If you think of filling out a spreadsheet, if the rows of the spreadsheet are each Asset in your ecosystem, the columns make up the data paradigm: it is the array of different fields of information that you collect about each item in the ecosystem.

A good data paradigm collects three key types of data: Keywords, Descriptive Information, and Relational Information. The Keywords are the different categories of things - they are “tags” that let you specify if something is an “Accelerator” or a “Funding Source”. Keywords not only help you categorize the data you collect, but also allows you to navigate it with filters and analyze your ecosystem on characteristics such as industry, audience served, types of resources available, and more.

Descriptive data tells you specific information about each asset, and includes things such as a Short Descriptions, URL, and Contact Emails. Descriptive fields are what make your dataset informative and useful. Finally, the Relational Information describes how different Assets relate to eachother, and is fundamental for understanding how your ecosystem is - or isn’t - connected.

Many ecosystems skip defining a data paradigm and try to create it as they go. This will not only slow down the process, but it will also lead to worse-quality data - if you add a Keyword halfway through the dataset, you’ll need to go see if that keyword applies to everything you already collected. That being said, defining a data paradigm isn’t easy - it took EcoMap 3 years, 4 full-time team members, and a linguist to finalize our set of 7,000 Keywords. Yours doesn’t have to be this robust, but it’s important you define it before you start the data collection process, or the accuracy of your data will be compromised.

2. Collect the Data

Once you have created your data paradigm, now you have to actually collect the data about your ecosystem. There are likewise three steps to this: identifying all of the assets, extracting the information about them, and then keeping the data updated. When you’re identifying all of the assets, start by crowdsourcing lists that other people in your ecosystem have already put together to get a starting list of key Organizations in the community. Then, identify the Resources that are provided by, or sponsored by, those Organizations. Finally, identify only the core people involved in those resources and organizations - if you try to identify all People in the ecosystem, your process will never end.

If you want to send out a survey to your ecosystem, we recommend you use it to crowdsource the list of different assets, but not to fill out the information about each asset. Why? Unless you take the time to train everyone filling out the survey, each person will have a different interpretation of what an “Incubator” is vs an “Accelerator”, or what constitutes a “Growth Startup” vs a “Scaleup” - which means your dataset will be inconsistent and inaccurate. The information about each asset should be extracted by the same few people, even if that’s a small group of well-trained interns. This guarantees that the data will at least be consistent.

After you collect all of that detailed data, you have to create a mechanism to keep it updated. There aren’t many recommendations we have here, because besides having a full-time person watching your ecosystem and updating the dataset, it’s a pretty complex process (we literally developed an advanced machine learning algorithm to help us do it - it’s not easy to do manually). Regardless, it is important that the data you collect is kept up to date, because otherwise you can’t rely on it for analysis, or to provide it to stakeholders who need reliable information.

3. Present & Analyze the Data

Once you have all this information, it’s likely stored in a Google sheet or similar spreadsheet tool. While this is helpful for data collection, for presenting the information to stakeholders and analyzing it, you’ll likely need a few different interfaces.

For one, you’ll want a simple directory that allows people to filter the data based on the Keyword tags you have defined (for example, to find “Funding” for “Growth Startups” in “Baltimore City”). Additionally, for many ecosystems, you’ll want a map so that you can see the physical location of these assets, and a platform that allows people to save things they are interested in, and has a way for you to contextualize the data (such as creating “guides” to the ecosystem.

Finally, you’ll want some type of analytics software that presents different charts and graphs that help you understand your ecosystem and how it’s broken down, in addition to how people are engaging with the data itself. While this may seem like extra work on top of the excessive work of gathering the data, it’s important that you present the information in a way that is easy to understand and navigate - otherwise, all that work was for naught.

Summarizing the process of Ecosystem Mapping into three steps is a bit like saying “getting a PhD is just doing research, writing a dissertation, and defending it”. Even though the full process is complex beyond the scope of this guide, it’s important that you have a general understanding of why Ecosystem Mapping is important, and how it can be done within your community.

Now that we’ve gotten through the fundamentals of ecosystem, let’s look at what ecosystems are made of.

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