Map your data ecosystem

You have identified your information needs and are now looking for data on your policy problem? Are you unclear about where these data lie? Are these data collected by several agencies within and outside of your government? Mapping your current data ecosystem can help.

What to expect

Mapping your current data ecosystem can help you identify data sources, data stewards, data users, their roles and how they interact with each other. A successful data ecosystem map consists of the public institutions, communities, private organizations and people – it shows how data flows, the data infrastructure that underlies it, how people create and benefit from the value created by the data flows and represents opportunities for strengthening overall data management.

How to get started

Before you start mapping your data ecosystem, a pre-step is to identify the reason you want the data ecosystem to be mapped, identifying and concretizing your problem statement and data needs to solve the problem.

For example, your problem could be:

  • Are you looking to skill more youth in your city or country?
  • Do you want to address the impacts of climate change on vulnerable populations?
  • Do you want to strengthen care infrastructure to increase women’s workforce?

All these challenges involve different stakeholders, demand different types and sources of data to be looked at and therefore warrant a clear problem definition. For guidance on how problem statements can be strengthened, refer to this section of the Navigator.

How do I map my data ecosystem?

To understand how your data ecosystem is structured, the first step is to map all relevant actors and identify what data lies with each actor in the entire data value chain. The data value chain describes the process of data creation and use from first identifying a need for data to its final use and possible reuse . The data value chain entails the following stages: Collection, Publication, Uptake and Impact.

Who are the actors stewarding the data or using it? Which actor is making decisions based on the data? Who is getting affected by any of those activities?. As a policymaker, the data ecosystem mapping exercise will help you understand the range of systems and jurisdictions that are involved in producing, managing and disseminating the data related to your problem.

The data ecosystem mapping tool developed by Open Data Institute (ODI) helps you map all relevant actors, data infrastructure and value exchange across a data ecosystem, so it can be communicated and improved. Doing this exercise with team members or people directly affected by your problem is a good idea. No other resources are required.

Once you have decided to map your data ecosystem, the four steps recommended by ODI are as follows:

Figure 1: Steps of Data Ecosystem Mapping I Adapted from ODI
Figure 1: Steps of Data Ecosystem Mapping I Adapted from ODI
Step 1: Map the actors

Start with drawing three circles using a whiteboard or large piece of paper and plot all people, organizations and services that are linked to the identified problem. Some key stakeholders in policymaking would be:

  • Line ministries
  • National planning office
  • National statistical office
  • Civil society organizations
  • Academic experts  
  • Private sector entities

These entities typically exchange information, data, money or services, among other things.

For example, with the objective of mapping the data ecosystem for women doing care work in Mexico City, stakeholders such as policymakers in the ministry of women affairs, civil society organizations and international organizations like the International Labour Organization (ILO), were mapped based on their relevance as primary, secondary or tertiary stakeholders.

Figure 2: Example of mapping of stakeholders relevant to "Women doing care work" policy challenge

Figure 2: Example of mapping of stakeholders relevant to "Women doing care work" policy challenge
  • Design the map around you and your role or around an organization that holds a specific dataset, or a specific use-case of the data (for example, unpaid care work and care infrastructure data in Mexico).
  • The division of stakeholders into categories is a useful sub-step to focus needs to be directed on the most relevant stakeholders first.
  • Subsequently, identify the people responsible for particular activities within this data value chain. For example, the national statistics office in most countries manages national surveys, while the methodology for collecting data on certain topics comes from international organizations, such as the ILO.
  • In addition to mapping the actors, mapping technologies such as data portals, platforms and databases relevant to the problem statement can also be helpful.

Are you finding it difficult to list down all the actors? Or are you feeling unsure of their relevance and roles in context of your problem? The below snapshot of a typical (basic) national statistical system is then a good place to start.

Figure 3: Basic structure to start mapping your relevant actors and their roles | PARIS21
Figure 3: Basic structure to start mapping your relevant actors and their roles | PARIS21
Step 2: Map the ‘formal’ value exchanges

Now that you have some or most of the actors, start mapping the flows and exchanges in your data ecosystem. Begin with the data that is shared or used by different actors, drawing lines and adding labels to indicate what data is being shared or used. Where is the data being generated first? What types of value are these data flows powering?

For example, survey data from the National Institute of Statistics and Geography in Mexico is obtained through the citizens and is used by the local governments, civil society organizations and  academic experts for further analysis. The local and federal governments have the responsibility for making important decisions based on this data. Many countries operate in similar ways as data from the National Statistical Offices (NSOs) is disseminated and used by policymakers for their relevant work streams.

At this step, the focus is on all the tangible value exchanges involved in sharing data. Examples include:

  • Reports and documents (a summary of national census, survey responses)
  • Services (bank accounts for gauging financial inclusion, care services)
  • Certificates (data licenses, operating licenses)
Figure 4: Mapping the 'formal' exchanges I Adapted from ODI
Figure 4: Mapping the 'formal' exchanges I Adapted from ODI

Think about the following questions while mapping formal value exchanges:

  • What data does your organization and other organizations access, use and share?
  • How is data shared? (e.g., via downloads, portals)
  • Is data shared reciprocally?
Step 3: Map the ‘soft’ value exchanges

In addition to sharing data and other tangible value exchanges, organizations support each other with advice, insights and other forms of knowledge. These are referred to as ‘soft’ value exchanges in the data ecosystem and are of critical value in decision making. In the case of the women in care work use case, civil society organizations in Mexico City were able to provide insights on public opinion of the care infrastructure and care work in certain communities. Similarly, academics with subject matter expertise are able to provide concrete policy advise based on research and evidence. It is therefore also essential to map the means and frequency with which these actors interact with your organization (and amongst each other). These soft value exchanges already direct you towards potential opportunities to act on the defined problem.

Figure 5: Full Mapping I Adapted from ODI
Step 4: Find opportunities

Now that you have your ecosystem mapped out, what does it tell you? Can you identify some areas where the data flow is hindered? Are there potential opportunities to make data more open and accessible for all stakeholders? Are there data leakages somewhere? This mapped out data ecosystem offers a first step in not only making some quick fixes but also identifying mechanisms to make overall data flow viable and more efficient in the long run. In addition, this ecosystem map helps to show incentive systems for why organizations work together through mutual benefit represented by data flows.

Potential Opportunities:

  • Improving data flow: What methods should be used to improve data flows?
  • Identifying impacts: What are impacts of changing how data is accessed, used and shared?
  • Creating new benefits: Which potential users and communities could benefit from new data infrastructure in a sector?
  • Creating new data standards: Where could data standards add value and bring clarity to the ecosystem?
  • Finding new stakeholders: Which new stakeholders should be involved going forward?
For more comprehensive examples of how the tool can be used, see the ODI page. More detailed guidelines on how this exercise can be conducted are available here.

How will I know I have successfully mapped my data ecosystem?

With this tool, the first benefits of a data ecosystem map can already be obtained during the process of drawing a map, as you can trace data production, data usage and imminent pain points as early insights. However, bilateral as well as multistakeholder consultations with all relevant actors would be useful in bridging any gaps that may emerge from one single perspective on the map.

Good to know: If you can’t get a group or cross-functional team together, you can always share it with others for feedback after you have the first draft.

In addition, it is important to be mindful that we are working in agile, fast-changing environments, which has implications on how data ecosystems evolve. For example, there is a huge potential in the private sector to become a big data producer for policy decisions going forward, given the scale at which it produces, collects and manages data. Therefore, these data ecosystem maps should be revisited and updated as needed.      

Note: When it comes to the policymaking process and data sharing, the ecosystem map inevitably becomes messy given the scope of issues and the number of stakeholders integral to address each issue. While it may look convoluted at first, this in fact allows you to capture as much information as possible. However, you may want to strike the right balance between being thorough while also being clear in communication. This webinar provides more information on the exercise and below you find an example of a health data ecosystem exercise in Peru.

Example: Health Data Ecosystem Mapping in Peru

What's next?

A data ecosystem map is typically revealing where the data ecosystem could be optimized. Therefore, some immediate next steps or outcomes from drawing such maps could look like this:

  • Improve the understanding of the ecosystem for stakeholders, including where changes may be required and the impact these changes have on the problem.
  • Explore opportunities for building new direct or indirect partnerships as well as strengthening current ones to ensure smooth data flows within the ecosystem. New partnerships such as data sharing agreements and cooperation can be explored with such players as the private sector as the ecosystem evolves. Learn more on that here.
  • Improve access to and dissemination of data for all relevant entities to ease the process of political decision making.

It is important to note this data ecosystem mapping tool can be used as one of the steps in the process of understanding and strengthening the overall data landscape in a particular sector or issue.

The Navigator offers a broad range of resources to ensure that this exercise is fruitful and the challenges we face are addressed in a sustainable and ethical manner. To explore more, see the next step of identifying data gaps.


Tool: Mapping Data Ecosystems
by ODI

Related Use Cases

Using Online Job Vacancy Data for more evidence-informed labour policies in Vietnam
Learn More
Mexico (Women in Business)
Enabling Women's Economic Empowerment: An AI driven approach to Gender Equality
Learn More