How to align data collection to an organization or project’s overall mission 

IntermediateApplication 5 StepsLast updated: March 21, 2024
Learn how to design a logic model to ensure your data collection is intentional, optimized, and aligned with your mission.  

Guide Objectives

  • Understand the design and value of a logic model to optimize data collection
  • Learn how to create your own organizational or project logic models
  • Align data, metrics, and data collection objectives to the logic model(s)

Social impact organizations (SIOs) need to regularly collect data from their stakeholders and the communities they serve to evaluate their impact and processes. Collecting such data is often done actively via surveys, interviews, or focus groups. It can also be through passive data collection, such as from attendance lists, user product logs, webpage performance, etc.
With the various approaches to collecting data and the various requests for it (via clients, funders, and project teams), getting the data you need can be a messy process.  

Multiple data sources and varying evaluation needs can make systematic data collection difficult. This guide will teach you how to design a logic model to ensure your data collection is intentional, optimized, and aligned with your mission.   

Guide Specific Disclaimer

A logic model is both a means to align stakeholders around a desired impact and a starting point for developing key metrics and systematic evaluations for your organization’s work. For a logic model to become a valuable tool, it must be created in collaboration with key stakeholders. It’s designed to be flexible and should be periodically updated to reflect new insights or changes in your organization’s strategy. 

Learn how to design and use a logic model 

The first step is to understand the flow of a logic model and its key components. A logic model is a systematic framework that helps align the activities and data collection of an organization (or a project) with its overarching mission to deliver impact.  

You can review the resources in this step to better understand logic models and how they are developed. 

Key components of a logic model

Inputs: The resources that an organization commits to a program to produce the intended outputs, outcomes, and impact. Examples of resources are: people, technology, or equipment. 

Activities: The actions or events undergone using the documented inputs. Examples of activities are: holding a course, running an awareness campaign, or creating a data visualization. 

Outputs: The immediate result of a program’s activities. Examples of outputs are: number of course graduates, amount of revenue generated, or creation of a product. 

Outcomes: Socially meaningful changes that are outcomes of the outputs. Generally defined in terms of expected changes in knowledge, skills, attitudes, behavior, condition, or status. For example, if an output is the number of graduates or a program, the associated outcome could be a greater number of people attaining jobs in a certain sector. 

Impact: The results that can be directly attributed to the outcomes of a given program or collection of programs. For example, if the outcome is a greater number of people attaining jobs, an associated impact could be a higher income per capita in a region.  

Create a logic model for one of your projects or programs 

Now that you’re familiar with the concept and value of a logic model, it’s time to craft one for a specific project or program. The first step is to download the.

If it is your first time making a logic model, we recommend starting with a specific program or project you know well rather than creating a logic model that covers the entire organization.  

When setting up your logic model you can start by writing a problem statement. This is the key problem your project or program is trying to solve. An example of this is: While jobs in STEM fields pay higher than the average salaries, there are far fewer women than men in these fields in California. 

After you have entered your problem statement, you can enter the information for each column. We recommend you start with the Impact column. You may find you need to jump between columns when filling it out, but the important thing is to make a linear connection between each component along a row. 

You can reference the example on the template to see the level of detail you should be providing for each section. 


It is important to note that there are many variations of the logic model. Depending on what resource you are referring to, it may use slightly different terminology and use different components to the core ones provided in our template.

Determine key metrics and data needed to evaluate your work 

Once you have developed your logic model, you can now use it to determine your data collection needs. To do so, you can move to the & Data tab on the template. 

While filling in the information on this tab, consider the metrics needed to measure success and performance regarding the Output, Outcome, and Impact components of the logic model, and the underlying data needed to develop these metrics. Provide this information in Columns C and D.  

Filling out this Metrics & Data tab will bring you one step closer to aligning your data collection tools and methodology to the logic model. 

Metrics may be based on one data point, multiple data points, and/or multiple types of data. For example, measuring the change in graduation rates across a period of time requires multiple data points:  

  1. number of graduates in year Y 
  2. number of graduates in year X  
  3. number of overall students in year Y 
  4. number of overall students in year X

If there are multiple data points needed to calculate a metric, list them all in the relevant cell in Column D. 

Determine your collection tool(s) 

Now that you know what you need to measure (i.e. the components of the logic model) and how you need to measure it (i.e. the metrics), you can now determine the best way to collect the data. Data sources can be from active data collection (surveys, focus groups, interviews, etc.), passive data collection (observations, program applications, etc.), or external datasets. 

In this process, It is important to take a step back to understand the objectives of your data collection before deciding on the tools and frequency of collecting data (via surveys, focus groups, etc.) For example, if the data is collected for real-time performance evaluations and improvement, data may need to be updated frequently. On the other hand, if it’s collected primarily for reporting purposes, six-month or annual assessments may be suitable. 

Fill out this information in Column E, listing the data source (or various data sources) for each dataset noted in Column D. For the “type of data collection”, this could be active or passive data collection. 

‘So what’ and next steps   

After documenting how your logic model ties into your data collection plans, a natural next step is to develop the tools for collecting data. Surveys are often a primary tool in this regard. Consult our How to streamline data collection through surveys guide for a comprehensive approach to survey design.

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