How do you validate your community organizing data?

1 Define your data needs

Before you collect any data, you need to define what kind of data you need, why you need it, and how you will use it. This will help you design your data collection methods, tools, and indicators, and align them with your community organizing objectives and outcomes. You should also consider the ethical and legal implications of your data collection, such as informed consent, privacy, and security.

2 Choose your data sources

The next step is to choose your data sources, which are the people or entities that provide you with data. Depending on your data needs, you may use primary or secondary data sources, or a combination of both. Primary data sources are those that you collect directly from your target population, such as surveys, interviews, focus groups, or observations. Secondary data sources are those that are already available from other sources, such as reports, statistics, or databases. You should evaluate the quality, credibility, and relevance of your data sources, and ensure that they are representative, diverse, and inclusive of your community.

3 Verify your data quality

Once you have collected your data, you need to verify that they are of high quality, meaning that they are accurate, complete, consistent, and timely. You can use various techniques to check your data quality, such as data cleaning, data auditing, data cross-checking, or data triangulation. Data cleaning is the process of removing or correcting errors, duplicates, or outliers in your data. Data auditing is the process of assessing and documenting the quality of your data and identifying any gaps or issues. Data cross-checking is the process of comparing and validating your data with other sources or methods. Data triangulation is the process of using multiple sources or methods to confirm or enrich your data.

4 Analyze your data

After you have verified your data quality, you need to analyze your data to extract meaningful insights and patterns. You can use different types of data analysis, such as descriptive, inferential, or predictive analysis, depending on your data needs and questions. Descriptive analysis is the process of summarizing and presenting your data using statistics, graphs, or tables. Inferential analysis is the process of drawing conclusions or testing hypotheses from your data using statistical tests or models. Predictive analysis is the process of forecasting or estimating future outcomes or trends from your data using algorithms or machine learning.

5 Interpret your data

The next step is to interpret your data, which means explaining and understanding what your data mean and how they relate to your community organizing goals and strategies. You can use various frameworks or tools to help you interpret your data, such as logic models, theories of change, or impact pathways. These frameworks or tools can help you link your data to your inputs, activities, outputs, outcomes, and impacts, and show how your community organizing efforts contribute to social change. You should also consider the context, limitations, and assumptions of your data, and acknowledge any uncertainties or biases.

6 Communicate your data

The final step is to communicate your data, which means sharing and presenting your data to your stakeholders, such as your community members, partners, funders, or policymakers. You can use different formats or channels to communicate your data, such as reports, dashboards, stories, or infographics. You should tailor your data communication to your audience, purpose, and message, and use clear, concise, and compelling language and visuals. You should also invite feedback, questions, and suggestions from your stakeholders, and use them to improve your data validation and community organizing processes.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?