What is impact data?
In order to measure social impact, it is crucial for organizations to have reliable output data that matches the set metrics. This helps them keep track of their programs and interventions, and evaluate their effectiveness. By collecting data in a structured manner, organizations can analyze the results and use the insights to make informed decisions about improving or adjusting their programs.
Data collection tools are essential for organizations working to maximize their social impact. These tools allow organizations to gather valuable information about the communities they serve, measure the effectiveness of their programs, and make informed decisions about future initiatives. Many different data collection methods are available, each with strengths and limitations. This article will explore the various options for collecting data and help you determine which method best suits your organization's needs. Whether you are looking to conduct surveys, focus groups, or other forms of research, this guide will provide you with the knowledge and resources you need to make informed decisions about your data collection efforts.
Impact Data and Data Collection Tools
Social impact data is an invaluable tool for measuring the effectiveness of organizations and programs that aim to create positive societal change. This data can take many forms, including activity, output, and outcome data. It can be captured through various methods, including paper and Excel-based systems, CRM systems like Salesforce, and case management apps. To collect and analyze this data effectively, organizations must design effective data collection processes that align with their theory of change and consider the different dimensions of impact. By collecting and analyzing social impact data over time, organizations can gain valuable insights into the effectiveness of their programs and make improvements to serve their stakeholders better.
This article will clarify the different types of data and the most effective data collection tools for your selection.
Importance of collecting and analyzing social impact data
Collecting and analyzing social impact data is crucial for a variety of reasons. Here are a few key points to consider:
- Understanding the impact of programs and initiatives: Collecting and analyzing social impact data allows organizations to understand the effectiveness of their programs and initiatives. This helps them to identify areas where they have a positive impact and areas where they may need to make adjustments or improvements.
- Demonstrating the value of the organization: By collecting and analyzing social impact data, organizations can demonstrate the value and impact of their work to stakeholders, including funders, donors, and policymakers. This can help to build support for the organization and its mission.
- Making informed decisions: Data analysis allows organizations to make informed decisions about allocating resources, designing programs, and measuring progress. It helps them prioritize their efforts and ensure they have the most significant possible impact.
- Identifying trends and patterns: Social impact data can also help organizations identify trends and patterns in their populations. This can inform the development of new programs and initiatives and help refine existing ones.
Collecting and analyzing social impact data is essential for organizations that want to understand and measure their work's impact, demonstrate their value, and make informed decisions about allocating resources and designing programs.
Impact Data design
When it comes to increasing data capacity, it's important to identify your sources of data, map the existing data to key metrics, and identify what data you're not capturing.
"Activity data" is generated from a person's actions or behaviors. The data can include information about what a person does, how they do it, and when. Various methods can be used to collect activity data, such as self-report surveys, electronic monitoring devices (e.g., wearable fitness trackers), or expert observations.
Activity data can be used to understand various behaviors and actions, including physical activity levels, work habits, leisure activities, and more. It can also track progress over time, identify trends and patterns, and inform decision-making.
Activity data can be particularly useful for organizations and researchers in health, wellness, and human performance. This is because it can help to inform the development of programs and interventions to promote healthy behaviors and improve performance.
Output data refers to data generated from an activity or process. It is typically the end result of a process or action and can be used to measure the effectiveness or efficiency of that process.
Output data can take many forms, depending on the nature of the activity or process being measured. For example, in a manufacturing setting, output data might include the number of units produced, the quality of the units produced, or the time it took to produce them. In a service-based organization, output data might include data about customer satisfaction, response times, or the number of transactions processed.
Output data is often used to track progress and measure the effectiveness of an activity or process. It can also be used to identify trends and patterns and to inform decision-making.
Overall, output data is an invaluable type of data that can help organizations and individuals understand the results of their efforts, identify areas for improvement, and make informed decisions about allocating resources and optimizing processes.
Outcome data measures the results or impact of a program, intervention, or other types of activity. It is typically used to assess whether a particular activity or intervention has achieved its intended goals or objectives.
Outcome data can take many forms, depending on the nature of the activity being evaluated. For example, in the context of a healthcare intervention, outcome data might include data about changes in patient health status, quality of life, or mortality rates. In a social program, outcome data might include changes in participants' income, employment status, or education level.
Outcome data is often collected through standardized measures or assessment tools, and it can be collected at multiple points to track progress and evaluate the long-term impact of an intervention.
Overall, outcome data is an essential type of data that helps organizations and individuals to understand the results and impact of their efforts and to make informed decisions about how to allocate resources and design programs.
Guide to Select Impact Data
When collecting data, it is essential to consider the data sources used. For example, suppose data is being collected in multiple formats such as Google Spreadsheets, Excel, Salesforce, etc. In that case, ensuring that the data is consistent and comparable across all sources is essential. This can be achieved by defining a clear set of metrics and ensuring that data is collected in a way that is consistent with these metrics. However, how do you decide on an ideal source of data? Data sources can be sitting in many sources. Before we look at different data sources, let's understand the use cases below.
Demographic data refers to statistical information about a population, such as its size, composition, and characteristics. For example, demographic data can include information about a population's age, gender, race, ethnicity, education level, income, employment status, and other characteristics.
Demographic data is often collected through surveys, censuses, or other methods. It is used to understand the characteristics and makeup of a population, and it can be helpful for various purposes, including policy-making, program planning, market research, and more.
Demographic data can help organizations and policymakers understand a particular population's needs and characteristics and design programs and policies tailored to those needs. It can also identify trends and patterns within a person and track changes over time.
Overall, demographic data is an essential type that helps organizations and policymakers understand and respond to a population's needs and make informed decisions about allocating resources and designing programs.
Stakeholder data refers to data relevant to stakeholders' interests or concerns in an organization or project. Stakeholders are individuals or groups with a vested interest in an organization or project's success or failure, including employees, customers, shareholders, vendors, partners, and other groups.
Stakeholder data can take many forms, depending on the stakeholders' nature and interests. For example, stakeholders may be interested in data about an organization's financial performance, customer satisfaction, employee retention, or environmental impact.
Collecting and analyzing stakeholder data is essential for a variety of reasons. It can help organizations understand their stakeholders' needs and concerns, identify areas for improvement, and make informed decisions about allocating resources and designing programs. It can also help organizations to build trust and credibility with their stakeholders by demonstrating transparency and a commitment to meeting their needs and concerns.
Stakeholder data is an essential type that helps organizations understand and respond to their stakeholders' needs and concerns and build trust and credibility with these groups.
Capacity Building or Training Data
The capacity building or training data refers to data that is collected and analyzed to assess the effectiveness of capacity building or training programs. Capacity building refers to helping individuals, organizations, or communities develop the skills, knowledge, and resources they need to achieve their goals and improve their performance. Training refers to teaching specific skills or knowledge to individuals or groups.
Capacity building and training data can be collected through various methods, including surveys, focus groups, interviews, and observations. It can include data about participants' knowledge and skills before and after a training program, as well as data about the impact of the training on their behavior, performance, or outcomes.
Collecting and analyzing capacity-building or training data is essential for various reasons. It can help organizations understand their programs' effectiveness and identify areas for improvement. It can also help organizations demonstrate the value and impact of their programs to stakeholders, such as funders and policymakers.
Overall, capacity-building or training data is an essential type of data that helps organizations understand and measure their programs' impact, make informed decisions about allocating resources, and design practical training and capacity-building initiatives.
Qualitative data is a type of data that is collected and analyzed in a non-numeric or non-quantitative form. It is often used to describe, interpret, or understand experiences, behaviors, and other phenomena that are difficult to quantify or measure using numerical data.
Qualitative data can take many forms, including words, images, sounds, and other types of sensory information. It is often collected through interviews, focus groups, observations, and document analysis.
Qualitative data is helpful for understanding complex phenomena and exploring the meaning and context behind people's actions and behaviors. It can also identify patterns, trends, and themes within a dataset.
Overall, qualitative data is essential to provide valuable insights and help inform decision-making and program design in various settings.
Organizations not currently collecting or collecting data on paper should start with a simple prototype data collection system using tools like Excel or Google Spreadsheets. Then, as they grow and develop their programs, they may consider building more sophisticated data collection systems using no-code app platforms that allow for data collection on desktop or mobile devices. These platforms can also allow for offline data collection and the creation of relationships between different data points.
Organizations that use CRM systems like Salesforce consider enhancing their platform to better support data collection and analysis for social impact measurement. This could involve adding custom fields or integrating with other tools to allow for more sophisticated data analysis.
Stakeholder surveys are also an essential tool for collecting data on social impact. Key benefits of designing effective stakeholder surveys include gathering insights and feedback from various stakeholders, including beneficiaries, donors, and partners. High-level stakeholder survey design best practices include defining clear objectives, selecting an appropriate sample size, using reliable and valid measures, and ensuring that the survey is accessible and easy for all stakeholders.
Data collection software
A data collection system is a system or process for collecting and storing data. Data collection systems can take many forms, depending on the type of data being collected and the purpose of the collection.
Some standard data collection systems include surveys, experiments, observational studies, focus groups, and other methods. These systems can collect quantitative data, such as numerical measurements, or qualitative data, such as words, images, or other sensory information.
Data collection systems often involve using specialized tools and technologies, such as survey software, data collection apps, or sensors, to collect and store data. They may also involve trained researchers or collectors to gather and record data.
Overall, data collection systems are essential for organizations and researchers to collect and analyze data and inform decision-making and program design.
Offline Data Collection
An offline data collection system is a system for collecting data without an active internet connection. This can be useful when an internet connection is unavailable or unreliable, such as in remote or rural areas or areas with limited infrastructure.
Offline data collection systems are often used for social impact data collection, as they can allow organizations to gather data from populations that may be difficult to reach or may have limited access to technology. They can also help collect data when security or confidentiality is a concern, as data can be collected and stored on local devices rather than being transmitted over the internet.
While an open-source system like Kobo Toolbox is free and robust, that's part of a small story. Organizations must consider many other features, such as time to integrate data analytics for continuous learning and improvement.
Some popular offline data collection software tools include:
Tightly Integrated with Data Analytics
- Sopact Survey: Sopact survey is tightly integrated for organizations looking to reduce strategy, data collection (from multiple sources including Sopact Survey), and advance data intelligence platform.
Sopact Survey has many advanced capabilities. Coupled with an integrated approach, this is the best option for low-resource organizations that do not have a strong IT team and want to complete strategy to dashboard in a short time with real-time (always connected data)
Open Source and Free Offline data collection
These tools are all useful options for organizations and researchers who need to collect data in offline or low-resource settings. They offer a range of features and capabilities to support data collection and analysis.
- OpenDataKit (ODK): ODK is an open-source data collection platform that allows users to design and deploy surveys and other data collection forms on mobile devices. It can be used offline and has various data management features.
- Kobo Toolbox: Kobo Toolbox is an open-source, offline data collection platform that allows users to design and deploy surveys, forms, and other data collection tools on mobile devices. It can be used in various settings, including disaster response, health care, and environmental monitoring, and has various features for managing and analyzing data.
Paid Subscription tools
- Ona: Ona is a cloud-based data collection platform that allows users to design and deploy surveys, forms, and other data collection tools on mobile devices. It can be used offline and has various features for managing and analyzing data, including integration with other tools such as Esri's ArcGIS mapping software.
- SurveyCTO: SurveyCTO is a cloud-based data collection platform that allows users to design and deploy surveys and other forms on mobile devices. It can be used offline and has various data management features.
- Survey123: Survey123 is a survey and data collection tool developed by Esri that allows users to design and deploy surveys on mobile devices. It can be used offline and has various data management features.
Mobile Apps-Based Tool
- CommCare: CommCare is a mobile platform for data collection and service delivery developed by Dimagi. It allows users to design and deploy surveys, forms, and other data collection tools on mobile devices and can be used offline and online. It is well-suited for use in low-resource settings and has various data management features.
- App Sheet: Google App Sheet is a modest cost option for organizations with many field operators that want to aggregate all kinds of data activity, output, and outcome data. This tool differs from regular mobile data collection surveys as they act like simple case management that keeps customer/stakeholder relationships and collects data offline.
Benefits of Data Management Systems
Improved data accuracy: Data management systems allow organizations to store and access data in a structured and consistent manner, reducing the risk of errors and inconsistencies.
Enhanced data security: Data management systems often have built-in security measures to protect sensitive data from unauthorized access or tampering.
Increased efficiency: Data management systems can automate various tasks and processes, saving time and resources for staff members.
Better data analysis: Data management systems often come with tools for analyzing and visualizing data, which can help organizations make informed decisions based on data insights.
Improved donor and constituent relationships: Data management systems can help organizations track interactions and interactions with donors and constituents, allowing for more personalized and targeted communication.
Google Sheets and Microsoft Excel are popular spreadsheet programs that mid-size social impact organizations can use. Here are some pros and cons of using each program:
Pros of using Google Sheets:
Google Sheets is a cloud-based program that allows you to access your sheets from any device with an internet connection.
It allows for real-time collaboration so multiple users can work on a sheet simultaneously.
It has many built-in functions and formatting options, making it easy to perform calculations and format data.
It offers integration with other Google products, such as Google Forms and Google Maps, which can be helpful for data collection and analysis.
Cons of using Google Sheets:
- Some users may prefer the more advanced features of Microsoft Excel, such as pivot tables and macros.
- Google Sheets may not have all of the advanced features of Excel, such as support for more complex formulas and data types.
Pros of using Microsoft Excel:
- Microsoft Excel is a powerful spreadsheet program that offers many features, including pivot tables, macros, and advanced formulas.
- Excel is a well-established program that has been around for a long time, so many users are already familiar with its interface and features.
- Excel offers integration with other Microsoft products, such as Power BI and Microsoft Access, which can be helpful for data analysis and reporting.
Cons of using Microsoft Excel:
- Microsoft Excel is a desktop-based program, so you will need to install it on a computer.
- Excel does not offer real-time collaboration, so multiple users cannot work on a sheet simultaneously.
- Excel may not be as user-friendly for beginners as other spreadsheet programs, such as Google Sheets.
- Pros and cons of Salesforce for a midsize social impact organization with lower IT resources
- Salesforce is a comprehensive CRM (customer relationship management) platform widely used in the social impact sector. Here are some pros and cons of using Salesforce for a midsize social impact organization with lower IT resources:
Pros of using Salesforce:
- Salesforce is a robust platform that offers a wide range of tools for managing donor relationships, fundraising, and financial management.
- Salesforce is a cloud-based platform, meaning you can access data from any device with an internet connection.
- Salesforce offers real-time collaboration so multiple users can work on the platform simultaneously.
- Salesforce strongly emphasizes data security and privacy, which are essential for organizations handling sensitive data.
- Salesforce offers a range of integrations with other platforms and tools, which can help automate tasks and processes.
Cons of using Salesforce:
- Salesforce can be a complex platform to set up and manage and may require a learning curve for users who are not familiar with it.
- Salesforce can be expensive for organizations with limited resources, as it is a subscription-based service.
- Salesforce may require more IT resources than other platforms, as it is a comprehensive platform with many features and integrations.
- Ultimately, whether to use Salesforce will depend on your organization's specific needs and resources. If your organization has the IT resources and budget to support Salesforce, it can be a powerful tool for managing donor relationships and other vital data. However, if your organization has limited IT resources and is looking for a more streamlined solution, other options may be more suitable.
In conclusion, data collection tools are essential for social impact organizations to collect, manage, and analyze data to inform decision-making and drive impact. Many data collection tools are available, including online and offline tools, CRM systems, and case management platforms. The best tool for an organization will depend on its specific needs and resources, including the type of data it collects, the size and complexity of its data sets, and its budget and IT capacity. By carefully considering its needs and choosing the right tools, a social impact organization can effectively collect, manage, and analyze data to drive impact and achieve its mission.
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