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data science for social impact

Data science for social impact

Harness the potential of data science to make a real difference in the world. Sopact can help you turn insights into action for social impact

Data Science for Social Impact

Data Science for Social Impact refers to the use of data-driven approaches to solve social and environmental problems. At its core, it involves using data analysis, machine learning, and statistical modeling to derive insights that can inform better decision-making and drive positive change. With Sopact's SAAS-based software, you can unlock the power of data science and take a more targeted and effective approach to social impact.

But why is Data Science for Social Impact important? By leveraging data to drive decision-making, organizations can better understand the needs and behaviors of their target communities, identify gaps in existing programs and services, and design more effective interventions that can lead to measurable impact. However, achieving these benefits can be challenging, requiring specialized skills, resources, and technology.

That's where Sopact comes in. Our impact strategy app provides a user-friendly and actionable approach to data-driven social impact. Through our software, you can access a library of strategies, training, and examples to help you design and implement effective programs that drive positive change. So why wait? Watch our impact strategy video, explore our library of strategies, and start your journey toward impactful social change today!

Social Impact Data

Data science has become essential for organizations to measure their social impact. However, effective use of data science is about more than just applying data within the organization. It requires collaboration between multiple partners - internal stakeholders, external partners, funders, and Sopact. Data science involves three integrated processes: data collection, analysis, and visualization. In this article, we will explore the importance of impact data in data science and how organizations can collaborate with their partners to use impact data effectively.

Data Science for Social Impact

Diagram 1: Data Science Processes

Data science involves collecting data from various sources, analyzing it to find patterns and insights, and presenting the findings visually appealingly. However, successful collaboration must have a practical impact strategy that typically includes a logic model or theory of change. Furthermore, the logic model or theory of change must align with different types of data and data sources. The data sources include activity data, output data, outcome data, and impact data.

impact data types

Diagram 2: Impact Data Types

What is impact data?

Impact data is an organization's most critical type of data to collect. The data shows the organization's impact on society or a specific group. First, however, effective data science must connect the logic model with data sources. This typically involves using Excel, Google Sheets, mobile data, or Salesforce data. However, collecting evidence requires connecting multiple data sources in real time with a business intelligence platform using a data pipeline.

impact data sources

Diagram 3: Impact Data Sources

Impact data can be collected using different sources, including surveys, interviews, and social media. It can be used to measure the effectiveness of an organization's programs and to identify areas that need improvement. Impact data can also inform decision-making, improve organizational performance, and communicate the organization's impact to stakeholders.

impact data aggregation

Diagram 4: Impact Data Aggregation

In conclusion, impact data is essential for organizations that want to measure their social impact. Effective use of impact data requires collaboration between multiple partners and alignment with different types of data and data sources. Data science involves collecting, analyzing, and visualizing data, but successful partnerships must have a practical impact strategy that typically includes a logic model or theory of change. Finally, collecting impact data requires connecting multiple data sources in real time with a business intelligence platform using a data pipeline.

impact data collaboration

Diagram 5: Impact Data Collaboration

 

Understand the key differences between

Output vs. Outcome

Before we delve into why measuring outcomes is essential for social impact, let's define output and outcome in the context of social impact.

Output is the direct result of a program or activity. For example, if a non-profit organization provides food to needy people, their output is the number of meals provided. Similarly, if an organization offers job training, its output is the number of people who completed the program.

The outcome, on the other hand, is the impact of the program or activity. It is the result that the program achieves. For instance, if an organization provides food to needy people, the outcome could be improved health and nutrition or increased financial stability. If an organization offers job training, the outcome could be increased employment and higher wages.

Difference between output vs. outcome

Key Criteria Output Metrics Outcome Metrics
Definition Measures of tangible deliverables or results Measures of the impact or effects of outputs
Focus What was produced? What difference did it make?
Timeframe Short-term Long-term
Direct or Indirect Link Direct link to activities or inputs Indirect link to activities or inputs
Evaluation Can be evaluated based on completion Evaluation requires measuring change
Examples Number of products produced Reduction in crime rate
Importance Helps track efficiency and productivity Helps track effectiveness and progress
Limitations May not always reflect the impact of outputs May be difficult to attribute outcomes to outputs
Application Useful for monitoring progress Useful for measuring success and impact

 

Output data

Output data refers to data generated from an activity or process. It is typically the result of a procedure 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 make 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 their efforts' results, identify improvement areas, and make informed decisions about allocating resources and optimizing processes.

Outcome Data

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 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 participant income, employment status, or education level changes.

Outcome data is often collected through standardized measures or assessment tools, and it can be ordered 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 understand the results and impact of their efforts and make informed decisions about allocating resources and designing programs.

Activity data

"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, identify trends and patterns, and inform decision-making.

Activity data can benefit 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.

Outcome Metrics to Stakeholder Survey

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 the survey is accessible and easy for all stakeholders.

Stakeholder Data

Social Impact Analytics

Social impact analytics is the process of collecting, analyzing, and interpreting data to understand the impact of a program or investment on a specific social issue. It involves using data to measure and track progress, identify trends and patterns, and make informed decisions about optimizing the impact of an organization's work.

One platform that offers social impact analytics tools is Sopact's Impact Cloud. Sopact is a business impact management platform explicitly designed by corporates, nonprofits, and social enterprises looking to make data-driven decisions. It is built on an advanced open-source platform ( used by thousands of organizations, including AirB2B and many more). In addition, Sopact is unique in that it is integrated with an impact strategy product, which allows organizations to design an impact strategy, data strategy, and integrated data collection, survey, and data pipeline. This can save significant time and effort compared to using separate tools.

In contrast, platforms like Tableau, PowerBI, and Salesforce dashboards can be difficult to manage for nonprofit staff who may need more skills or resources for data analytics, data science, and impact management. In addition, these platforms may require more time and expertise to set up and maintain, which can be challenging for organizations with limited resources. Additionally, they may be designed for something other than the nonprofit sector, making it challenging to customize them for specific social impact goals.

Overall, Sopact offers a more streamlined and integrated approach to social impact analytics, which can be especially useful for nonprofits and social enterprises looking to optimize their impact and reduce the time and effort required for data management and analysis. For a growth-driven organization, we work together to build a comprehensive impact data engine that can seamlessly bring data from both disconnected and connected data. Impact Cloud is a business and impact integration engine that allows connecting data in near real-time.  As data arrives, our semantic layer can allow further enrichment for better business and impact analytics solutions.  Combined with qualitative feedback, it can be used to adapt your service.  Data that encompasses both social impact dimensions and customer data can be collected to help you understand the deep impact and operation relationships.

Use Case Key Technical Challenges
Comparing results for partners based on the baseline targets set by the project Unification of data sources from different partners; merging data based on common indicators; creating virtual tables for temporary data storage and analysis
Unifying output data, such as learning Integration of data from different systems (e.g., student information systems, learning management systems); standardizing data formats and structures; identifying and resolving data inconsistencies or errors; creating virtual tables for joining and querying data
Management system to understand student success in higher education vs. outcome for finding employment success Collection and management of data at different levels (e.g., school, district, community, country); alignment of indicators with SDG targets; addressing issues related to data quality, privacy, and security; developing tools and methods for data visualization and communication
Tracking the performance of education, healthcare, community, social, and mental healthcare programs by community and country for aligning with SDG targets Collect household surveys, align response rates based on various factors such as poverty rates in a different country and score them and align with SDG indicators.
Table: Different social impact analytics use cases

 

Advanced visualization and real-time dashboard

Most social sector organizations must have the budget and skills to execute such a project. The broad range of impact data pipeline approaches allows for continuous data integration and modern data visualization and storytelling. Effective dashboards must start with a transparent storytelling approach; the dashboard must document the theory of change, evaluation strategy, key impact management goals, and hypothesis. Finally, the effective dashboard must be comprehensive enough to focus on the most critical materiality and demonstrate impact based on “five dimensions of impact” based on impact management project (IMP) or other publicly agreed upon evaluation approaches.

charts and visualizations

 

Impact Storytelling

dashboard

Demonstrate Impact

dashboard intro

 

Enrich data with a semantic layer

Every organization has a unique understanding of social impact. The semantic layer enriches data for analytics. A semantic layer allows for proper calculation and alignment with various frameworks personalized to each organization. Some examples are:
  1. Due diligence score, for example, scoring for 2x challenge for financing for women
  2. Monitoring outcome or product traction through stakeholder data alignment with five dimensions of impact from impact management project (IMP)
  3. Comparison and benchmarking through external data comparison, statistics, and calculations
  4. Unification of data from multiple sources to understand stakeholder product adoption and social impact goals

investment-and-women-employees-by-country

 

Scoring, Benchmarking, and Calculations

An intelligent approach to unify data from different layers - partners, system, and external data.  Impact Cloud semantic layers allow building simple to complex data unification, scoring, benchmarking, and comparison.  Build filters to view all your outcomes for different locations, chapters and investments.

employee-gender-distribution

Preparing for

Impact modeling data analytics

Impact modeling is a type of data analytics that aims to understand and quantify the impact of a particular intervention or change on a system or process. It involves analyzing data and using statistical or mathematical techniques to estimate the likely effects of a given intervention or modification. Impact modeling is often used in various contexts, such as business, economics, public policy, and health care, to predict the outcomes of different decisions or actions and inform decision-making.

Impact Data Sources

Impact modeling typically involves collecting and analyzing data from multiple sources, including historical, survey, and other data relevant to the system or process being studied. The data is then used to create models that can be used to estimate the impact of different interventions or changes on the system or method. These models may be based on statistical or mathematical techniques such as regression analysis, simulation, or optimization.

Nonprofit Datawarehouse

Sopact Impact Cloud is designed based on an advanced impact modeling approach and data warehouse for organizations that cannot handle the complexity of complex data warehouses like Snowflake. It is unique in that it integrates strategy, data collection, pipeline, and analytics. Over several years we have reduced overall time from 100 hours to 10-20 hours per implementation for medium size o organizations, thanks to optimization at every layer. We also are launching an innovative strategy product that replaces strategic advisory with a collaborative approach.

Impact Data Pipeline

 

Impact modeling can help organizations and policymakers make informed decisions about allocating resources, designing programs, or implementing policies. It can also be used to evaluate the effectiveness of existing interventions or identify improvement areas.

Data Science for Social Impact

"Data science for social impact" refers to using data science techniques and technologies to address social and environmental issues. It involves collecting, cleaning, and analyzing data to understand and quantify the impact of different interventions or changes on a system or process.

Data science for social impact is crucial because it can help organizations and policymakers make informed decisions about allocating resources, designing programs, or implementing policies that have the most significant positive impact on social and environmental issues. It can also help organizations demonstrate the value and impact of their work to internal and external stakeholders, such as employees, donors, funders, and the public.

Data science for social impact can be used in various contexts, such as public policy, health care, education, and the environment, to understand and address multiple issues. For example, data science techniques can be used to analyze patterns in healthcare data to identify trends and practices that can inform the development of new treatments or policies. Data science can also be used to analyze trends in energy consumption and emissions data to develop more sustainable energy policies.

Overall, data science for social impact is an essential tool for organizations and policymakers looking to address social and environmental issues in a data-driven and evidence-based way.

Skills 

Individuals typically need a strong foundation in statistical analysis, data visualization, and programming to work in data science for social impact. Specific skills that may be helpful include:

  1. Statistical analysis: The ability to use statistical techniques to analyze data and draw conclusions from it. This includes skills such as regression analysis, hypothesis testing, and time series analysis.
  2. Data visualization: The ability to create charts, graphs, and other visual representations of data to communicate findings effectively.
  3. Programming: The ability to use programming languages such as Python, R, or SQL to manipulate and analyze data.
  4. Data cleaning and wrangling: The ability to work with messy or incomplete data and transform it into a usable form.
  5. Communication: The ability to present findings clearly and concisely, orally and in writing.

In addition to these technical skills, individuals working in data science for social impact may also benefit from strong problem-solving and critical thinking skills and the ability to work collaboratively with others.

Why social purpose programs should invest in data science

The benefits of having these skills for organizations are numerous. For example, data science for social impact can help organizations make more informed decisions about allocating resources, designing programs, or implementing policies that have the most significant positive impact on social and environmental issues. It can also help organizations demonstrate the value and impact of their work to internal and external stakeholders, such as employees, donors, funders, and the public. This can be particularly important for organizations focused on making a positive social or environmental impact, as it can help them attract funding, support, and partnerships.

How to design

Impact Report

An impact report is a document that presents the results and impact of an organization's work on social or environmental issues. It typically includes information about the goals and objectives of the organization, the programs and initiatives it has implemented, and the outcomes and impact of those efforts. Organizations can use impact reports to communicate their work to internal and external stakeholders, such as employees, donors, funders, and the public.

To design an effective impact report, it is essential to consider the following factors:

  • Audience: Who will be reading the impact report? Understanding the needs and interests of the target audience can help inform the content and design of the report.
  • Goals and objectives: What are the organization's goals and objectives, and how have they been met through the work described in the report?
  • Data and evidence: What data and evidence will be used to support the claims made in the report? Using reliable and relevant data to help the organization's impact is essential.
  • Visualization: How will the data and information be presented in the report? Using charts, graphs, and other visual elements can help make the report more engaging and easier to understand.
  • Layout and design: How will the report be structured and designed? A clear and logical layout can help make the report more readable and compelling.

Overall, an effective impact report should be well-written, clearly organized, and visually appealing and should use reliable data and evidence to support the claims made about the organization's impact.

Conclusion

Designing and modeling impact data and conducting impact data analytics and reporting are essential for organizations that want to understand and communicate the impact of their work on social or environmental issues. These processes involve collecting and analyzing data from multiple sources, using statistical or mathematical techniques to estimate the likely effects of different interventions or changes, and presenting the results clearly and concisely.

To design impact data, it is essential to consider the goals and objectives of the organization, the types of data that will be needed to support the claims made about the organization's impact, and the methods that will be used to collect and analyze the data.

It is crucial to model impact data using appropriate statistical or mathematical techniques to estimate the likely effects of different interventions or changes and to validate the results using appropriate methods.

To conduct impact data analytics, it is important to use appropriate tools and techniques to analyze the data, including visualization and statistical analysis, and to present the results clearly and concisely.

The purpose of designing, modeling, analyzing, and reporting impact data is to provide organizations with the information they need to make informed decisions about allocating resources, designing programs, or implementing policies. It can also help organizations demonstrate the value of their work to internal and external stakeholders, such as employees, donors, funders, and the public.