What is your experience with data analysis?

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Data analysis is a valuable skill for any social impact professional, as it helps you measure, evaluate, and communicate the outcomes and impacts of your projects, programs, or policies. Whether you are applying for a job, pitching an idea, or reporting to a stakeholder, you need to demonstrate your ability to collect, analyze, and present data in a clear and compelling way. In this article, you will learn about some of the common types of data analysis, the tools and methods you can use, and the best practices for sharing your findings.

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1 Types of data analysis

Different types of data analysis can be used depending on the purpose, data source, and complexity. Descriptive analysis is a common one, which involves summarizing and describing the main features and patterns of the data. This can help answer questions such as "What happened?", "How many?", or "How often?". Inferential analysis involves drawing conclusions and making predictions based on the data, while exploratory analysis helps discover new insights and relationships in the data. Lastly, prescriptive analysis recommends actions and solutions based on the data. All of these types of analysis can provide valuable insights to help answer questions such as "Why did it happen?", "What will happen next?", or "What should we do?".

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2 Tools and methods for data analysis

Depending on the type and size of the data, and the level of sophistication required, you can use different tools and methods for data analysis. Excel is a widely used spreadsheet software which allows for basic calculations, functions, formulas, charts, and tables; it is particularly useful for simple and quick data analysis of small and structured data sets. R is a programming language and environment that enables advanced statistical analysis, data manipulation, visualization, and modeling; it is especially effective for complex and rigorous analysis of large and unstructured datasets. Python is another programming language with a variety of libraries and packages for data analysis; it is ideal for flexible and versatile analysis, particularly for machine learning and AI applications. Tableau is a software platform that creates interactive dashboards and reports for data visualization; it is effective for exploratory and prescriptive analysis, especially when presenting findings to different audiences.

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3 Best practices for data analysis

No matter the type, tool, or method of data analysis, there are certain best practices that should be followed for quality, validity, and reliability of your results. Firstly, you should have a clear and specific question and hypothesis that guide your analysis and help you interpret your results. Secondly, you must select a data source and sample that are relevant, representative, and reliable for your research question and hypothesis. In addition, you should check for errors, inconsistencies, or missing values in your data and transform it to make it suitable for analysis. Thirdly, you should choose and apply the appropriate analytical technique for your data type and research question. Lastly, you should interpret and report your results in a clear and concise way with charts, tables, graphs or narratives. Highlighting the key findings, limitations, and implications of your analysis is also essential.

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4 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?

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Adam Rose

Senate Intern | United Nations Fellow | DEI & Social Impact Advocate
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?? Elevating Social Impact Analysis: From Data to Business Metrics ?? In the realm of social impact and ESG investments, bridging the gap between quantitative and qualitative data enhances the compelling nature of your analysis. Given the nebulous nature of concepts like CSR and DEI, incorporating recognized standards like the GRI is beneficial. However, leveraging common business metrics and methods such as ROI, MTFB, SWOT, and VUCA adds a layer of clarity. Approaching social impact akin to a business, using familiar KPIs and methods, not only makes the analysis more understandable but also increases its appeal to a broader audience, particularly potential partners and financiers.

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Paulo Bega

Ciência de dados | Business Intelligence (BI) | Gest?o de Projetos | Gest?o Estratégica | Administrador
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Na minha perspectiva o mais importante é o conhecimento do negócio, com esse conhecimento a análise de dados fica bem mais fácil. Com conhecimento do negócio é possível identificar o que realmente é importante, e por consequência, entregar análises com contextos, ou seja, algo que as pessoas do negócio realmente enxergar?o valor, caso contrário, poderá ser mais uma entrega que n?o foi entendida ou que tem pouca serventia. Afinal, toda entrega relacionada a dados deve passar pelo seguinte crivo de relacionamento: Aumentar receita, otimiza??o de processos ou redu??o de despesas. Se a entrega relacionada a dados n?o passar por nenhum desses crivos, dificilmente terá impactos significativos na organiza??o ou projeto.

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What is your experience with data analysis?

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1 Types of data analysis

2 Tools and methods for data analysis

3 Best practices for data analysis

4 Here’s what else to consider

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