Grant data is a valuable resource for assessing the impact, effectiveness, and sustainability of grant-funded projects. However, grant data can also be messy, inconsistent, and incomplete, making it hard to analyze and report on. How can you clean and transform grant data for analysis? Here are some tips and tools to help you improve the quality and integrity of your grant data.
The first step to clean and transform grant data is to identify the common data issues that affect your data sources, such as missing values, duplicates, outliers, errors, inconsistencies, and irrelevant or outdated information. You can use descriptive statistics, data visualization, and data validation techniques to inspect your data and detect any anomalies or problems. For example, you can use pivot tables, histograms, or box plots to summarize and explore your data, or you can use formulas, filters, or conditional formatting to check for errors or discrepancies.
The next step is to resolve the data issues that you identified, using appropriate methods and tools. Depending on the nature and severity of the issue, you may need to delete, replace, correct, merge, split, or standardize your data. For example, you can use functions, formulas, or scripts to fill in missing values, remove duplicates, or fix errors, or you can use tools like Excel, Google Sheets, or OpenRefine to manipulate, transform, or format your data. You should also document your data cleaning steps and decisions, and keep backup copies of your original and cleaned data.
The third step is to integrate and align your data from different sources, formats, or systems, to ensure that your data is consistent, compatible, and comprehensive. You may need to convert, map, or join your data, using common identifiers, standards, or schemas. For example, you can use tools like Power Query, SQL, or R to import, query, or merge your data from various files, databases, or APIs, or you can use tools like Data Studio, Tableau, or Power BI to connect, visualize, or analyze your data from different platforms or applications. You should also verify that your data integration and alignment processes preserve the accuracy and integrity of your data.
The fourth step is to enhance and enrich your data with additional information, insights, or features, to make your data more useful, meaningful, and relevant for analysis. You may need to create, calculate, or extract new data elements, using formulas, functions, or algorithms. For example, you can use tools like Excel, Google Sheets, or R to create new variables, indicators, or metrics from your existing data, or you can use tools like Google Maps, Geocoding API, or Census API to add geographic, demographic, or socioeconomic data to your data. You should also validate that your data enhancement and enrichment processes improve the quality and value of your data.
The fifth step is to organize and structure your data in a way that facilitates analysis and reporting, using best practices and conventions. You may need to arrange, label, or classify your data, using tables, columns, rows, headers, or categories. For example, you can use tools like Excel, Google Sheets, or R to organize your data in a tabular format, with one row per observation and one column per variable, or you can use tools like Airtable, Notion, or Trello to structure your data in a relational, hierarchical, or kanban format. You should also ensure that your data organization and structure processes follow the principles of tidy data and data governance.
The final step is to review and validate your data before you use it for analysis and reporting, using quality assurance and quality control techniques. You may need to check, test, or audit your data, using rules, criteria, or standards. For example, you can use tools like Excel, Google Sheets, or R to review your data for accuracy, completeness, consistency, and relevance, or you can use tools like Data Quality Tester, Dataiku, or DataRobot to validate your data for quality, reliability, and validity. You should also solicit feedback and input from your stakeholders, partners, or beneficiaries, to ensure that your data meets their expectations and needs.
I would like to add that grant writing teams should consist of professionals with diverse strengths so the grant is written, implemented and finalized in an efficacious and cohesive manner. Not all grant writers are data analysis mavens, so it’s important to select team members who are data experts and who understand the objectives of the proposal and how those goals will be formally evaluated at completion. Also, not all grant reviewers are data mavens, so when presenting final results, one should do so with easy to follow graphs, charts, and written summaries that leave technical jargon and acronyms out of the report and presentation. By so doing, the team can clearly illustrate that the end goals have been met.