Opening Data on Countries & Global Issues: Value Guide

Updated 5 November 2026 · Standard formula, shown below

This guide explains how opening data on countries and global issues creates value for researchers, policymakers, journalists, and citizens. You will learn a practical workflow for finding, cleaning, analyzing, and communicating open data, turning raw numbers into insights that can inform decisions, spark accountability, and drive change. No prior data science experience is required, but basic spreadsheet skills help.

What You’ll Need

A computer with internet access
A spreadsheet program (Excel, Google Sheets, or LibreOffice Calc)
A code editor or text editor (optional, for more advanced work)
Basic familiarity with CSV or Excel file formats
Patience and curiosity

Step-by-Step

Identify a specific question or problem. Value comes from purpose. Instead of downloading everything, start with a question like “How has child mortality changed in Sub-Saharan Africa since 2000?” or “Which countries have the highest income inequality?” A clear question guides your data choices.
Find reliable open data sources. Major repositories include the World Bank Open Data, UN Data, FAOStat, WHO Global Health Observatory, Our World in Data, and national statistics offices. For cross-country comparisons, the World Bank and UN are the most comprehensive. Search their portals using keywords from your question.
Download the data in a machine-readable format. Prefer CSV, XLSX, or JSON files over PDFs or HTML tables. Most portals offer a “Download” button. If you need multiple indicators, use the portal’s bulk download or API if available. Save the file with a descriptive name including the date.
Open the file in a spreadsheet. Double-click the downloaded file or use File > Open in your spreadsheet program. Inspect the first few rows. Note the column headers, the unit of measurement, and the time period covered. Check for footnotes or metadata files that explain definitions.
Clean the data. Look for missing values (often shown as blank cells, “NA”, or “..”). Decide how to handle them: you can leave them blank, mark them as missing, or drop rows with too many missing values. Remove duplicate rows. Ensure numbers are stored as numbers, not text (check cell formatting).
Reshape the data for analysis. Most global datasets are “long” (one row per country-year) or “wide” (one row per country, columns per year). For time-series charts, long format is easier. Use spreadsheet pivot tables or the “Unpivot” feature in Power Query (Excel) or Google Sheets’ built-in functions to reshape if needed.
Explore and summarize. Calculate basic statistics: mean, median, min, max, and standard deviation for your indicator. Use pivot tables to group by region or income level. Create simple charts (line charts for trends, bar charts for comparisons, scatter plots for relationships) to see patterns visually.
Analyze for insights. Look for outliers, sudden changes, or long-term trends. Compare countries or regions. For example, compare the trend in life expectancy between two countries to see if gaps are widening or closing. Use simple calculations like percentage change over time.
Cross-reference with other datasets. Value often emerges from connecting data. Merge your dataset with another (e.g., GDP data with education data) using a common key like country code. In spreadsheets, use VLOOKUP or INDEX-MATCH to combine columns from different sheets.
Visualize the results. Choose the right chart type: line for time series, bar for comparisons, map for geographic patterns, scatter for correlations. Label axes clearly, include units, and cite the source on the chart. Tools like Datawrapper, Flourish, or even Google Sheets charts work well.
Write a short narrative. Explain what the data shows in plain language. State the key finding, the evidence (numbers or chart), and any caveats (data limitations, missing years). This narrative is what transforms data into insight.
Share and publish. Publish your analysis as a blog post, report, or interactive dashboard. Include a link to the original data and your cleaned dataset. When you share, you enable others to verify and build on your work, multiplying the value.

Common Mistakes

Ignoring metadata: Not reading the codebook or definition sheet leads to misinterpreting units (e.g., percent vs. ratio) or what the indicator actually measures.
Comparing incompatible data: Different sources may use different definitions or collection methods. Always check if indicators are truly comparable across countries.
Overlooking missing data: Treating blank cells as zero or dropping them without consideration can bias your results.
Using the wrong time period: Mixing annual and quarterly data, or comparing different years, creates false conclusions.
Confusing correlation with causation: Just because two trends move together does not mean one causes the other.
Not citing sources: Failing to credit the data provider undermines credibility and reproducibility.

Tips

Start small: Pick one indicator and 5-10 countries before scaling up.
Use ISO country codes: Always keep the three-letter ISO code (e.g., USA, FRA, JPN) as a column; it makes merging datasets much easier.
Download the metadata file: It explains the indicator definition, source, and any caveats.
Use pivot tables aggressively: They are the fastest way to summarize and explore data without writing code.
Check for updates: Open data portals revise figures; note the version date in your file name.
Consider using the APIs: For repeated updates, learning the World Bank or UN API in Python or R saves time.

Troubleshooting

Data won’t download: Try a different browser or right-click and “Save Link As.” If the portal is down, use the Wayback Machine or an alternative source like Our World in Data.
File opens with garbled characters: The file may be encoded in UTF-8 with BOM. In Excel, use Data > From Text/CSV and choose encoding 65001: UTF-8.
Numbers are stored as text: Select the column, use Text to Columns, and choose Delimited > Tab. Or multiply by 1 using Paste Special to convert.
Missing values break your chart: In Excel, use the “Select Data” option to hide empty cells. In Google Sheets, use FILTER to exclude blanks.
Merging datasets fails: Ensure the key column (country code) has the same format in both files. Remove leading/trailing spaces with TRIM.
Your chart looks wrong: Check the axis scale. If values vary widely, use a logarithmic scale. Verify that you selected the correct data range.

Frequently Asked Questions

Q: Which open data source is best for beginners?
A: The World Bank Open Data portal is the most user-friendly, with clear search, downloadable CSV files, and built-in charts. Our World in Data is also excellent for pre-visualized, well-documented datasets.

Q: Do I need to know programming to work with open data?
A: No. Spreadsheets can handle cleaning, pivoting, charting, and basic statistics for datasets up to a few hundred thousand rows. Programming (Python/R) becomes useful for very large datasets or complex analysis.

Q: How do I handle missing data for a country?
A: First check the metadata to see why it is missing (no survey, conflict, etc.). Then decide: exclude that country-year, interpolate if the gap is small, or note it as a limitation in your narrative. Never fill with zeros unless zero is a real value.

Q: Can I use open data for commercial purposes?
A: Most major sources (World Bank, UN) release data under open licenses (e.g., CC BY) that permit commercial use with attribution. Always check the specific license on the download page.

Q: How often is the data updated?
A: It varies. The World Bank updates its main indicators annually, usually with a 1-2 year lag. Health data from WHO may update more frequently. Check the “Last Updated” date on the portal.

Q: What if the data contradicts what I expected?
A: That is often the most valuable finding. Double-check the definition and source, verify you didn’t misread units, then present the unexpected result with appropriate caveats. Contradictions can reveal data quality issues or new insights.

Method: the standard formula for this calculation, rounded as each result states. See how the calculators work or report an error.