How to Clean & Analyze ODK Data in Power BI & Tableau: A Data Analyst’s Guide

Raw ODK (Open Data Kit) data is messy—nested JSON, inconsistent formats, and test submissions clutter your dataset. As someone who’s cleaned hundreds of ODK surveys, I’ll show you exactly how to transform raw submissions into analysis-ready data in Power BI and Tableau.

By the end, you’ll know how to:
✔ Fix common ODK data issues (GPS, repeats, metadata).
✔ Prepare data for visualization.
✔ Build automated pipelines for ongoing projects.

Let’s dive in!

Step 1: Fix Common ODK Data Issues

Problem 1: Nested JSON Structures

Why it happens: ODK stores repeat groups and complex questions as nested JSON.
Solution:

• In Power BI:
  • Use Power Query’s “Expand” button to flatten columns.
  • Watch for list structures (e.g., { "answer": "Yes" } → extract “answer”).
• In Tableau:
  • Use Tableau Prep’s “JSON Parse” function.
  • Or pre-process with Python/R if nested deeply.

Problem 2: GPS Coordinates in Weird Formats

Example: "12.3456 -1.2345 accuracy=5"
Fix:

• Power BI: DAXCopyLatitude = VALUE(LEFT([GPS], FIND(” “, [GPS]) – 1)) Longitude = VALUE(MID([GPS], FIND(” “, [GPS]) + 1, FIND(” “, [GPS], FIND(” “, [GPS]) + 1)))
• Tableau:
  • Use SPLIT() or REGEX in a calculated field.

Problem 3: Test Submissions Cluttering Data

Fix: Filter out dummy entries:

• Power BI/Tableau:
  • Add a filter: [SubmitterName] <> "demo_user".
  • Or exclude records with [SubmissionDate] < project start date.

Problem 4: Categorical Data as Free Text

Example: “Yes/No” answers typed as “Y”, “N”, “YES”, “NO”.
Fix: Standardize values:

• Power BI: DAXCopyCleaned_Answer = SWITCH( UPPER([Answer]), “Y”, “Yes”, “N”, “No”, [Answer] )
• Tableau:
  • Use GROUPING in the data pane to merge categories.

Step 2: Prepare Data for Analysis

A. Handling Repeat Groups (e.g., Household Members)

Power BI:

1. Expand repeats into separate rows.
2. Use GROUPBY or SUMMARIZE to aggregate:DAXCopyHousehold_Count = COUNTROWS(FILTER(Data, [HouseholdID] = EARLIER([HouseholdID])))

Tableau:

• Use LOD (Level of Detail) expressions:Copy{ FIXED [HouseholdID] : COUNTD([MemberName]) }

B. Merging Multiple ODK Forms

Example: Baseline + Endline surveys.

• Power BI: Append queries in Power Query.
• Tableau: Union data sources or use Tableau Prep.

C. Calculating Survey Metrics

Example: Response Rates

• Power BI:DAXCopyResponse_Rate = DIVIDE([Completed Surveys], [Total Surveys], 0)
• Tableau:
  • Create a calculated field:CopySUM(IF [Status] = “Complete” THEN 1 ELSE 0 END) / COUNT([SurveyID])

Step 3: Build Automated Dashboards

Power BI Template

1. Parameters for Dynamic Filtering:
   • Let users toggle between “Baseline” and “Endline” data.
2. Bookmarks for Navigation:
   • Switch between “Map View” and “Detailed Tables.”

Tableau Dashboard Tricks

1. Use Actions to Drill Down:
   • Click a region → filter to villages.
2. Embedded Data Quality Checks:
   • Add a sheet highlighting missing GPS coordinates.

Real-World Example: Health Survey Analysis

I used this process to:

1. Clean 10,000+ ODK submissions from clinics.
2. Build a real-time dashboard tracking:
   • Vaccine coverage by district.
   • Data quality flags (missing/outlier values).
3. Reduced manual cleaning time from 8 hours to 20 mins/week.

Pro Tips for Ongoing Projects

• Schedule Refreshes: Power BI Gateway or Tableau Server.
• Use ODK Central’s “Review” Tab: Flag issues before export.
• Document Your Steps: Save Power Query/Tableau Prep flows for reuse.

Final Thoughts

ODK data is powerful—but only if you clean it right. Start with small datasets, automate where possible, and always validate results.