Evaluator
Student
Development Sector Specialist
Academic Institutions
Data Analysts
Ethnographic Researcher
Data Enthusiasts
Evaluator
From field data to donor story — in one session
The situation
Kareem coordinates a maternal health programme. He has survey data from 400 beneficiaries and transcripts from 22 interviews — and a donor report due in two weeks. He needs to show what the numbers mean and what beneficiaries actually said, in a format non-technical stakeholders can read on their phones.
Analyze
Survey data and interview transcripts in one session
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Interpret
Sense Making ties quant and qual findings together
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Canvas
Charts and participant quotes in a public data story
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Share
Live dashboard link sent to the donor before the call
The workflow
  1. Loads the survey and runs Descriptive Analysis — frequency tables and a crosstab of antenatal care visits by district.
  2. Uploads 22 transcripts into Qualitative Analysis, builds a codebook, and codes segments across all interviews.
  3. Uses Comparative Analysis to see how beneficiaries and staff differ in how they describe programme impact.
  4. Opens Data Visualization, builds a bar chart and donut chart, assembles a Multi-Chart Dashboard, and shares it as a live link.
  5. Uses Sense Making to synthesize the quantitative and qualitative findings into one coherent narrative.
  6. Clicks Add to Canvas on the two key charts and two participant quotes. Opens Data Canvas, uses Draft all headlines to generate plain-language insight titles, and publishes as a public link.
The result: A live dashboard for the debrief call, a Sense Making narrative for the report, and a public Data Canvas story the trustees can read on their phones — all from one session.
Postgraduate Student
Run the analysis, get the write-up — without the software headache
The situation
Lojayn has 300 survey responses for her dissertation, a deadline approaching, and no statistics software. She needs to run the analysis, understand what it means, produce a publication-style write-up, and share findings with her supervisor.
Analyze
Frequency tables, crosstabs, and regression — guided
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Interpret
Sense Making drafts a publication-style write-up of results
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Canvas
Key findings assembled into a shareable story
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Share
Dashboard link shared with supervisor for review
The workflow
  1. Uploads her data and runs Descriptive Analysis — frequency tables and summary statistics — with plain-English interpretation at every step.
  2. Runs a crosstab and chi-square test to check the relationship between her key variables.
  3. Runs linear regression in the Inferential module. AnalyZ explains each coefficient in plain language: "For every one-unit increase in X, Y increases by 0.43 — a statistically significant relationship."
  4. Opens Sense Making, selects Academic mode, and generates a publication-style write-up covering findings, discussion, conclusions, and limitations — based on her actual output tables.
  5. Builds a dashboard with three key charts and shares the live link with her supervisor for feedback.
  6. Adds the key table and chart to Data Canvas and publishes as a story for her thesis committee.
The result: Analysis done, a publication-style write-up drafted, findings shared — without wrestling with syntax or software.
Development Sector Specialist
Client findings delivered — before the meeting starts
The situation
June needs to present client findings to a board on Friday. She has the data, a tight deadline, and a client who expects a polished deliverable — not a spreadsheet attachment.
Analyze
Client data profiled, cleaned, and visualised
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Interpret
Sense Making generates a narrative write-up of results
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Canvas
Results assembled into a client-facing story
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Share
Dashboard link sent the night before the board meeting
The workflow
  1. Loads the client dataset, runs Descriptive → Summary Statistics and a publication table broken down by key segments.
  2. Builds interactive charts in Data Visualization — customises colours to match the client's brand, exports PNG for the slide deck.
  3. Assembles a Multi-Chart Dashboard with cross-filtering and publishes as a live link — sends it to the client the night before.
  4. Opens Sense Making, selects Executive mode, and generates a narrative write-up covering findings, discussion, and conclusions — ready to paste into the client report.
  5. Adds key charts and tables to Data Canvas, uses Draft all headlines to generate plain-language insight titles, and publishes as a public story.
The result: A live dashboard, a narrative write-up, and a client-facing data story — delivered before the deadline.
Academic Institutions
Identify predictors of student dropout risk
The situation
Dr. Mohanty heads the Office of Research at a university. The Vice-Chancellor wants a brief on which factors predict whether a first-year student will drop out — backed by data.
The workflow
  1. Loads the student-records dataset (3,400 students, 24 variables) and runs a Descriptive → Table of dropout status across academic background, family income, GPA, and entrance score.
  2. Switches to Inferential → Logistic Regression. AnalyZ explains: "Each one-point increase in first-semester GPA decreases odds of dropout by 38%."
  3. Builds a dashboard with key charts and shares the live link with the Vice-Chancellor.
  4. Uses Sense Making to produce a plain-language summary of findings for the one-page brief.
The result: A Word table, a live dashboard, and a plain-language write-up — ready for the brief.
Data Analysts
Clean a messy dataset and produce a profile report
The situation
Paul has received a 12,000-row customer survey with inconsistent codes, missing markers, and duplicates. He needs a clean dataset and an exploratory profile — fast.
The workflow
  1. Loads the CSV and opens Data Management — recodes variables, removes duplicates, and drops incomplete rows.
  2. Runs Descriptive Analysis: frequency tables and summary statistics across every variable.
  3. Runs a Data Quality Check to surface missing value patterns and outliers.
  4. Builds a dashboard with key distribution charts — shares the live link with the project team.
  5. Uses Sense Making to generate a narrative summary of the data profile.
The result: A cleaned dataset, an Excel profile report, a live dashboard, and a narrative summary — all exported from one session.
Ethnographic Researchers
Analyze interview transcripts from a programme evaluation
The situation
Sukesh is leading a mid-term evaluation. He has 18 key informant interviews and 4 focus group discussions. The donor wants evidence of effectiveness — from both the survey data and beneficiary voices.
The workflow
  1. Uploads all 22 transcripts into Qualitative Analysis, builds a codebook with evaluation criteria: Relevance, Effectiveness, Efficiency, Sustainability, Impact.
  2. Uses Comparative Analysis — finds staff are far more optimistic about sustainability than beneficiaries.
  3. Checks Co-occurrence — "Impact" and "Attribution" rarely appear together, suggesting respondents struggle to attribute outcomes to the programme.
  4. Uses Code Saturation to confirm 18 transcripts was enough.
  5. Uses Sense Making to synthesize qualitative and quantitative findings into one evaluation narrative.
  6. Opens Data Canvas, adds the comparative chart and two participant quotes, and shares as a public story with the donor.
The result: A structured evaluation analysis, a Sense Making narrative, and a public mixed-methods story — from a single platform.
Data-Curious Professionals
Test a hunch about workplace patterns
The situation
James manages People Operations at a tech company. He's noticed engineers from certain teams leave within their first year more often — but he doesn't have a statistics background.
The workflow
  1. Exports HR records and opens AnalyZ. Runs Descriptive Analysis — frequency table of departures by team, summary statistics of tenure.
  2. Runs Logistic Regression. AnalyZ explains: "Engineers in Team B have 2.4× the odds of leaving compared to Team A — even after controlling for role and salary."
  3. Builds a bar chart of average tenure by team and shares the dashboard with his manager.
  4. Uses Sense Making to generate a plain-language summary he can paste into an internal memo.
The result: A clear answer backed by a chart, a verified result, and a plain-language summary — without needing a statistics background.