Familiarising Yourself with Data (& Why That’s Not as Scary as You Think)
Jul 9, 2026
In this session, Ibrahima Ball, International Programs Data Analyst at Unbound, and Davide Gallo, founder of Vetta, presented a simple, people-centered approach to using data.
The objective was not to turn participants into specialists or teach complex formulas. The session sought to strengthen three basic capabilities: asking better questions, looking for evidence, and making better-informed decisions.
The speakers reminded participants that data is not limited to numbers, tables, or charts. It also includes observations, survey responses, photographs, conversations, testimonies, and experiences that help us understand families and communities.
DATA REPRESENTS PEOPLE
Ibrahima explained that when he began working with Poverty Stoplight information, he initially viewed each record as a number within a database.
His perspective changed after visiting an Unbound program in Honduras and observing how information was collected through conversations inside families’ homes.
“Behind every data point, there is a real story.”
Indicators represent concrete experiences, decisions, and conditions. Analyzing data therefore requires keeping in mind the context in which it was collected and the people it represents.
A field visit is not the only way to incorporate this context. It can also be obtained by speaking with facilitators, asking why an indicator changed, or finding out which intervention was implemented in a community.
The answers received are also data and can explain what is not visible on a dashboard.
WHY DOES DATA SEEM INTIMIDATING?
At the beginning of the session, participants shared the ideas and emotions they associated with the word “data.” Concepts such as evidence, information, opportunities, charts, confusion, pressure, and responsibility emerged.
Several people noted that data can seem:
• Too technical.
• Reserved for specialists.
• Difficult to interpret.
• Risky when working with sensitive information.
• Overwhelming because of the number of figures involved.
• Complicated because it is not always clear where to begin.
Ibrahima acknowledged that even people who work with data every day feel pressure because of the possibility of making mistakes. A number published in a report or used to make decisions can have consequences.
However, the problem often comes from believing that it is necessary to understand databases, formulas, methodology, statistics, and technological tools all at once.
The session proposed beginning in a much simpler way: with a good question.
SOME MYTHS ABOUT DATA
Davide presented several common ideas that make data appear more complex than it really is.
“Data is only numbers” Data can be any fact that can be recorded. A written response, photograph, audio recording, transcript, or a person’s mood can also become evidence.
Artificial intelligence has expanded the ability to analyze information in formats that were previously more difficult to process, such as images, audio, and long texts.
“A number proves the truth” A number offers one perspective, but it needs context.
An increase of 300 percent may appear extraordinary, but it could represent growth from one case to four. Without knowing the starting point, the number can create a misleading impression.
“Data is evidence, not the complete truth.”
“Sophisticated tools are necessary” A spreadsheet may be enough to get started. A database is essentially information organized into rows and columns.
Specialized systems become useful as the amount or complexity of data increases, but they are not required to ask questions, identify patterns, or make decisions.
“Only technical people can work with data” Specialists can organize, clean, or connect information. However, the people who understand the program and the communities are the ones who can interpret what the results mean.
A dashboard does not replace the experience of those who understand the context.
“More data always produces better answers” A large amount of information can create noise and make it harder to identify what matters.
The question should not be how much data can be collected, but which data is necessary to make a decision.
STEP ZERO: BEGIN WITH A QUESTION
The first step comes before opening a database or creating a chart.
Ibrahima recommended formulating the question in simple language. Organizations often begin with broad questions such as:
• Is the program working?
• Is the situation of families improving?
• What is happening with poverty?
• What are the trends?
These questions can serve as a starting point, but they need greater precision.
A useful question should include three elements:
• Who: The population, project, or territory to be analyzed.
• What: The change or indicator to be observed.
• When: The period to be compared.
For example, instead of asking, “Is the program working?” the question could be:
“Did the number of red income indicators decrease between the two most recent assessments conducted in Guatemala?”
Another question could be:
“How many families reduced at least one red indicator during the past twelve months?”
Precise questions help identify which information is needed and prevent organizations from analyzing large amounts of data without a clear purpose.
STEP ONE: IDENTIFY WHERE THE EVIDENCE IS LOCATED
After formulating the question, it is necessary to identify where the answer may be found.
Data may be located in:
• Surveys.
• Project management systems.
• Customer service platforms.
• Financial records.
• Photographs.
• Audio recordings.
• Reports.
• Case studies.
• Conversations and testimonies.
Davide explained several technical terms used at this stage.
Data migration This means moving information from one place to another. For example, transferring responses from paper surveys into a spreadsheet or moving records from one platform to a central database.
Data warehouse This is a central space where information from different sources is brought together. Conceptually, it can be imagined as a large, organized folder where the information needed for analysis is stored.
API This is the mechanism that allows two systems to exchange information.
Davide compared it to a language used by computer programs. Just as people request information through a conversation or email, applications use an API to request and receive data.
LLM A large language model receives words, documents, images, or instructions and generates a response.
It can be used to summarize texts, classify information, or transform data, but its results must be reviewed by people.
STEP TWO: MAKE THE DATA USABLE
Collected information is rarely completely ready for analysis.
It may contain:
• Empty fields.
• Duplicate records.
• Names written in different ways.
• Dates in different formats.
• Numbers stored as text.
• Variations in currencies or units.
• Categories that mean the same thing but have different names.
For example, one system may record “United Kingdom,” another “Great Britain,” and another “UK.” A person can easily recognize that they refer to the same country, but a computer may interpret them as different categories.
This stage consists of cleaning, organizing, and standardizing the information so that records can be compared correctly.
Quality also depends on how the data is collected. Clear questions and well-designed forms reduce problems later in the process.
STEP THREE: VISUALIZE, DECIDE, AND LEARN
Once the information has been organized, it can be represented through charts, dashboards, reports, or highlighted figures.
The objective is not to produce the most sophisticated visualization, but to answer the initial question clearly.
After interpreting the evidence, the organization can make a decision. That decision usually generates new questions, so the process begins again:
• Formulate a question.
• Identify the sources.
• Organize and clean the data.
• Analyze and visualize.
• Decide.
• Learn and formulate a new question.
Data is not a final product. It is part of a continuous learning process.
THE ROLE OF ARTIFICIAL INTELLIGENCE
The speakers emphasized that AI can help organize large amounts of information, analyze texts, identify patterns, and perform tasks that previously required more time.
It can also make tools that were once reserved for people with technical expertise more accessible.
However, it should not be used to interpret data automatically and without supervision.
“Human judgment is still necessary.”
AI may indicate that an indicator improved, but it may not know why the change occurred. Understanding this may require speaking with the local team, reviewing the interventions, or learning about the families’ experiences.
Technology can process information, but people contribute context, judgment, and responsibility.
QUESTIONS AND ANSWERS FROM THE AUDIENCE
• How should we decide which type of chart to use?
A participant asked whether there is a guide for determining when to use bar charts, percentages, stories, diagrams, or pie charts.
Ibrahima explained that the choice depends on the question and how quickly the answer needs to be understood.
A pie chart may be useful for showing a simple distribution between red, yellow, and green. However, it would not be appropriate for comparing fifty indicators because it would be difficult to read. In that case, a bar chart would be clearer.
Line charts are often useful for observing changes over time.
Davide added that a visualization always represents one perspective. In some cases, it may be useful to use several charts to understand the information from different angles.
The best answer may also not be a chart, but rather a highlighted figure or a story that explains the context.
• Could a practical session be conducted using real data?
Julia Corvalán, Poverty Stoplight Global Operations Manager, proposed organizing a follow-up session that would walk participants through the complete process using concrete information, from the database to interpretation.
The speakers expressed their willingness to develop a practical exercise and prepare a brief resource outlining the main steps.
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You can learn more about the upcoming Cerrito Forum 2026, the annual international global development event that brings together global stakeholders committed to the elimination of poverty from October 12th - 16th by visiting cerrito.com.py.
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