Define your Problem to Get the Best out of your Data

Why start with a question?

Before any data collection or analysis, there is one essential step: clearly defining the question that the data will need to answer.

For organizations, this means aligning data projects with field requirements, management objectives, or reporting obligations. This scoping phase prevents wasting time on useless data that is poorly exploited or disconnected from the realities of the organization.

1. How will the data be used?

Start by asking yourself the question of “business need”: what problem are we trying to solve? What decision needs to be supported?

Some examples:

• Improving support for beneficiaries

• Measuring the impact of an action or project

• Better management of resources (human, financial)

A poorly formulated need often leads to unsuitable indicators or unnecessary data collection.

2. For whom?

Data must always have a target audience. Who needs it? For what purpose?

Depending on the profile of the recipients (management, field teams, funders, partners), expectations differ:

• Raw data?

• Visually appealing dashboards?

• Summary reports?

This also determines the form of presentation, the level of detail, and even the need to anonymize certain information.

3. which indicators to choose?

Once the need and the audience have been clarified, the right indicators can be determined.

A few principles:

• Prioritize simplicity: a simple, well-understood indicator is better than a complex but vague one.

• Choose actionable indicators: ones that enable decision-making or action

• Distinguish between management indicators (internal) and valuation indicators (external)Distinguish between governance indicators (internal) and promotional indicators (external)

Examples: satisfaction rate, average turnaround time, number of people supported, budget spent, etc.

4. Over what period? How often?

All data analysis is based on a clear time period.

• Period to be analyzed: the last 6 months? The current year? The complete history since 2019?

• Update frequency: once a year? Every month? Continuously?

These choices influence the volume of data to be anticipated, the workload, the level of detail, and the frequency of reports.

5. Align data with the organization's strategy

Data strategy should not exist in silos. It must:

• Serving the organization's mission and goals

• Enhance transparency towards partners and sponsors

• Improve decision-making and day-to-day management

A relevant data project is one that is connected to the reality on the ground, the teams, and the ambitions.

To sum up

Steps to follow:

Why? : What is the business need or purpose of the project?

For whom? : Who will use or read the data?

What? : Which indicators are most useful?

When? : What period? How often?

Alignment: Is this consistent with the organization's objectives?

Properly defining the challenge is key to laying the groundwork for targeted data collection, useful analysis, and effective data utilization.