For years, Excel can perfectly meet an organisation's needs. One file for the budget, another for activities, a third for beneficiaries. A few formulas, some pivot tables, a handful of charts, and the reporting gets produced.
Then the organisation grows. Projects multiply, data comes in from several regions, teams use KoboToolbox, DHIS2, Excel files, accounting software or other systems. Management asks for finer-grained analysis. Donors want regularly updated information.
And gradually, the problem appears: several versions of the same file circulate, consolidation takes several days, some formulas are understood by only one person, the same indicators get calculated differently from one team to another, data has to be checked manually before every report, management gets the information several days after events happen, and a new analysis request often means rebuilding a file from scratch.
At this point, the question is no longer really whether Excel is still powerful enough. It becomes: is our data system still fit for our needs? The nuance matters, because moving to Power BI is not simply a matter of replacing Excel sheets with more modern-looking charts. It means progressively changing the way the organisation collects, structures, transforms, analyses, shares and uses its data.
The real problem is rarely Excel itself
Excel is not a bad tool. It stays extremely useful for many organisations: entering or checking data, running one-off analyses, building financial models, preparing files, doing exploratory calculations, quickly producing an internal report, or testing an indicator before rolling it out more broadly.
The problem appears when a spreadsheet is asked to simultaneously play the role of database, transformation tool, reporting system, indicator repository, collaboration tool and steering system. A file can be excellent on its own while becoming problematic once it turns into the core of a complex organisational system. So it is less Excel's power that is the issue than the way of working built up around the files.
Seven signs a file-based system has reached its limits
There is no magic number of rows or files beyond which an organisation must switch to Power BI. The best indicator remains the complexity of the need. Here are the signals that come up most often.
Several files hold the same information. One file per project, per year, per region or per partner can work at first. But once these files need to be merged every month, consolidation becomes a job in its own right.
Only one person genuinely masters the system. If only they understand the formulas, the hidden sheets, the links between files or the calculation rules, the organisation depends on knowledge that is hard to pass on. The problem then stops being technical and becomes organisational.
The same errors keep coming back. Duplicates, inconsistent formats, mismatched values, poorly filled-in categories, totals that do not add up: a dashboard does not automatically fix these problems, but their recurrence usually signals that the data process itself needs improving.
Reporting eats up more time than analysis. When several days are needed every month to copy, merge, clean, check and format the data, most of the analytical work gets swallowed up by repetitive preparation tasks.
Analysis requests keep getting more detailed. Management wants to cross results by region, district, project, period, activity, beneficiary or category, sometimes across several dimensions at once. Each new request should not require manually building a new file.
Several sources need to be cross-referenced, for instance KoboToolbox, Excel, accounting and DHIS2, before ever reaching a dashboard. Past a certain level of complexity, the real question becomes one of data integration.
The figures exist, but not at the right time. An organisation can hold a great deal of data while having very little information that is actually useful for deciding. When figures arrive after the meeting, after the report, or after the decision window has closed, the problem is rarely just the chart: it runs through the whole chain, from data to action.
Excel, Power Query and Power BI: three different roles
There is no need to systematically pit Excel against Power BI. In a well-thought-out architecture, several tools play complementary roles.
Excel stays particularly relevant for controlled data entry, one-off analyses, financial models, simulations, exploration and certain operational reports.
Power Query adds another dimension: connecting different sources, cleaning data, transforming structures, merging or aggregating tables, automating repetitive steps, making transformations reproducible. It is not, incidentally, only a gateway to Power BI. For a small organisation with fairly moderate needs, Excel combined with Power Query can be a genuinely solid, lasting solution in its own right.
Power BI becomes particularly worthwhile once an organisation needs to centralise several sources, build a data model, define consistent measures and indicators, refresh data regularly, offer interactive analysis, share reports with several users, secure access to certain information, or turn reporting into a genuine steering system.
Microsoft, for that matter, presents the two tools as complementary rather than competing: Excel keeps its strength in flexible analysis, while Power BI brings modelling, visualisation and sharing capabilities suited to more structured uses. The question, then, is not "Excel or Power BI?" but which combination of tools matches the organisation's actual level of maturity and real needs.
A tool is not a system
This is probably the most important point. Buying Power BI does not automatically turn an organisation into a data-driven one. A reliable data system rests on at least six dimensions: people, processes, the data itself, technology, governance and decision-making.
A dashboard can look visually remarkable and still rest on poorly defined data, duplicates, inconsistent indicators, manual transformations or undocumented calculation rules. In that case, Power BI simply industrialises the problem rather than solving it. A good transformation therefore starts with the system, not with the chart.
Data quality comes before the dashboard
An organisation should not start by asking which chart to put on the dashboard. It should start by asking which decision it wants to improve, what information is needed for that, what the exact definition of each indicator is, where the data lives, who produces it, who validates it, how it gets transformed, what the reference source is, how often it needs refreshing, and who is responsible for its quality.
Data quality comes before transformation, which comes before the model, which comes before the calculations, which themselves come before visualisation, interpretation, and finally decision-making. If the first link in that chain is weak, a beautiful dashboard is not enough to make up for it.
And what about the data model?
Once an organisation starts structuring its BI, the data model becomes central. That said, no single architecture should be turned into dogma. Depending on the context, the model can take the form of a classic relational setup, a star schema, a snowflake schema, several interconnected schemas, or a hybrid architecture. The choice depends on the available sources, the complexity of the relationships, the analytical needs, the performance sought, the level of governance, the skills available, and how the system is expected to evolve.
The goal, then, is not to "put everything into a star schema," but to build a model that is coherent, understandable, efficient and maintainable, suited to the organisation's real analytical needs.
What does a move to BI really cost?
The cost of Excel is often underestimated, because a large part of it never shows up on an invoice: the time spent consolidating, manual checks, corrections, errors, conflicting versions, reporting delays, dependence on specific people, the difficulty of responding quickly to analysis requests.
Power BI has its own costs too: licensing depending on the architecture chosen, initial design, data cleaning, modelling, report development, training, documentation, maintenance, access administration, and the system's evolution over time.
The right way to reason about this, then, is neither to assume Power BI simply costs more than Excel, nor to promise it will automatically save money: what matters is measuring the relationship between the investment and the value created. How many days of reporting get saved? How many repetitive tasks disappear? How many decisions can be made faster? How many errors get avoided or caught earlier? How much time can teams spend on analysis rather than consolidation?
When should you stay on Excel and Power Query?
Moving to Power BI is not always necessary. Excel combined with Power Query can remain the best solution when data volumes stay reasonable, the number of sources is limited, reports are few, users are few, analyses stay mostly one-off, sharing needs are limited, processes are still being stabilised, or the organisation does not yet have the skills needed to maintain a BI system.
In some cases, better structuring Excel is a smarter decision than prematurely building a BI architecture that is more complex than needed. Technical sophistication is not, by itself, a sign of maturity.
When Power BI genuinely becomes worthwhile
Power BI becomes particularly worthwhile once several conditions appear together: data comes from several systems, the same reports are produced regularly, indicators need harmonising, several users need to consult the same information, analyses need to be interactive, data needs regular refreshing, reports have become critical for management, manual consolidation is getting costly, and the organisation wants to progressively build a genuine BI function.
The move to Power BI should then be seen as an evolution of the data system, not simply a change of software.
A progressive transition in seven steps
An organisation does not need to transform its entire system in one go. At Helpazone, we think of this transition as a progressive sequence, in seven steps.
Diagnose. Identify the sources, files, reports, users, indicators, quality issues, manual processes and decision-making needs.
Stabilise. Before building a dashboard, clarify definitions, data-entry rules, structures, responsibilities, indicators and reference sources.
Transform. Progressively introduce Power Query or other suitable tools to replace repetitive manipulations with reproducible transformations.
Model. Build a data model that matches the analytical needs, chosen for the context rather than simply copied because it is popular.
Visualise. Build a first dashboard on a limited scope: one project, one process, one monthly report, one business area, or one priority need from management.
Govern. Document the indicators, sources, transformations, calculation rules, responsibilities, access and maintenance procedures.
Scale up. Once the first use case is under control, progressively extend the solution to other projects, departments, regions or processes.
This progression helps avoid a common trap: building a technology far more complex than the organisational system it is meant to support.
Example: an NGO in Madagascar
Picture an NGO running three projects across several regions. At first, each project has its own Excel file, indicators are not always structured the same way, field data comes from KoboToolbox, expenses are tracked separately, and monthly reporting takes several days of consolidation.
The first instinct might be to think Power BI is the answer. That is not yet the right first step, though: the data and processes need to be understood first. Once that stabilisation stage is done, the different sources (KoboToolbox, Excel and the other tools) go through a transformation and control phase, get organised into a shared data model, feed harmonised indicators, and only then reach a Power BI dashboard that makes it possible to analyse results by project, region, period and activity, ultimately supporting decisions and action.
The main gain, then, is not simply having nicer charts. It is having information that is more coherent, more accessible and easier to keep up to date to support steering.
Example: an SME with several points of sale
An SME with five points of sale may receive five Excel files every week. Each manager sometimes uses a different format, different product names, different units, different data-entry rules. The problem is not that the managers use Excel: it is that the system does not clearly enough define how data should be produced and integrated.
A sound approach can start by standardising formats, controlling data entry, transforming the data automatically, centralising it, then analysing it. Power Query can already automate much of the preparation, and Power BI can then give management a dashboard for analysing sales at whatever refresh frequency is defined.
It is worth talking about data refreshed automatically or regularly, rather than promising "real time" when the architecture is not actually designed for that.
African constraints need to be built in from the design stage
A BI architecture designed for a large company with a sizeable IT team cannot simply be copied onto a small African NGO or SME. The constraints involve connectivity, electricity, equipment, available skills, budgets, maintenance, staff turnover, existing systems, the requirements of different donors, and sometimes the need for solutions that work offline in the field.
In Madagascar, for example, an organisation may need to make data from KoboToolbox, DHIS2, Excel, CommCare, an accounting system, or several project-specific tools all work together. The right architecture, then, is rarely the most technologically impressive one. It is the one that stays simple, reliable, secure, maintainable and genuinely used.
Team skills, infrastructure quality, management's involvement and organisational culture generally matter just as much as the technology itself for this kind of transformation to succeed.
A dashboard is not yet steering
This is where many BI projects stop too early. The organisation builds a dashboard, then finds it barely gets used. The reason is simple: a dashboard does not automatically create a decision-making culture.
It is also necessary to define which meetings genuinely use the data, which decisions need to be made, which indicators need reviewing, who analyses the gaps, who decides on corrective actions, how those decisions get tracked, and how the lessons get fed back into the processes.
The real goal, then, is not to have a Power BI dashboard, but to turn data into information useful for decisions, and decisions into actions tracked over time. This is the level at which BI meets MEAL, programme management, information systems and organisational governance.
From Excel to BI: a transformation, not a replacement
A mature organisation does not necessarily aim to get rid of Excel. It aims to give each tool the role it is best suited to: Excel for flexible analysis and certain operational uses, Power Query for reproducible preparation and transformation, SQL and databases for structuring and querying information once that becomes necessary, Power BI or Tableau for analysis, visualisation and steering, and possibly platforms like BigQuery once volumes or architectural needs justify it. Governance, finally, ensures the coherence, security, accountability and sustainability of the whole.
So this is not simply a migration from Excel to Power BI. It is a deeper evolution, moving from the isolated file to structured data, then to reliable information, to analysis, to decision, and finally to action.
How Helpazone supports this evolution
At Helpazone, this evolution fits into a wider progression of skills development in data analysis and business intelligence, designed to start from each organisation's real context rather than from a standard model imported as-is. Support can cover diagnosing existing systems, structuring data, setting up Power Query and suitable data models, building the first Power BI dashboards, and progressively transferring skills to internal teams, so they can evolve and maintain the system themselves once the support ends.
The goal is never to impose a tool. It is to help each organisation choose, at its own pace, the data system that genuinely matches its means and its steering needs.
Conclusion: the right tool is the one that fits your system, not the other way around
Excel and Power BI are not rivals. They are two tools that answer different needs, and that can coexist very well within the same organisation, provided each one holds the place it is best suited to.
The real question, then, is not "Excel or Power BI?" but "does our data system currently let us understand our reality and decide in time?" As long as the answer is yes, there is often no urgency to change tools. The day it becomes no, the best response is not to buy a licence, but to retrace, step by step, the path that runs from the file to the decision.