Replacing Excel as a database: when the switch pays off

In performance diagnostics, therapy and research, Excel is often the first tool used to collect measurement data. It is flexible, everyone knows it, and a new column is added in seconds. At some point, though, the question arises whether to replace Excel as a database. This guide shows how to recognise that the time has come, what a relational database does differently and how to migrate your existing data successfully.

Why Excel reaches its limits as a database

Excel is a spreadsheet, not a database. As long as one person maintains a manageable file, this hardly matters. Problems arise when data grows over years, several people contribute and analyses across many files are needed. The typical disadvantages of Excel as a database then show up in four areas.

Versions and files

Over time, files pile up per test, per season, per group and per person. Then come copies with suffixes such as “final” or “new”. Often only the person who created a file knows which one is valid. If they leave the organisation, that knowledge leaves with them.

Assignment to people and points in time

In Excel, a person is usually just a name in a row. Spellings change, names repeat, and the same person appears differently in different files. The same goes for points in time: sometimes the date is in a column, sometimes in the file name, sometimes in the worksheet. Putting together a history over several years becomes manual work as a result.

Rights and traceability

Anyone who can open an Excel file usually sees everything in it. Fine-grained rights, such as a coach seeing only their own squad, are hardly feasible. Changes are not reliably logged. With health data, this is also a data protection issue.

Analysis over years

Columns are renamed, units change, tests are adjusted. Every analysis over several years therefore starts with harmonising the files. The more data accumulates, the greater this effort becomes, and the less often the analyses actually get done.

Excel vs database: the comparison

A relational database stores data in tables that are linked to each other via unique keys: people, appointments, parameters and readings. Every reading belongs to exactly one person, one point in time and one parameter with a defined unit. This results in the key differences.

Criterion	Excel	Relational database
Structure	Free, different for each file and person	Consistent, defined by parameters and units
Assignment to people	Via names in rows	Via a unique identifier
Points in time	Column, file name or worksheet	Integral part of every reading
Multiple users	Conflicts when editing simultaneously, copies	Simultaneous work on a single data set
Access rights	Whole file or nothing	Controllable per role, person and data area
Tracking changes	Hardly possible	Can be logged
Analysis over years	Files must first be harmonised	Directly across all data
Data volume	Limited to just over a million rows per worksheet, large files become sluggish	Designed for large volumes of data
Flexibility for individuals	Very high	Lower, but reliable

Excel remains a good tool for quick calculations, ad hoc analyses and reports. As a store for measurement data that needs to remain comparable over years, a relational database is the better foundation.

When the switch pays off

Not every Excel file needs to go into a database. The switch pays off when several of the following apply:

• Several people record or use the same data.
• The same tests are repeated regularly, and histories matter.
• Data from devices, questionnaires and manual entry needs to come together.
• Analyses across groups, sports or years are needed.
• It involves health data, where access and changes must be traceable.
• Knowledge about the files rests with just a few people.

Converting Excel into a database: how the migration works

The biggest concern when switching is usually the existing data: years of data in dozens of files with different structures. The migration has to bring this data into the database without loss and with reasonable effort.

In the Datico^® Life Hub, this is done with Excel Import using import schemas. An import schema describes once how an Excel file is structured:

• which column contains which parameter, with unit and data type
• how the person is identified
• how the event is identified, i.e. the test, training session, competition or therapy
• which row the data starts in and which delimiter applies

After that, your team imports further files in the same format in just a few clicks, without reconfiguration and without Datico^® support. Every row becomes a person-related event that is immediately available for analysis. For different devices, software or test protocols, you manage several schemas in parallel. This means even tables that have grown over years can be migrated in one step. Every import is logged in the Audit Trail.

Excel Import is not just for historical data. If data continues to be recorded in Excel in the field, it flows into the database via the same schema.

What becomes possible after the switch

Once the data is in a relational structure, the possibilities change.

• Classify: with Reference Sets, every reading is automatically assigned to an evaluation zone, matched to sex, age group or your own dimensions.
• Condense: with Index Management, several parameters are combined into scores that are recalculated automatically when new data arrives.
• Report: Custom Reports use Excel and Word templates in your own design and are filled from the database at the click of a button.
• Display: with the Dashboard Generator, you put together a dedicated view with the relevant metrics for each role.
• Process further: for in-depth statistics, the Statistics Connector passes the data to R, SPSS or Python.
• Protect: Roles & Rights determine who sees which people and data, and the Audit Trail logs access and changes.

Excel remains available as an output

Switching to a database does not mean Excel disappears. With Excel Export, you define export schemas: which fields appear in the file, in which order and with which labels. You can then generate Excel files yourself at any time for individuals, groups or entire projects, filtered by event type and period. Roles and rights control who may use which schema.

This makes the transition easier. Those used to analysing in Excel can carry on doing so at first, only now with clean data from a single shared source.

Typical pitfalls when switching

The technology is rarely the problem. The difficulties lie elsewhere:

• Everything at once: if you try to migrate all your existing data in one go before the parameters have been clarified, you carry the disorder from Excel over into the database. It is better to start with the most important tests.
• Inconsistent terms: if the same measurement has three different names in three files, this has to be decided before the import. That is a subject-matter task, not a technical one.
• Switching Excel off instead of replacing it: teams that suddenly lose their familiar way of working fall back on private files. An export to Excel during the transition keeps everyone on the same data.
• No one in charge: someone has to decide which parameters apply and who may create new ones. Otherwise, the database will also become a muddle over time.

Case study: Olympic Training Centre Lower Saxony

At the Olympic Training Centre Lower Saxony (OSP Niedersachsen) in Hanover, the training science department’s data had grown over years, much of it in Excel. In the field, too, data is still often entered directly into Excel. Since 2025, OSP Niedersachsen has been working with Datico^® to develop this into a relational data infrastructure.

The approach was deliberately step by step. Existing data and new Excel data are imported via Excel import schemas. Manual entry of tests and test batteries in the Datico^® Life Hub was adapted to stay as close as possible to the familiar Excel way of working. Excel export schemas allow free export to Excel at any time. The long-term goal is clear: to replace Excel and carry out both data entry and analysis in the Datico^® Life Hub.

Dr Sebastian Buitrago, Head of Training Science, puts it like this:

“Our training science team worked with Excel for years. With the Datico^® Life Hub, we are bringing this vast amount of data and files together in a real database for the first time – while initially keeping familiar ways of working with Excel in both directions. As the Datico^® Life Hub develops further, however, the aim is to implement workflows that require little or no use of Excel at all. That would be a completely new working basis for us and for other Olympic training centres too.”

Read the full story in the OSP Niedersachsen case study. In the webinar “From Excel to a relational database”, Markus Stadler and Dr Sebastian Buitrago show the path from practice. You will find the recording under Webinars.

Checklist for switching from Excel to a database

1. Take stock: which files exist, who maintains them, and which are still in use?
2. Define parameters: which readings are there, with which unit and which unique name?
3. Make people unique: one identifier per person, resolve duplicates and differing spellings.
4. Define events: which tests, training sessions, competitions or therapies are recorded, and how can they be recognised in the files?
5. Create import schemas: one schema per file structure, starting with the most important files.
6. Check samples: compare imported values against the source files before migrating all existing data.
7. Define rights: who sees which people and data, and who may import and export?
8. Set up exports: create the Excel exports your team needs today as export schemas.
9. Switch data entry: record new data directly in the database step by step, via device connections, forms or manual entry.
10. Bring the team along: support the transition instead of switching Excel off overnight.

If you would like to know how your Excel data would fit into a database, arrange a personal initial consultation. You can also try the Datico^® Life Hub free for 30 days.