Service
Data Automation
The reports built by hand today are built automatically tomorrow.
Definition
Data automation removes manual work between tools: extracting, cleaning, joining and loading information with SQL, Python and connectors, so reports and processes depend on systems instead of one person's memory.
For whom
Teams copying data between tools every week.
Operations depending on critical spreadsheets.
Companies that need to automate reporting before building heavy infrastructure.
Problems it solves
Errors from manual copying.
Duplicated files or conflicting versions.
Processes depending on a specific person.
Late alerts about important data.
What is included
Lightweight pipelines (ETL).
Cleaning and normalization.
Tool integrations.
Automatic alerts.
Connected spreadsheets.
How it is measured
Manual hours reduced.
Update frequency.
Reporting errors avoided.
Documented and repeatable processes.
How I work
The same process in every project. No black box: you know what is done, why, and how it is measured.
How I workFrequently asked questions
Do I need a data warehouse for this?
Not always. Sometimes connectors, connected sheets or lightweight pipelines are enough; a data warehouse makes sense when volume, history or governance justify it.
Which processes should be automated first?
Start with repetitive, frequent processes that affect decisions. It is also worth prioritizing where manual error changes budgets, sales or operating time.
What stack do you use?
I use SQL, Python, APIs and native connectors when they are enough. The stack is chosen according to the current system, not tool preference.