Automated data retrieval and filtering
Anyone who works with data knows the ratio: most of the time goes into collecting, exporting, cleaning and merging. The analysis itself, the work that matters, comes last and gets the least time. I build automations that take over the retrieval and filtering of data. The data is ready in the form you need, at the moment you need it, so you can start looking at what it means straight away.
Where the time goes today
- Logging into four tools, setting the same period in each and creating an export
- Merging exports, aligning column names, removing duplicate rows, dates that are formatted differently in every source
- Filtering down to what actually matters: only those clients, only those campaigns, only that period
- And doing it all again next week, because last week’s export is already out of date
This work is predictable and follows the same steps every time. That makes it ideally suited to automation. An automation I built does exactly this for several data sources at once: the data is retrieved automatically, filtered on predefined criteria and delivered to a spreadsheet or database. What used to take half a day per week now happens before the working day starts.
How the automation works
- Retrieve. The workflow pulls data via the APIs of your sources: analytics, ad platforms, CRM, accounting, web shop, project tool or an external data source. At a fixed time, or as soon as something changes.
- Filter. Only the data that meets your criteria goes through: a certain period, client group, product category, threshold value. The rest is left out, so you do not have to dig through piles of raw data.
- Clean and merge. Column names, date formats and currencies are aligned, duplicate rows removed and data from different sources linked by client, campaign or product.
- Deliver. The result lands where you work with it: Google Sheets, Excel, a database, BigQuery, a dashboard or directly in an automated report. With a notification if something stands out, such as a source that returned no data.
What it delivers
More time for analysis
The hours you spent exporting and merging go to the work that produces insight: spotting patterns, substantiating decisions, adjusting course.
Always the same definitions
Because the filters and calculations are fixed, “revenue” or “conversion” means the same thing every week. No more debate about whose export is correct.
Faster signals
Data that arrives automatically can also be monitored automatically. A threshold that is exceeded or a source that goes quiet, you see the same day instead of at the end of the month.
Examples of data automations
Keyword rankings and traffic per client pulled automatically from SEO tools and Search Console, filtered to the pages that matter. Orders and returns from a web shop merged daily with stock data. Outstanding invoices from the accounting system filtered by age and prepared for follow-up. Leads from multiple forms and ad platforms brought together in one overview, deduplicated and enriched with company data. Prices or availability from external sources retrieved periodically and compared with your own offering.
Sometimes the source is not an API but an email attachment, a PDF or a website. An automation can extract data from those too, where needed with an AI step that turns unstructured text into clean fields. How that works is explained under system integrations.
What it costs and what is included
A data automation with two or three sources and a fixed set of filters usually comes in between 750 and 2,000 euros one-off. More sources, more complex links between datasets or AI steps for unstructured data bring it to 2,000 to 4,000 euros. The monthly maintenance covers changes in the APIs of your sources, new fields or filters as your questions change, and the monitoring that warns you when a source goes quiet. More on the pricing and maintenance page.
Frequently asked questions
Where does the data end up?
Wherever you work with it. For most business owners that is Google Sheets or Excel; for larger volumes a database such as PostgreSQL or BigQuery, or a dashboard tool such as Looker Studio. We make the choice together based on how much data there is and who needs to work with it.
Can I adjust the filters myself later?
Yes. Where possible I put the filter criteria in a settings sheet or configuration you can change without touching the workflow itself. Larger changes, such as adding a new source, fall under maintenance.
How often is the data refreshed?
As often as necessary and sensible: hourly, daily, weekly or as soon as something happens in a source. Some APIs limit how often you may request data; I take that into account in the setup.
What happens with personal data?
Upfront we define which data the automation retrieves and where it is stored. Personal data that is not needed for the analysis is not retrieved or is anonymised immediately. Data stays within your own environment where possible.
How many hours a week do you spend collecting?
Tell me which sources you pull data from and what you do with it. I will let you know what can be automated and what it costs. Or see the overview of all automations first.