Data automation and web scraping
Collect and prepare data automatically
In many teams the week looks the same: someone opens the same websites or portals, opens PDFs, copies numbers into a spreadsheet and checks what changed since last time. The information exists – it just lives in different places and different formats.
That kind of research and data entry can be automated in most cases. A program pulls the sources on a schedule, extracts the relevant details, validates them and delivers them in a structured format – in Excel, in a database or straight into a report.
Web scraping is just one of several techniques. Which one gets used depends on the source.
Typical starting points
- Prices, availability or master data of suppliers and competitors are looked up by hand on their websites on a regular basis.
- Figures from published financial statements, registers or government portals have to be picked out company by company.
- PDF reports or invoices arrive every week and individual values are keyed into a spreadsheet.
- Data from an API should be fetched regularly and reconciled with internal files.
- Several exports from different systems are merged by copy-paste before the actual analysis can even begin.
Who this is for
Companies and business teams where staff regularly gather information from external or internal sources: purchasing, sales, market intelligence, controlling, consulting firms, administration. The industry doesn't matter. What matters is that the research is recurring and follows a recognizable pattern.
What can be automated
Websites and portals
Pull publicly accessible pages on a schedule and extract the relevant details: prices, product data, publications or register entries. For portals behind a login, only where the terms of use allow it.
APIs
If a provider offers an API, that's almost always the better route: more stable, faster, and explicitly built for it. I connect APIs and handle authentication, rate limits and error cases.
PDF documents
Extract values from reports, invoices or financial statements automatically – tables as well as details buried in running text. For scanned documents, text recognition is added.
Excel, CSV and text files
Automatically read, standardize and merge files that arrive regularly, even when structure and column names vary from one delivery to the next.
Databases and exports
Query data from internal systems and combine it with external sources so analyses no longer have to be stitched together from several files.
Validation and delivery
Every pipeline checks the retrieved data for completeness and plausibility and stores it where it's needed: as an Excel file, in a database or as the basis for reporting.
How I decide which technique fits
The opening question isn't whether web scraping is possible, but which source delivers the information most reliably. If there's an API, I use it. If there's a structured export, it usually beats reading a website. Only when both are missing does the page itself get scraped.
The implementation is usually in Python, because retrieval, validation, processing and storage can be combined into one maintainable workflow. For your team, the technology behind it is secondary. What matters is that the data arrives on time, validated and in a usable format.
Name the sources and what the data is needed for. I check up front whether retrieval can be automated – and tell you if a source is off the table.
Talk data automationWhen does it make sense?
Makes sense when …
- the same information is collected regularly from the same sources.
- the sources are technically accessible and using them this way is permitted.
- manual collection is the bottleneck in front of the actual analysis.
- the data is processed further in Excel, a database or a report.
Less suitable when …
- the research is a one-off – then doing it by hand is often faster than any automation.
- the source explicitly prohibits this kind of use, or personal data is involved whose processing wouldn't be permitted.
- the source data is so unreliable that every delivery would have to be checked by hand anyway.
How I work
- 01
Clarify sources and needs
What information is needed, from which sources, how often, and what happens with the data afterwards? I look at the sources and check whether and how they can be retrieved automatically.
- 02
Check feasibility
Before any quote, I test the critical sources. That way you know up front whether the automation holds technically and where the limitations are.
- 03
Build the pipeline
Retrieval, validation, processing and storage are implemented step by step and tested with real data. Every run is logged so deviations become visible.
- 04
Operate and hand over
You receive the source code, operating documentation and a walkthrough. On request I set up the scheduled runs and stay on for the first few cycles.
What you get
- An automated data pipeline with logged runs
- Structured output in the format you need – Excel, CSV, database or reporting basis
- Plausibility checks that flag missing or suspicious values
- Source code, operating documentation and a walkthrough
Limits
- Not every website can be read automatically. Technical protections, terms of use or legal requirements can stand in the way. I check that up front and tell you plainly.
- Sources change. If a website is restructured, the pipeline has to be adapted. I build it so such changes are detected early; they can't be prevented.
- The quality of the results depends on the quality of the source. Automation can expose incomplete or contradictory source data, but it can't fix it.
Frequently asked
- What is web scraping?
- Web scraping is the automated extraction of information from websites. A program opens the pages, recognizes the relevant details – prices, addresses or table values, say – and saves them in a structured form. It replaces manual copying from the browser and changes nothing on the website itself.
- Which data sources can be processed automatically?
- Websites and portals, APIs, PDF documents, Excel and CSV files, databases and structured exports. APIs and exports are the most reliable because they're built for machine use. Websites work too, but they're more prone to changes.
- Can data be extracted from PDFs automatically?
- Yes. With digitally generated PDFs, text and tables can be read directly. Scanned documents go through text recognition first, and that result has to be checked. How reliable it is depends on the structure of the documents: consistent templates are straightforward, highly variable documents need extra checks.
- What happens when a website changes?
- Then the pipeline delivers either no data or suspicious data. That's why every run validates the result and reports deviations instead of silently passing on wrong values. Adapting to the changed page is usually a small fix, made either by me or by your team using the documentation.
- Is web scraping always possible?
- No. Some websites prohibit automated reading in their terms of use; others protect themselves technically. For personal data, privacy law applies on top. I check this before every project and propose a different route where necessary. Often there's an API or an export that's better suited anyway.
- Can the data be delivered into Excel automatically?
- Yes. The pipeline can generate finished Excel files, fill existing workbooks or provide a data source that Excel pulls through Power Query. The familiar working environment stays; only the manual collecting goes away.
Next step
What information does your team keep collecting over and over?
Name the sources and what the data is needed for. I'll check whether and how retrieval can be automated – and tell you if it can't.