Anthropic Just Released Cowork (And I Think Data Teams Should Pay Attention)

Anthropic released something yesterday called Cowork. I spent some time reading through the announcement and thinking about what this means for how we work with data. Here's why I think it matters.

The simplest way to explain it: you point Claude at a folder on your computer, and Claude can read, edit, and create files in that folder.

That might sound basic, but the key difference is how Claude works once you give it access. Instead of the usual back-and-forth where you ask a question, get a response, copy it, paste it somewhere, ask another question, it works more like leaving tasks for a coworker.

You give Claude a job. It makes a plan. It executes. It loops you in as it goes.

If you have used Claude Code (the developer-focused version), this is built on the same foundation. But Cowork is designed for work that is not coding such as organizing files, processing documents, building spreadsheets, drafting reports from scattered notes.

For data people, I think this opens up some interesting possibilities.

Data work involves a lot of file wrangling that sits outside your main pipeline.

You download CSVs from different sources. You screenshot dashboards for a presentation. You keep notes in random text files. You have folders of SQL queries that need organizing. You generate reports that pull from five different places.

Most of this work is tedious but necessary. It is also the kind of work where the current AI chat interface doesn't quite fit. You would need to manually upload files, copy results back out, repeat the process across multiple files.

With Cowork, you can point Claude at a folder and say "organize these downloaded CSVs by date and source" or "create a summary spreadsheet from these expense screenshots" or "build me a clean report from the notes scattered across these files."

Claude handles it. You review the work. You correct it if needed.

I keep coming back to a few specific scenarios where this could change how we operate.

Data exploration and cleaning. You are handed a messy dataset. Instead of writing scripts to explore it, you give Claude access to the folder and say "show me what is in here, flag any quality issues, suggest a cleaning approach." It can create summary files, restructure the data, document what it finds.

Documentation generation. You have a folder of SQL queries that power your reporting layer. You have been meaning to document them. Point Claude at the folder: "create documentation for each of these queries, what they do, what tables they touch, what assumptions they make."

Report automation. You generate a weekly report that pulls data from three different exports, combines them, and formats the output. You have been doing it manually. Give Claude the folder with your exports and template, let it handle the repetitive parts.

Adhoc analysis. Business asks for something quick. You have the raw data in a folder. "Pull out the top 10 customers by spend, create a breakdown by region, put it in a spreadsheet I can send over." Done.

The pattern I am noticing is that all these are tasks that currently take just enough effort that you procrastinate on them, but not enough to justify building proper automation.

I need to mention this because it matters: when you give Claude access to a folder, it can delete files if you tell it to.

Anthropic has built in protections, Claude asks before taking significant actions, and they have added defenses against prompt injection (where someone tries to hijack Claude's behavior through content it reads). But you are still giving an AI permission to modify your local files.

Their recommendation is to start with low-stakes folders while you learn how it works. Test it on a downloads folder before pointing it at your production data directories.

For data teams, I would add: be explicit about what you want. "Reorganize these files" is ambiguous. "Create a new subfolder called 'processed' and move CSV files there after validating they have date columns" is clearer and safer.

This is useful for file-level work, but it's not replacing your data pipeline.

You are still building ETL processes. You are still managing databases. You are still writing the complex transformations that define your metrics.

What Cowork handles is the messy periphery, the file prep, the one-off analyses, the documentation you have been putting off, the reports that do not justify automation but still take time.

It also does not understand your business context. Claude can organize files and create spreadsheets, but it won't know that "Q4" at your company means October through December, not the calendar quarter. You still need to be specific about what you want.

I think we are going to see data people use this to handle an entire category of work that currently falls through the cracks.

The work that is too small to automate properly but too repetitive to enjoy doing manually. The documentation you should write but never get around to. The exploratory analysis that would be useful but takes just enough setup time that you skip it.

If Cowork works as advertised, it turns "I should do this but I don't have time" into "Claude, handle this."

The gap between data teams that figure this out and teams that don't will show up in small ways that compound, better documentation, faster adhoc requests, cleaner file organization, more thorough exploratory work.

It's available now as a research preview for Claude Max subscribers on macOS. You can join the waitlist if you are on a different plan. They mentioned they are planning to add Windows support and improve it rapidly based on feedback. I'm planning to test this on a few low-stakes folders and see where it fits into my workflow. If you try it, I would be curious to hear what you use it for.

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