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Those 30 lines of Python coming back to haunt you?

If history can be trusted, the accumulation of data debt poses significant risks to any organization from hindering progress to increasing costs - not the mention the future compliance issues (who wants to deal with them!)<br><br>

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Those 30 lines of Python coming back to haunt you?

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  1. Those 30 lines of Python coming back to haunt you? All data products and pipelines supporting a modern business today 30 lines of Python written in 2018 5 signs you have data debt www.polestarllp.com

  2. What is data debt data debt / day-tuh d-eh-t/ noun Data debt is the build-up of data-related problems over time, including quality, literacy, and security issues www.polestarllp.com

  3. Dashboard graveyard where reports go die Your dashboards look like a digital hoarding situation - hundreds of outdated reports that nobody remembers creating or knows (or bothers) how to delete SQL spagheti monster scares new developers Technical debt is real folks! If your SQL code looks like a tangled mess of queries built upon queries that nobody dares to touch - you are there!! www.polestarllp.com

  4. Nobody knows which dataset is real Fragmented data management across multiple teams and architectures - without clear data stewards and owners causes unnecessary duplication Don't touch that pipeline, it'll break everything! It’s like duct tape is holding your data pipelines - like a data scientist creating a series of interconnected dbt DAG files with SQL. Leading to messy querying over time. www.polestarllp.com

  5. Your documentation is a ghost town Documentation exists only in Bob's head - lots of cryptic table names and columns that might as well be ancient hieroglyphics. This makes easy tasks take too long. www.polestarllp.com

  6. Stop ignoring the signs If history can be trusted, the accumulation of data debt poses significant risks to any organization from hindering progress to increasing costs - not the mention the future compliance issues (who wants to deal with them!) The clear risks and demerits you see are: • Your expensive data scientists are playing "Find the Right Excel Sheet" instead of building AI models Summary • When your marketing team targets teens with retirement plans because the customer data is wrong • One tiny change to a table and suddenly the CEO's dashboard shows everyone made $0 in sales And You're still paying premium for that 2010 data warehouse because “it has important stuff somewhere” www.polestarllp.com

  7. Be data debt-free with Polestar! Get a consultation today!

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