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# Is managing a data BI project complicated? Challenges, big mistakes, frameworks and real lessons from big companies

**[C\# Corner](https://daily.dev/sources/csharpcorner)** · 10 min read · 0 upvotes · 0 comments

## Summary

Managing a BI project is far more complex than building dashboards — it spans business definitions, data quality, governance, architecture, adoption, and trust. Key challenges include business ambiguity around KPI definitions, poor data quality, weak integration pipelines, unclear ownership, and low user adoption. Common mistakes include starting with tools before defining business problems, building too much before validating, ignoring data quality frameworks, and neglecting change management. Frameworks like Kimball DW/BI Lifecycle, DAMA-DMBOK, CRISP-DM, and DataOps can guide structured delivery. Real-world lessons from Airbnb (Minerva metrics platform), LinkedIn (DataHub), Uber (real-time architecture), Target Canada (data quality failures), and Knight Capital (lack of controls) illustrate the cost of getting BI wrong. Success is measured not by dashboards delivered but by better decisions enabled.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.csharp.com/article/is-managing-a-data-bi-project-complicated-challenges-big-mistakes-frameworks>

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Tags: [#big-data](https://daily.dev/tags/big-data), [#bi](https://daily.dev/tags/bi), [#data-quality](https://daily.dev/tags/data-quality), [#power-bi](https://daily.dev/tags/power-bi)

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