Data Reuse
Data Reuse Pitfalls: Not Understanding Data Export Controls
Learn how to navigate the complexities of data export controls in Power BI and avoid common mistakes.
Solid data documentation is crucial for self-service analytics. The BI Catalog can bridge the gap between complex data structures and user comprehension.
For self-service analytics to truly deliver, users need confidence in finding and using the best data assets that fit their needs. However, data hub documentation frequently misses key details for productive data reuse. Without helpful context around data definitions, derivations, and appropriate uses, practitioners struggle with analysis, making sense of data, and actionability.
This blog explores why solid data documentation is vital for building understanding and trust.
Why Documentation Matters
Comprehensive documentation bridges the gap between intricate data structures and end-users' needs. In its absence, uncertainty arises, leading users to question the appropriateness of available data. The potential for data reuse diminishes while the risk of misinterpretation increases. It creates a detrimental cycle that organizations cannot afford, as the effectiveness of analysis relies on unlocking the true potential of the data.
Introducing the BI Catalog - A 3-Tier Comprehension Solution
The BI Catalog offers a methodology using interconnected documentation layers to promote data comprehension and drive informed decisions.
- Field-Level Description: Detailed field descriptors embed meaning into data structures per standardized guidelines. It connects technical complexity with human understanding.
- Dataset-Level Details: Robust dataset documentation integrates field-level building blocks into a unified narrative to reveal a broader value and applications.
- Trustworthy Data Hub: A curated data catalog endorses reliable assets into a searchable repository to build user confidence.
In unifying these knowledge-building tiers, the BI Catalog systematically bridges gaps between intricate technical data representations and accessibility to practitioners.
An Informed Lens
Self-service analytics fundamentally relies upon mitigating uncertainty through multidimensional clarity. The BI Catalog promotes cohesive structural and contextual comprehension, tackling barriers around navigating complex data landscapes. It cultivates literacy for practitioners to discover the “best” data and the right data - assets reflecting reality and powering decisions confidently.
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7 Costly Mistakes of Data Reuse, and How to Avoid Them
Dive into the potential risks associated with data reuse and equips you with strategies to avoid making them.
