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Why You Need End-to-End Data Lineage

erwin

Not Documenting End-to-End Data Lineage Is Risky Busines – Understanding your data’s origins is key to successful data governance. Not everyone understands what end-to-end data lineage is or why it is important. Who are the data owners? Five Consequences of Ignoring Data Lineage.

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What is Data Lineage? Top 5 Benefits of Data Lineage

erwin

An understanding of the data’s origins and history helps answer questions about the origin of data in a Key Performance Indicator (KPI) reports, including: How the report tables and columns are defined in the metadata? Who are the data owners? Data lineage offers proof that the data provided is reflected accurately.

Metadata 111
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Data Dictionary vs. Business Glossary (and How They Can Get Your Business and IT Teams on the Same Page)

Alation

That’s why it’s critical that important terms be defined, documented, and made visible to everyone. This is where a data dictionary and business glossary become useful for getting both your business and IT teams on the same page. One per data source. Data modeling, database design, documentation of data sources.

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Data Science at The New York Times

Domino Data Lab

Here is how we think about the mindset and the toolset of data science at The New York Times. Because I’m an academic I like to look at the original founding documents. Here is an ancient document about data science, specifically a piece by Jeff Hammerbacher. This is a very data sciency crowd. Good, okay.

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Your Effective Roadmap To Implement A Successful Business Intelligence Strategy

datapine

It should be sponsored by an executive who has bottom-line responsibility, a broad picture of the organization’s strategy and goals, and knows how to translate the company mission into mission-focused KPIs. They can govern the implementation with a documented business case and be responsible for changes in scope.

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The art and science of data product portfolio management

AWS Big Data

In the same way, overly restrictive data governance practices that either prevent data products from taking root at all, or pare them back too aggressively (deforestation), can over time create “data deserts” that drive both the producers and consumers of data within an organization to look elsewhere for their data needs.