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All in the Data: Elements of a Data Strategy

TDAN

I published an article a few months back that was titled Where Does Data Governance Fit in a Data Strategy (and other important questions). In the article, I quickly outlined seven primary elements of a data strategy as an answer to one of the “other important questions.”

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Metadata Improves Security, Quality, and Transparency

TDAN

They use data better. How does Spotify win against a competitor like Apple? Using machine learning and AI, Spotify creates value for their users by providing a more personalized experience.

Metadata 119
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Top 10 Metadata Management Influencers, Sites, and Blogs You Must Follow in 2021

Octopai

Aptly named, metadata management is the process in which BI and Analytics teams manage metadata, which is the data that describes other data. In other words, data is the context and metadata is the content. Without metadata, BI teams are unable to understand the data’s full story.

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Metadata is the Magic Behind Data Fabric

TDAN

The main goal of creating an enterprise data fabric is not new. It is the ability to deliver the right data at the right time, in the right shape, and to the right data consumer, irrespective of how and where it is stored. Data fabric is the common “net” that stitches integrated data from multiple data […].

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Business Glossary and Metadata: Small Teams Data Governance

TDAN

In my last article I suggested that many organizations have approached Data Governance incorrectly using only centralize data governance teams and that approach is not working for many.

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Why data observability is essential to AI governance

erwin

And do you have the transparency and data observability built into your data strategy to adequately support the AI teams building them? Will the new creative, diverse and scalable data pipelines you are building also incorporate the AI governance guardrails needed to manage and limit your organizational risk?

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Metadata-Driven Data Warehouses are Ideal

TDAN

A metadata-driven data warehouse (MDW) offers a modern approach that is designed to make EDW development much more simplified and faster. It makes use of metadata (data about your data) as its foundation and combines data modeling and ETL functionalities to build data warehouses.