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What is garbage in, garbage out (GIGO)?

Dataconomy

Types of garbage inputs Understanding the types of garbage data is essential for addressing and improving data quality. Identifying these pitfalls can help improve overall data quality. Strategic data quality assurance MDM aims to consolidate various data sources into a single, reliable source of truth.

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Data stewardship

Dataconomy

Essential skills of a data steward To fulfill their responsibilities effectively, data stewards should possess a blend of technical and interpersonal skills: Technical expertise: Knowledge of programming and data modeling is crucial. Effective communication: The ability to collaborate across departments is essential.

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10 GitHub Awesome Lists for Data Science

Flipboard

Ideal for data scientists and engineers working with databases and complex data models. Awesome SQLAlchemy: Tools for Python’s Leading ORM Link: dahlia/awesome-sqlalchemy It is a list of tools, extensions, and resources for SQLAlchemy, Python’s most popular ORM.

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Data Mesh Architecture on Cloud for BI, Data Science and Process Mining

Data Science Blog

It advocates decentralizing data ownership to domain-oriented teams. Each team becomes responsible for its Data Products , and a self-serve data infrastructure is established. This enables scalability, agility, and improved data quality while promoting data democratization.

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Data vault

Dataconomy

Data vault is not just a method; its an innovative approach to data modeling and integration tailored for modern data warehouses. As businesses continue to evolve, the complexity of managing data efficiently has grown. As businesses continue to evolve, the complexity of managing data efficiently has grown.

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Data architecture strategy for data quality

IBM Journey to AI blog

Poor data quality is one of the top barriers faced by organizations aspiring to be more data-driven. Ill-timed business decisions and misinformed business processes, missed revenue opportunities, failed business initiatives and complex data systems can all stem from data quality issues.

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Data mart

Dataconomy

This process extracts data from various sources, transforms it into a desired format, and loads it into the data mart. With efficient ETL practices, organizations can maintain high data quality and relevant structures. This type often benefits from comprehensive data governance and a unified data model.