Data Discovery Data Security Documentation 
Smart Data Collective
NOVEMBER 25, 2019
New technologies, especially those driven by artificial intelligence (or AI), are changing how businesses collect and extract usable insights from data. New Avenues of Data Discovery. These new avenues of data discovery will give business intelligence analysts more data sources than ever before.
Astera
JUNE 14, 2024
Data Privacy: Protecting private information from unlawful access and ensuring data handling practices comply with privacy laws and regulations. Data Security: Safeguarding data against breaches and cyber threats through robust security measures like encryption and regular security audits.
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Astera
JULY 12, 2024
A resource catalog is a systematically organized repository that provides detailed information about various data assets within an organization. This catalog serves as a comprehensive inventory, documenting the metadata, location, accessibility, and usage guidelines of data resources.
Astera
APRIL 24, 2024
It helps establish policies, assign roles and responsibilities, and maintain data quality and security in compliance with relevant regulatory standards. The framework, therefore, provides detailed documentation about the organization’s data architecture, which is necessary to govern its data assets.
Astera
MARCH 28, 2024
Establishing a data catalog is part of a broader data governance strategy, which includes: creating a business glossary, increasing data literacy across the company and data classification. Data Catalog vs. Data Dictionary A common confusion arises when data dictionaries come into the discussion.
Astera
APRIL 4, 2024
Here are some real-world scenarios where each approach is effectively implemented: Data Governance: E-commerce Quality Assurance: In e-commerce, data governance ensures product quality consistency. Healthcare Data Security: Data governance is vital to protect patient information.
Astera
MAY 14, 2024
Process metadata: tracks data handling steps. It ensures data quality and reproducibility by documenting how the data was derived and transformed, including its origin. Examples include actions (such as data cleaning steps), tools used, tests performed, and lineage (data source).
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