Knowledge Hut
APRIL 20, 2023
Data governance refers to the set of policies, procedures, mix of people and standards that organisations put in place to manage their data assets. It involves establishing a framework for data management that ensures data quality, privacy, security, and compliance with regulatory requirements.
Databand.ai
AUGUST 30, 2023
This includes defining roles and responsibilities related to managing datasets and setting guidelines for metadata management. Data profiling: Regularly analyze dataset content to identify inconsistencies or errors. Data cleansing: Implement corrective measures to address identified issues and improve dataset accuracy levels.
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Databand.ai
AUGUST 30, 2023
Data silos: Legacy architectures often result in data being stored and processed in siloed environments, which can limit collaboration and hinder the ability to generate comprehensive insights. This requires implementing robust data integration tools and practices, such as data validation, data cleansing, and metadata management.
Monte Carlo
JULY 11, 2023
Even if the data is accurate, if it does not address the specific questions or requirements of the task, it may be of limited value or even irrelevant. Contextual understanding: Data quality is also influenced by the availability of relevant contextual information. is the gas station actually where the map says it is?).
LinkedIn Engineering
JUNE 2, 2023
Discoverability: Consumers must be able to easily discover and access the correct data sources for their needs, whether it is stored in a centralized repository or scattered throughout the wider data landscape. Accordance: State-of-the-art data infrastructure technologies and tooling are not sufficient to fully realize our vision.
Monte Carlo
JULY 10, 2023
Data sampling If you’re working with large data sets where it’s impractical to evaluate every attribute or record, you can systematically sample your data set to estimate completeness. Be sure to use random sampling to select representative subsets of your data.
Databand.ai
AUGUST 30, 2023
Data Quality Rules Data quality rules are predefined criteria that your data must meet to ensure its accuracy, completeness, consistency, and reliability. These rules are essential for maintaining high-quality data and can be enforced using data validation, transformation, or cleansing processes.
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