Data Quality
Data Quality | News, how-tos, features, reviews, and videos
6 ways to avoid and reduce data debt
Data debt can be just as bad as tech debt, causing security and trust problems if it isn’t addressed throughout the data pipeline.
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Why observability in dataops?
Because building reliable data pipelines is hard, and the first step to becoming a data-driven organization is trusting your data.
2023 is all about analytics
Getting your data to tell you what you don’t know requires analytics. And analytics requires cloud.
MIT startup DataCebo offers tool to evaluate synthetic data
Synthetic Data Metrics is an open-source Python library for evaluating model-agnostic tabular data by pitching machine generated data sets against real data sets.
What devops needs to know about data governance
Industry leaders agree that data governance belongs to everyone in IT. Managing the privacy, security, and reliability of data impacts all aspects of the business.
3 data quality metrics dataops should prioritize
Data-driven decisions require data that is trustworthy, available, and timely. Upping the dataops game is a worthwhile way to offer business leaders reliable insights.
My data killed my cloud project!
As we push more data to the cloud, avoidable mistakes are hampering migration. The biggest culprit: messy data with inadequate security and integration.
Use synthetic data for continuous testing and machine learning
Where real data is unethical, unavailable, or doesn’t exist, synthetic data sets can provide the needed quantity and variety.
Get your cloud data right the first time
All cloud migration projects must deal with how data is used by cloud and non-cloud applications and humans. These 3 pragmatic tips should get you beyond the confusion.
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