Plus: Traps enterprises should avoid, data governance best practices, the need for data fabric and dataops along with much more.
In this post, Bernard Marr gives some tips for making sure you apply AI ethically within your organization.
Here are three common traps that companies should steer clear of as they get themselves AI-ready.
Businesses need to put guidelines in place as they grow to make sure that there is consistency in what people are referencing.
Data purging rules have long been set in stone for databases and structured data. Can we do the same for big data?
The promise of data-driven enterprises may be just that if they cannot overcome their data management challenges.
Embedded analytics: A building block towards productizing data
How to Organize Data Labeling for Machine Learning
Data mesh: the next big data architectural shift
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