What's ahead for DataOps? From automated data analysis to the transformation of subject matter experts into data curators, we look at what's next in the last article in our four-part series.
- By Mark Marinelli
- April 30, 2019
Two real-world examples demonstrate how putting DataOps principles into practice can yield big payoffs. (Third in a four-part series)
- By Mark Marinelli
- April 29, 2019
To provide robust data logistics, your data fabric will need these four traits.
- By Jack Norris
- April 26, 2019
Learn about data and analytics strategies from sports, plan for AI-driven, highly personalized content, and find out how predictive analytics is working for universities.
- By Upside Staff
- April 23, 2019
Traditional data quality best practices and tool functions still apply to big data, but success depends on making the right adjustments and optimizations.
- By Philip Russom
- April 19, 2019
Data quality issues become even more important as machine learning use grows. DataOps and data wrangling help enterprises address this vital problem.
- By James E. Powell
- April 16, 2019
Machine learning applications are dependent on, and sensitive to, the data they train on. These best practices will help you ensure that training data is of high quality.
- By Greg Council
- April 15, 2019
Adding property rights to inherent human data could provide a significant opportunity and differentiator for companies seeking to get ahead of the data ethics crisis and adopt good business ethics around consumer data.
- By Richie Etwaru
- April 9, 2019