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
Applying DevOps-style test automation to your projects can guarantee a high level of data quality.
- By Wayne Yaddow
- March 22, 2019
Most enterprises can't fully leverage their data because they haven't established policies that build trust in the data and in collaboration.
- By Rami Chahine
- March 18, 2019
Is it possible to keep data preparation processes from becoming unmanageable?
- By Ian Macdonald
- February 15, 2019
How data management impacts customer experience, factors for choosing self-service architecture, and improving data quality and risk management.
- By Upside Staff
- February 7, 2019
If you're serious about data quality, pay attention to these four key trends in 2019 and beyond.
- By Geoff Grow
- December 21, 2018
From privacy to pricing, scalability to self-driving technology, 2019 will be a crucial year for data prep advancements.
- By Piet Loubser
- December 17, 2018