Profile of Jessica Davis Senior Editor, Enterprise Apps
Member Since: 9/16/2015
News & Commentary Posts: 565
Jessica Davis has spent a career covering the intersection of business and technology at titles including IDG's Infoworld, Ziff Davis Enterprise's eWeek and Channel Insider, and Penton Technology's MSPmentor. She's passionate about the practical use of business intelligence, predictive analytics, and big data for smarter business and a better world. In her spare time she enjoys playing Minecraft and other video games with her sons. She's also a student and performer of improvisational comedy. Follow her on Twitter: @jessicadavis.
Articles by Jessica Davis
posted in September 2018
Everybody talks about blockchain, but is anybody really doing it? Here's a look at five use cases across industries.
Microsoft, Adobe, and SAP will enable a common data model in a Microsoft Azure-based data lake that allows for interoperability of data among platforms from all three companies.
AI and machine learning deployments are hitting the mainstream in enterprises, but executives still hesitate to blindly accept insights from inside the "black box" without seeing the logic behind them.
While cloud spending continues to grow as a percent of the total IT budget, traditional IT still commands the majority of IT dollars.
Enterprise organizations are already seeing benefits from their machine learning practices, but Forrester Research says they've only scratched the surface.
Decision intelligence engineering applies the technology already available to solve the business problems that you have today.
Machine learning, artificial intelligence, and unified data platforms are the focus for data scientists attending the Strata Data Conference in New York.
In a tight market for AI and machine learning, tech companies have always relied on a secret weapon -- tapping into the talent available in academia. But who will train tomorrow's AI experts?
AIOps takes the vast amounts of machine data generated by IT infrastructure and ingests, monitors, and analyzes it to ultimately predict issues before they occur.
Data engineers build the infrastructure and tools that data scientists and business users need to perform analysis and create machine learning models. Maybe that's why demand is high for this emerging category of IT pro.