Profile of Jessica Davis Senior Editor, Enterprise Apps
Member Since: 9/16/2015
News & Commentary Posts: 505
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 January 2019
CIOs and IT organizations will take a more central role in all of business in a few years. Here's what you can expect.
Google's appeal of its GDPR fine over data privacy and transparency just may illuminate the best way forward for enterprise IT.
Executives and entrepreneurs offer advice about finding mentors, creating mentor programs, and giving back.
Combining computer vision, IoT, and AI creates a system that delivers more value than those three technologies in isolation, as the retail industry is highlighting.
Take a look at the forces shaping the job market for data scientists and AI experts, according to a leading quantitative executive recruitment specialist.
Virtual employee assistants and voice interfaces to enterprise applications are relatively rare in enterprises today, but Gartner is predicting big growth in the next few years.
If you want customers to be loyal to your company or to your brand, you need to protect their data and treat it with respect.
Retailers will invest more heavily in IT technology than other industries do in the coming years. Here's how this industry will leverage technology to take them into the future.
What are some of the big data-oriented trends for enterprise programs and culture in 2019? Here's a look.
Plenty of enterprises have run pilots or small-scale projects that leverage machine learning and AI. Scaling those to high-value, large-scale data projects can get complicated.