Profile of Jessica Davis Senior Editor, Enterprise Apps
Member Since: 9/16/2015
News & Commentary Posts: 637
Jessica Davis is a Senior Editor at InformationWeek. She covers enterprise IT leadership, careers, artificial intelligence, data and analytics, and enterprise software. She has spent a career covering the intersection of business and technology. Follow her on twitter: @jessicadavis.
Articles by Jessica Davis
posted in April 2019
As AI and underlying technologies such as machine learning and deep learning spread, we need to create a field of study that examines machine behavior.
In its effort to cure pediatric cancer, the research hospital faced challenges associated with sharing terabytes of data with researchers. Here’s what they did.
Distributed teams can offer your organization an advantage over your peers. Here’s how.
Osano and its browser plugin, Privacy Monitor, aim to provide consumers with transparency regarding consumer data privacy policies. The company will release data supply chain tools for companies later this year.
Artificial intelligence is adding value for many enterprise organizations, but getting it into production takes longer, costs more, and adds less value than many organizations are expecting.
T-Mobile was using the same analytics platform it had in 2005 when the iPhone was new and George W. Bush was president. Now, the mobile carrier has modernized.
Adding external data sources to your analytics and machine learning initiatives can provide new dimensions of insights. Here are some sources of data you can tap.
The big enterprise IT vendors -- IBM, Microsoft, Oracle, and SAP -- all want you to buy more and pay more. Here are some tactics for you to protect your organization and get the best deal as you plan and engage in negotiations with them.
Operationalizing those data science, analytics, and machine learning projects is one of the top concerns of IT leaders. But the same tried-and-true best practices you've used for other IT projects can guide you on these new technologies, too.
CIOs say that AI and machine learning are the top technologies that will drive transformation, but there aren't many enterprises who have them in production, yet. Here's how they are planning to get there.