Profile of James M. ConnollyEditorial Director, InformationWeek and Network Computing
Member Since: 11/18/2013
News & Commentary Posts: 127
Jim Connolly is a versatile and experienced technology journalist who has reported on IT trends for more than two decades. As editorial director of InformationWeek and Network Computing, he oversees the day-to-day planning and editing on the site. Most recently he was editor of UBM's All Analytics. He has written about enterprise computing, data analytics, the PC revolution, the evolution of the Internet, networking, IT management, and the ongoing shift to cloud-based services and mobility. He has covered breaking industry news and has led teams focused on product reviews and technology trends. He has concentrated on serving the information needs of IT decision-makers in large organizations and has worked with those managers to help them learn from their peers and share their experiences in implementing leading-edge technologies through such publications as Computerworld. Jim also has helped to launch a technology-focused startup, as one of the founding editors at TechTarget, and has served as editor of an established news organization focused on technology startups at MassHighTech.
Articles by James M. Connolly
posted in February 2017
Recruiting firm Randstad's annual salary survey shows data pros are in the upper stratosphere, and DevOps experience is starting to justify a premium.
Breaking down the silos is critical in an enterprise IT strategy, and there are ways to get different IT and business groups on the same team.
A Verizon report highlights how big data complexity and a shortage of data science talent are hurdles for an IoT implementation, but we also have to remember the key best practice of having a business goal as part of any analytics initiative.
The business-analytics disconnect remains a hurdle for adoption of data-driven decision making. Maybe it's time for the analytics team to hit the road and show employees what data can do for them.
Dealing with the data science talent crunch isn't just about competing for talent; it also requires the right data science strategy to help the analytics team be effective and efficient.