I have had the privilege of being a SHARE Board Member for over five years. In that time, I have seen the association make a great deal of progress, which I think is important to reflect on here with you our members.
After events like SHARE Sacramento, I usually come away with new connections and new platform knowledge. But this time, my biggest take away was this: The next time you hear someone say people entering the workforce don’t want to work with mainframes, think again.
While we cannot control the future, we can do our best to shape it. I’d like to think that is one reason we gather for events like SHARE St. Louis. We leave knowing more than we did before and return to our organizations, ideally making improvements and moving our industry forward as a whole.
For many organizations, data is as much a terrifying liability as it is a valuable asset. My question is this: are we, as an industry, looking at data the wrong way? Are we missing a trick when it comes to understanding, managing, and securing our data?
Emerging technologies like artificial intelligence (AI), machine learning (ML), and deep learning (DL) are rapidly developing and evolving—as is what we can do with those technologies.
According to Jeff Bisti, Cognitive AI Architect at IBM, the mainframe is on the periphery of being able to partici
