In part 4 of this blog post series, we discuss how culture and innovation can either drive or block adoption of data driven Generative AI at exponential scale and what to do about it.
In part 3 of this blog post series, we discuss how a data fabric can be used to implement techniques borrowed from lean manufacturing to optimize the time required to integrate training data for LLMs and maximize business results.
We delve into sources of data friction that pose significant hurdles for utilizing enterprise data with LLMs
In the first article of this series, I spoke about how and why cloud IT providers use lock-in. I'll briefly revisit this and then focus on strategies to maintain buyer power by minimizing lock-in.
Here are two predictions for differences in industry evolution between Cloud IT and 20th century autos:
By allowing administrators to partition up underutilized physical servers into ‘virtual’ machines, they could increase utilization and free up capital. Unfortunately that hasn’t happened for the most part. It’s a poorly held secret that server utilization in enterprise datacenters is much lower than most people think as virtualization reaches saturation with about 75% of x86 servers now virtualized.
While most people working for cloud providers (I used to work at one) will tell you that Disaster Recovery is a great use case for cloud, our panelists weren't so sure. The feeling in the room is that utilizing cloud environment in addition to traditional on premise environments created a bunch of operational complexity and it was safer to keep both production and DR in-house.
None of this would have happened if Carly hadn’t made a mistake in firing all those HP salespeople and I hadn’t taken a risk.
Although successful with these large companies, something was always missing. I yearned to be able to create something on my own, and I’ve just taken an even bigger risk. After 12 years of planning, I left Rackspace in January to join the ranks of the entrepreneurs (or unemployed as some like to call us).
It's all about user generated data. You might think that GE is a product company, and you'd be right. But it's also a services company that services millions of industrial devices in the energy, transportation, healthcare, and manufacturing sectors. These devices create literally exabytes of data, data which GE currently uses to service and maintain it's customers equipment and gain insights on product design, and in the future will be used for much more.
