As organizations look for opportunities to put data analytics to work, DevOps is one area that deserves attention. In many organizations, data scientists and software developers are still siloed, but bringing their talents together makes sense. Development informed by data can help teams in a number of ways.

The goal of DevOps is to shorten the time required to get ideas into practice by accelerating the development process in a quality-controlled way. Its methodology depends on a steady, reliable source of clean, organized and indexed data. Generating the right data during the DevOps process, and then using the resulting insights to inform the next iteration, can minimize error and increase the likelihood of success.

Analytics is a natural complement to the DevOps feedback loop, providing valuable insights that inform continuous improvement in decision-making. Collaboration, however, is essential. When developers know what data they need to collect, they can create code that will write logs to a file for analysts’ review. That requires not only joint planning but also clarity about what the goals are — both for development and for the business as a whole.

It’s no secret that software and applications drive business today — and the future is only going to be more app-driven. So, it’s imperative for businesses of all shapes, sizes and industries, whether cloud newbies or cloud natives, to be thinking strategically about how to develop their software. And to do that, business leaders need to be thinking about DevOps.

Yes, DevOps is a buzzword, but that doesn’t detract from its growing importance for business. To help our customers understand what DevOps is, we started a podcast, Simplifying DevOps.

For decades, colleges and universities have struggled with making online and blended learning courses as meaningful and engaging as in-person classes. At least momentarily, the COVID-19 pandemic has put many students and instructors on a level playing field. More courses than ever, including those that have historically been offered exclusively in person, are being taught through a remote or blended model.

The current environment represents an enormous opportunity for higher education institutions to step back, take stock and seek out solutions to increase the quality of their remote and blended learning approaches.

Here are three ways that technology can help.

2020 is over. And while it was an unprecedented year, the As a Service companies like Five9 helped make the widespread adoption of remote work a reality. For more As a Service information, check out my other blog series.

It takes a catastrophic event to stress test a system. Last year was just that event, as we saw massive amounts of internet traffic, cloud services up and down and sometimes degraded. Through it all, Five9 continued to function and moved forward with mergers and acquisitions.

Within CDW’s AWS practice, we maintain a number of AWS accounts for managing our day-to-day business activities. One of the accounts is the “Sandbox Training” account that provides an environment for our CDW coworkers to learn and experiment with a multitude AWS services.

Periodically, we go to the account and try to understand the cost and resource usage pattern within the account. Recently I found out that there were many snapshots created by AWS Backups in our training account. Most likely, the vault was created as a part of the training and learning exercise. The AWS Backup policy was created to back up a few EBS volumes every night. Due to an oversight, the user forgot to delete the backup policy after the experimentation was over.

In a world where software drives nearly every technology, the term software-defined everything (SDx) strikes me as a bit redundant. After all, what isn’t dependent on software these days? Even in the technologies we readily associate with hardware — storage, for instance — the magic is in the software that runs on top.

As SDx matures, this once holistic idea is becoming more narrowly segmented. As with any evolving technology, opinions may differ on what falls under its broad umbrella. Does SDx include Infrastructure as Code? Artificial intelligence? Cloud operating models?

IT professionals may enjoy debating what SDx is, but there’s another valuable approach we can take to the conversation, and that’s to define what SDx isn’t. To that end, let’s examine and debunk four common myths about SDx.