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.

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.

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.

Automating security operations, or SecOps, is one of the most powerful strategies organizations can employ to protect themselves and their data. All too often, however, they don’t leverage it as well as they could.

Security automation pulls together data from disparate systems to make information more visible and actionable. For example, a solution may not only alert IT teams to a critical vulnerability on enterprise devices but also initiate remediation. It could spot a vulnerability that can be solved with a specific patch, so staff members can determine the best way to solve the problem.

Here are a few of the ways organizations are putting SecOps to work, delivering greater efficiencies and peace of mind to their IT teams.

Cisco customers have a choice between Application Centric Infrastructure (ACI) and Ethernet VPN (EVPN) for building their data center network fabrics. Since these technologies are thought to be interchangeable, the choice often falls to cultural, political, or other “Layer 8” decision-making. But the fabric technologies are, in fact, different in ways both subtle and significant, and one’s choice has a profound impact on fabric orchestration, operations, and staffing.

I hope you’ll join me all the way through this three-part blog series as I compare and contrast these technologies and provide a foundation for deciding on the best choice for your organization’s needs.

Let‘s face it: Cybersecurity teams are overwhelmed with work. There simply is not enough time in the day to perform all the high-value tasks they are responsible for, such as consulting with project teams, conducting risk analyses and developing strategic security plans. To make matters worse, security teams often find themselves consumed with time-sensitive and repetitive operational work. Anything they can do to reduce this burden increases their ability to add value to the organization.

Automation has the potential to help with this heavy lifting. Any time security teams find themselves performing work that requires following a repetitive process, it is likely that automation can play an important role in reducing the operational burden. In our work with CDW customers, we have found a few common areas where automation can help increase the efficiency and effectiveness of cybersecurity teams. Let’s look at a few examples.