Knowledge is king in today’s information-driven business world, as most knowledge managers like me know. Having the right information at customers' fingertips can greatly influence their perception of the customer support experience. In today’s self-service world, a well-populated knowledge base with a reliable group of contributors is not enough to create a great experience. The content must be relevant and timely.
What if there was a way to deploy a new feature into production — and not actually turn it on until you’re ready? There is! These tools are called feature flags (or feature toggles or flippers, depending on whom you ask). Feature flags are a powerful way to fine-tune your control over which features are enabled within a software deployment. Of course, feature flags aren’t the right solution in all cases.
Two categories a software organization should always strive to improve in are: Data analysis is one way that your organization can improve the efficiency of incident management and overall application quality. However, the questions remain – which metrics should be collected? How can analysis of these metrics facilitate these improvements? Read on to hear about five key metrics essential to incident response.
With the release of Splunk 9.0 came support for SmartStore in Azure. Previously to achieve this, you’d have to use some form of S3-compliant broker API, but now we can use native Azure APIs. The addition of this capability means that Splunk now offers complete SmartStore support for all three of the big public cloud vendors. This blog will describe a little bit about how it works, and help you set it up yourself.
If you’re new to the concept or just trying to keep up with the conversation, Gartner defines Observability as the evolution of monitoring into a process that offers insight into digital business applications, speeds innovation and enhances customer experience. Some folks think that Observability is a new buzzword, but in fact the term was coined in 1960 by Rudolf E. Kalman, a Hungarian-American engineer.
Control flows are the backbone of automation. Identifying what to do with a set of data – and how – is a key component of high-value automation, but it can also be confusing to wrap your head around at first. What is a conditional? And what does it have to do with a loop? How do you deal with a set of information versus a single data point?
