If you were one of the thousands of Splunk customers who joined us this year at.conf23, you heard our CEO Gary Steele say that Splunk's mission is to help you be digitally resilient. (And don't worry if you couldn't join us, because you can catch the keynote replays.) But what is digital resilience and how do you attain it?

In our service-driven world, businesses must provide the best user experience possible. Great service helps you retain long-term customers while also growing your customer base — to keep tabs on service performance, a few key metrics and signals come into play.

Adaptive thresholding is a key capability in Splunk IT Service Intelligence (ITSI) that enables customers to dynamically monitor the status of their key performance indicators (KPIs) and derive meaningful service insights and alerts.

The ability to make decisions quickly can mean the difference between success and stagnation. Of course, quick decisions aren’t necessarily the right decisions. The right decisions are the best informed, and the best way to get informed is through data. That’s what operational intelligence is all about. In this article, we’re diving into all things operational intelligence (OI), including key benefits, goals and how to get started.

Disruptive cybersecurity incidents become more and more commonplace each day. Even if nothing is directly hacked, these incidents can harm your systems and networks. Navigating cybersecurity incidents is a constant challenge — the best way to stay ahead of the game is with effective incident management.

You may be familiar with manipulating tokens via `eval` or `condition`, or showing and hiding panels via `depends` in Classic (SimpleXML) dashboards, and wondering how to do that in Dashboard Studio. In this blog post, we'll break down how to accomplish these use cases in Dashboard Studio, using the same examples that were shown at.conf23.

If you mention 'log data' at a crowded business event, you'll quickly be able to tell who's in IT and who isn't. For the average person, log data is about as thrilling as a dental appointment or reconciling a years-old bank account. At the mere mention of log data, their eyes glaze over as they search for an escape from the conversation. Conversely, IT professionals' eyes light up and they become animated when the topic of log data arises.

Distributed systems might be complicated…luckily, the concept is easy to understand! A distributed system is simply any environment where multiple computers or devices are working on a variety of tasks and components, all spread across a network. Components within distributed systems split up the work, coordinating efforts to complete a given job more efficiently than if only a single device ran it.

The availability and reliability of any IT service ultimately govern end-user experience and service performance, both of which have significant business impact. These two concepts — availability and reliability — are particularly relevant in the era of cloud computing, where software drives business operations, but that software is often managed and delivered as a service by third-party vendors.

Imagine yourself with a folder containing millions of gigabytes of data. If you were asked to process it with an Excel spreadsheet, you wouldn’t need to be a data expert to know that’s impossible. We refer to that amount of data as “big data”. Big data requires advanced techniques, tools, and methods beyond what regular data analytics entails, which is where big data analytics comes in.