I have often heard it said that "data is the new oil" - it has value if it can be extracted and used correctly. How to extract value and leverage this opportunity - and occasional threat - is what I most commonly hear is keeping today's executives awake at night. From mom-and-pop shops to global enterprises, within nonprofits and the public sector, every leader wants to become data-driven. Unlocking the power of data is, obviously, critical to success.

Our Data-to-Everything vision is to bring data to every question, decision and action—blockchain data is no exception. Part of making that vision a reality is getting data into Splunk where you can start to investigate, monitor, analyze and act in order to turn data into doing.

It is imperative for organizations to monitor performance, security and stability of their blockchain integrations. Splunk makes it easy to achieve this observability, not just with blockchain but with all enterprise infrastructure. Our customers have told us this is simply a necessity for getting to production and also makes application development faster. Based on Ethereum, Quorum is an open source blockchain platform built for business and backed by strong partnerships such as J.P.

Remember when you wanted great alerts, so you read our past two blogs about cyclical statistical forecasts and anomalies? Hopefully, the techniques in those blogs gave you some great results. Here we’re going to show you another way of finding anomalies in your data using a slightly different technique.

Over the past 24 months or so, I have been studying investing/trading while also working to become more proficient with Splunk. I like to combine activities and gain momentum, so I decided stock market and economic data would be the perfect way to dig deeper into Splunk and hopefully improve my investing/trading. In the beginning, I only looked at it as a way to learn more about Splunk while using data that was interesting to me.

In so many ways, speed is a security analyst’s best friend. From threat detection to containment to response – the faster you are, the more secure your business will be. It’s exactly why metrics like dwell time, MTTD (mean time to detect) and MTTR (mean time to respond) exist. It’s a barometer for the strength of your organization’s security, and a gauge of success for any good security team.

How would you like to be in touch with what’s happening at your doorstep at all times — whether it’s a package delivery, or your loved ones arriving home — no matter where you are? Now think about the folks at Arlo, a leading home automation company, who deliver on this promise for 3.4 million homes in over 100 countries. We hear from Suma about how she uses Arlo to get notified as soon as her kids arrive home from school.

In the first part of this blog series, we presented a use case on how machine learning can help to improve police operations. The use case demonstrates how operational planning can be optimized by means of machine learning techniques using a crime dataset of Chicago. However, this isn’t the only way to predict and prevent crime. Our next example takes us to London to have a look at what NCCGroup’s Paul McDonough and Shashank Raina have worked on.

Hi, Let's take a look behind the scenes and find out how Security Orchestration Automation and Response (SOAR) solutions can have a positive impact on your security investigation and response efficiency. In this article, I'll also highlight how Phantom-mobile makes your life as the “officer on duty” a lot easier.

Howdy Splunkers, I’m back and this time I’m packin’ a serious punch. I’m so excited to announce the availability of my new IT Service Intelligence (ITSI) Content Pack for Monitoring and Alerting.