On their own, enterprise applications and systems are not always straightforward. Writ large, they are complex, integrated environments, full of multiple data formats and structures. You spend a great deal of effort and time to define and maintain diverse data models among these integrated components. A Canonical Data Model helps reduce that burden significantly — by promoting a standard and consistent data model between connecting components. This article describes a few things to get you started.

DORA (the Digital Operational Resilience Act) recently came into force and will soon impact thousands of financial services organisations across the European Union (EU). In this blog, my colleague Clara Lemaire and I share some insights about the requirements of DORA, as well as how Splunk can support financial services organisations on their resilience journey. Let’s explore DORA!

DevOps gained popularity in order to combat siloed workflows, decreased collaboration and a lack of visibility across the software development lifecycle. While establishing a culture of DevOps has helped teams collaborate better and deliver reliable software faster, DevOps teams don’t necessarily have someone specifically dedicated to developing systems that increase site reliability and performance. That’s where a site reliability engineer (SRE) comes into the picture.

The ability to continue business operations for the foreseeable future is a key metric from a financial standpoint. But from a risk management perspective, all dimensions of an organization’s strategic and operational framework must be analyzed in order to… The last part relates to business resilience — and it’s what we’re going to explore here. (This article was written by Joseph Nduhiu. See more of Joseph’s contributions to Splunk Learn.)

Artificial Intelligence (AI) - or more specifically Machine Learning (ML) - and automation were big topics for many of our customers in 2022. Common reasons for the interest in AI and automation were to: increase efficiency, reduce manual processing, minimise human error and - especially for the use of ML - identify ‘unknown unknowns’.

Many business organizations begin their data analytics journey with great expectations of discovering hidden insights from data. The concept of unified storage — data lake technologies in the cloud — have gained momentum in recent years, especially with the exponential options for cost-effective cloud-based storage services. Big data is readily available. In fact, 2.5 quintillion (2.5 x 10^18 or 2.5 billion billion) bytes generated every day!

Yes, it’s that time of year again. As the New Year’s resolutions fade and the planning cycles kick-in, technical leaders in various organisations are often asked to get out the crystal ball to inspire our teams or steer an excited Board.

Today executives and customers expect IT and digital services to be available and performant at all times; compromised availability or performance is no longer tolerable. Think about it; when was the last time a digital service was unavailable and it didn’t make the news or social media? When was the last time you visited a website that was unavailable and you waited for the outage to be over, rather than finding an alternative in the moment?

When an IT or Security issue impacts a development team’s software how are they notified? Is your organization still relying on mass emails that lack context and most engineers have probably already filtered out of their inbox? Communicating between siloed tools and teams can be difficult. How would you like to put IT, Security, legacy processes, and business notifications specific to development teams right into one of their most important tools? Now you can!