Security analysts have long been challenged to keep up with growing volumes of increasingly sophisticated cyberattacks, but their struggles have recently grown more acute. Only 46% of security operations leaders are satisfied with their team’s ability to detect threats, and 82% of decision-makers report that their responses to threats are mostly or completely reactive — a shortcoming they’d like to overcome.

The success of data-driven initiatives for enterprise organizations depends largely on the quality of data available for analysis. This axiom can be summarized simply as garbage in, garbage out: low-quality data that is inaccurate, inconsistent, or incomplete often results in low-validity data analytics that can lead to poor business decision-making.

At first glance, there may seem to be little not to love about the ELK Stack. It’s open source. It’s free (if you set it up and manage it yourself, at least). It’s a widely used solution with a thriving ecosystem surrounding it. But if you’ve ever actually built and managed an ELK stack environment, you have probably found that the theory doesn’t match the reality. The ELK stack is full of hidden costs, and it often fails to deliver real value over the long term.

If your business is among the more than one million organizations that use Amazon Web Services (AWS) to host applications and data, there is a good chance that you struggle to monitor AWS. After all, although AWS makes it easy to deploy cloud services, collecting and analyzing data about those services in an efficient, centralized way can be a real challenge.