We often find it hard to remember the world we left behind, but cast your mind back, say, 20 years, and we lived in a very different world. Personal Computers and the internet were on the rise, and businesses were all becoming connected. This provided companies with immense opportunities in terms of collaboration and digital adoption, and on the flip side, it eased the distribution of computer viruses. Today we barely even think about our antivirus software and policies.

This Eckerson Group report gives you a good understanding of how the Unravel platform addresses multiple categories of data observability—application/pipeline performance, cluster/platform performance, data quality, and, most significant, FinOps cost governance—with automation and AI-driven recommendations.

Data observability is the ability to monitor and understand the data that flows through an organization's systems. Organizations can monitor their data in real-time, detect anomalies, and take corrective action based on alerts. Organizations use data observability to collect, analyze, and visualize data from various sources to manage their system's behaviour across the data ecosystem.

As data becomes more central to our lives, it becomes more important to understand what it means and how it works. Data observability is the ability to see what's happening inside your organization. It's about knowing what your data looks like, where it comes from, how it’s collected—and how you can take advantage of that information to make better decisions. Data observability is about more than just understanding what your data looks like right now.

As we move into 2023, I am very excited to see all of the predictions for data and analytics and what they mean to Kensu. I looked at different publications and spoke with various industry experts and analysts to see if there were any conclusions we could draw.

Data teams and their business-side colleagues now expect—and need—more from their observability solutions than ever before. Modern data stacks create new challenges for performance, reliability, data quality, and, increasingly, cost. And the challenges faced by operations engineers are going to be different from those for data analysts, which are different from those people on the business side care about. That’s where DataOps observability comes in.