IDC says to estimate reaching 175 zettabytes of data by 2025, a 61 percent increase from today’s data volumes. Business leaders and IT executives overwhelmingly agree that they can do more to harness this data, but are we as an industry lacking for imagination? Or do we simply not know where to start or how to progress? To add insult to injury, today’s enterprises are stuck in the land of silos and replication, and too much data wrangling that consumes an already oversubscribed budget.

If you’re reading this, you likely know what a log is, and what a metric is. But sometimes there are questions on their differences, whether you really need both, and if you should use dedicated solutions to manage each type. The answers? Yes, you need both; yes, they should be unified. Logs and metrics, aka machine data, are complementary.

The data visualization space is crowded. There are lots of tools, each purporting to be the tool that solves your data woes and leads you to insight via illustrations. But while you may get good-looking graphs, you are probably not seeing the behind-the-scenes pain from IT: analytics dashboards and vertical applications take multiple meetings for gathering requirements, and they discover the direction wasn’t quite right the first time around.

There’s a lot of talk about the ability of AI and machine learning to augment digital transformation journeys by creating better customer experiences and empowering employees to make decisions using data. However, IT and business leaders can sometimes face analysis paralysis when confronted with this topic because it means something different for every business – and it means shifting an entire company culture towards a new way of working. One key shift is making use of machine data.

Here at Devo’s Cambridge, MA office, we’ve been steeped in news of national sports league playoffs for several weeks. The games are great, even with the stress and uncertainty of overtime, but it’s gotten me thinking about the professional hockey and basketball players, and how they’ve become as successful as they are.

Logs began with UNIX in the 1960s, partly to preserve the culture of close communication in programming. Luckily, that culture has held fast as programming and technology have taken many different shapes and evolutions over the years, and today, the idea behind logs is still to maintain data for correlation and analysis to meet enterprise security and compliance needs.

Forrester Research has released The Forrester Wave™: Intelligent Application & Service Monitoring, Q2 2019 report and I am excited to share that Devo has been identified as a Strong Performer. Devo’s recognition as a Strong Performer is, in our opinion, a great validation of our data-first approach.

It’s a trap! If you’re going to learn how to escape the data management TCO trap, the first place to start is understanding what the cost factors are and how they are interrelated. Then, understand what the traps are and how you can avoid them. Follow along to learn more.

DevOps is the new normal, and cloud here is to stay – sound familiar? When you combine the two and distill the technology at the core, what you end up with is the realization of the importance of logs and log management. This is because logs at multiple levels help DevOps teams understand their application and even allow them to detect and address application issues before being promoted into production.

Log management is the answer to all of your digital transformation woes. No, hear me out. At its heart, log management is the (challenging) task of collecting and storing all machine-generated data from across your entire enterprise into a common repository. If this collection doesn’t happen, or if log collection is limited to certain datasets, there’s little chance of deriving those high value insights you dream of.