The conservation of quantum information theory states information can neither be created nor destroyed. Stephen Hawking used this theory to explain how a black hole does not consume photons like a giant cosmic eraser. It is clear to me that neither Stephen Hawking, nor any quantum physicist, has ever worked in IT.
Outside the realm of quantum mechanics we have the physical world of corporate offices. And in the physical world information is generated, curated, and consumed at an accelerated pace with each passing year. The similarity between both realms? Data is never destroyed.
We are now a nation, and a world, of data hoarders.
Thanks to popular processes such as DevOps, we obsess over telemetry and observability. System administrators are keen to collect as much diagnostic information as possible to help troubleshoot servers and applications when they fail. And the Internet of Things has a billion devices broadcasting data to be easily consumed into Azure and AWS.
All of this data hoarding is leading to an accelerated amount of ROT (Redundant, Outdated, Trivial information).
Stop the madness.
It’s time to shift our way of thinking about how we collect data. We need to become more data-centric and do less data-hoarding.
Becoming data-centric means you define goals or problems to solve BEFORE collecting or analyzing data. Once defined, you begin the process of collecting the necessary data. You want to collect the right data to help you make informed decisions about what actions are necessary.
Three Ways to Become Data-Centric
Here are three things you can start today in an effort to become data-centric. No matter what your role, these three ways will help put you on the right path.
Start with the question you want answered. This doesn’t have to be a complicated question. Something simple as, “How many times was this server rebooted?” is a fine question to ask. You could also ask, “How long does it take for a server to reboot?” These examples are simple questions, yes. But I bet your current data collections do not allow for simple answers without a bit of data wrangling.
Have an end-goal statement in mind. Once you have your question(s) and you have settled on the correct data to be collected, you should think about the desired output. For example, perhaps you want to put the information into a simple slide deck. Or maybe build a real-time dashboard inside of Power BI. Knowing the end goal may influence how you collect your data.
Learn to ask good questions. Questions should help to uncover facts, not opinions. Don’t let your opinions affect how you collect or analyze your data. It is important to understand how assumptions form the basis for many questions. It’s up to you to decide if those assumptions are safe. To me, assumptions based upon something measurable are safe. For example, your gut may tell you that server reboots are a result of O/S patches applied too often. Instead of asking, “How often are patches applied?” a better question would be, “How many patches need a reboot?” then compare that number to the total number of server reboots.
Summary
When it comes to data, no one is perfect. These days, data is easy to come by, making it a cheap commodity. When data is cheap, attention becomes a premium. By shifting to a data-centric nature, you can avoid data hoarding and the amount of ROT in your enterprise. With just a little bit of effort, you can make things better for yourself, your company, and help set the example for everyone else.
