Make no mistake – we’re living in the era of Big Data. From the moment that you wake up and go through your morning routine to the time that you return home, you’ve both produced and relied on Big Data that – in theory – made you more productive. But the very moment when that data becomes inaccurate or unactionable, then Big Data produces a big headache.
According to the Digital Enterprise Journal, 70 percent of the information today’s organizations generate and collect is not delivering value. There is no way of knowing how to put it to work because the data lack the context necessary to provide insight and meaning.
To be clear, the answers and ability to make the data useful are still present. But until the data has the proper context, organizational efficiency becomes a grueling race against time, draining resources, and potential.
It stands to reason, therefore, that before you can begin to realize any significant results from your investments in AIOps, management, monitoring, or automation, you need to get your data house in order. That’s a daunting task, but others have done it. They’ve taken the journey, learned the lessons, and shared their experiences. In some cases, they’ve written white papers to serve as a guide for those planning their own journeys.
Here’s one that might help if that’s where you are. It’s called, AIOps: A Guide to Operational Readiness; It’s All About the Data.
We talk a lot about context as that quality that gives data value, but what do we mean? Context is the association that data has with the elements and conditions of your network’s operational health. When you aren’t feeling well, and you stick a thermometer under your tongue, you need to know that 98.6 degrees is a normal temperature, otherwise whatever reading you get is merely data. That’s the context by which you know 101 is a fever, and that context helps to determine what is wrong and what steps you need to take to get well.
There are five steps required to ensure your data has context and to understand what you need to do to improve network health:
- Data Collection – While this might seem obvious, it’s important to make sure that there are no gaps in your collection of data. That means comprehensive, real-time discovery is vital to ensuring every input is known and up-to-date.
- Data Preparation – This process “cleanses” collected data, identifying and correcting errors, eliminating duplicated data, and transforming it into a common model that enables correlation of events and performance across the entire IT environment.
- Data Enrichment – Once you’ve collected and prepared your data you can begin to enrich it with the associations it needs to give it context. Enrichment enables infrastructure, application, and business service mapping, giving you a three-dimensional view of your operations.
- Data Analysis – With context established you can now understand what is happening and what it means at any given time. Because networks are constantly changing, this step allows you to establish dynamic baselining, thresholding, and event correlation to understand how changes affect operations.
- Data-Driven Action – Finally, when you understand what the data is telling you about network health, you can take decisive action to address errors and avoid problems. Better yet, you can achieve the holy grail of IT operations and automate your processes, like CMDB enrichment and self-healing, to minimize errors, downtime, and other conditions detrimental to efficient IT operations.
Do you want to continue to weigh yourself down with more and more data that does nothing to help your organization, or do you want to separate yourself from the crowd and become part of the 29 percent of companies that Forrester Research says are turning their Big Data into Actionable Data? Before you take that first step, remember: if it’s all about data, it’s all about context.