Since the rise of DevOps about a decade ago, the amount of data that exists worldwide has grown from about 2 zetabytes to 50. Only some of that data impacts DevOps teams, of course. Nonetheless, it seems a safe bet that the typical DevOps team today has much more data to contend with than it did even just a few years ago, thanks to the stunning rate at which data volumes have increased.
One of the challenges for DevOps teams today, then, has become how to handle all of this data in a way that delivers actionable insights, and that helps drive the automation and collaboration that are at the core of DevOps. The more data you have, the harder it becomes to manage it in a way that improves DevOps.
That’s part of the reason why AIOps has become an increasingly central pillar for DevOps. By automating data ingestion and analysis in order to help automate IT processes, AIOps allows DevOps teams to make better use of all of the data surrounding them in order to improve software delivery.
Let’s take a look at how DevOps and AIOps go hand in hand.
What Is DevOps?
DevOps is a combination of development, quality assurance (QA), and operations. DevOps includes a range of best practices and methods.
The goals of DevOps are to:
Shorten the software development life cycle (SDLC).
Improve the response to market needs.
Speed the time to market of products.
DevOps implies a continuous communication cycle consisting of the steps shown in the image below.
The continuous part means that building, testing, and deploying the releases must be automated to some degree. The goals of automation are to achieve the following objectives:
Shorten the market delivery time of a product.
Improve the quality of the release.
Reduce the time between the releases.
Shorten the mean time to repair (MTTR).
Traditionally siloed development, QA, and operations departments were not aligned to support automation or achieve such goals. The effort would require these teams working together and helping each other with their assigned tasks. It would also require them learn new skills. For example, QA would have to learn scripting, and operations would have to learn automation.
As a result of the cross-training and collaboration, the boundaries of the silos got blurred. The cultural shift began to take shape.
When IT departments adopted the new DevOps culture and created best practices, DevOps teams were confronted with the next challenge: handling vast amounts of data—big data—from multiple sources.
Luckily, another shift in IT departments was also occurring: the adoption of data science.
With technological advances, businesses no longer had to analyze data stored in spreadsheets and relational databases. The interdisciplinary field of data science increased the speed of data mining, and enhanced the accessibility of data—growing amounts of data—that then gets organized in elegant and structured systems.
This enabled businesses to make data-driven projections about how their markets are evolving, for example, rather than having to rely so heavily on intuition.
Data science paved the way for artificial intelligence (AI) and machine learning to become mainstream in the work of the IT departments. Computers—faster, more sophisticated, and with increased memory—enable complex calculations and visualizations in a shorter time period, which is vital for business.
AIOps was born. DevOps was enhanced by refined and hyper-relevant data.
What Is AIOps?
AIOps is short for “artificial intelligence for IT operations.”
AIOps is all about how IT organizations use AI to manage data and information in their environments in order to gain actionable insights. In general, this concept relates to:
Other analytics techniques like root cause analysis
A major goal of AIOps is to enhance IT operations by aggregating and correlating data from multiple sources for analytics. With these capabilities, AIOps helps maximize automation usage and intelligence. In addition, it optimizes the productivity of highly skilled engineers as they manage complex tasks that can’t be automated.
How Does AIOps Enhance DevOps?
AIOps Enables IT Operations Management (ITOM). ITOM stands for IT operations management. What’s important here is that data can be collected from all available sources and shared across your whole IT team. This facilitates a more simplified analytics process without involving individual subject matter experts.
AIOps Supplies Answers with Supported Data. Data from multiple sources requires multidimensional analysis, which is not an easy task. You can slice and dice the different data sets and get processed data, but still be left wondering about the meaning of that data.
An AIOps platform delivers explanations, with supporting data. This is a valuable benefit when it is your job to convince, persuade, influence, or inform.
AIOps Helps HumanOps. Automation with AIOps (and DevOps) is not about getting rid of human intervention or creating fewer jobs for developers, QA staff, and operations engineers. On the contrary, it helps to automate the simple tasks so engineers can concentrate on the complex ones. This will decrease the workload and stress, which can result in a happier workforce.
AIOps Filters Your Data Automatically. Today’s data from IT systems is vast and varied. There are all kinds of events: logs, network anomalies, issue reports, and so on. To manually filter and identify these events is very complicated, laborious and error-prone.
AIOps algorithms automate those tasks. This automation also reduces noise; and when you connect your ticket and incident systems to the monitoring systems, you get a continuous real-time flow where incidents can be dealt with on-the-fly.
AIOps Improves User Experience and Accelerates Digital Transformation of Your Business. By increasing the speed of ITOM, issues can be solved faster and clients will see a shorter waiting time for fixes. This will immediately improve the user experience, leading to happier clients who, in turn, are more likely to promote your services.
Digital transformation of your business will also get a boost as your IT efforts generate positive results.
AIOps really enhances DevOps. How? By using refined data from IT systems to gain actionable insights. A company can benefit from the following advantages:
AIOps enables IT operations management (ITOM).
AIOps supplies answers with supported data.
AIOps helps add a human element to operations.
AIOps filters your data automatically.
AIOps will improve user experience and accelerate digital transformation.
IT systems will continually grow, and more data will be stored and processed. AIOps will help DevOps teams manage these systems well, so clients can work with them in an efficient and effective way.