DevOps combines the information technology and software development teams and increases communication and collaboration between the two groups.
With DevOps, then, it becomes possible to adopt an approach to project management that allows for shorter times between new versions of apps or other products. As such, DevOps encourages continual evolution brought about by team or client needs and feedback.
Something called process data mining — analysing large amounts of data about processes and taking action accordingly — could enhance DevOps practices in several ways.
Here are five ways that leaders and IT professionals can apply process data mining to DevOps:
1. Let process data mining reduce decisions made on gut instinct or opinions
Data mining involves looking through collections of information and identifying patterns. Process data mining is more targeted because it aids in understanding the processes used within the DevOps team. Ideally, process data mining allows seeing the big picture, plus looking at things on a deeper level to identify bottlenecks or other areas where a process breaks down or deviates.
The information gained, then, allows DevOps team members to focus on making decisions on what the data says about their processes instead of letting people’s opinions or what their instincts tell to take the spotlight.
2. Explore insights gleaned from process data mining before making major workflow changes
When people don’t understand processes, they’re typically also in the dark about why unexpected things happen. They fail to realise that some non-beneficial aspects of processes have a domino effect that could cause other things to go wrong.
That’s why it’s smart to depend on process data mining before altering an existing way of doing things. In one example not connected to DevOps, Sellafield, a nuclear reprocessing and decommissioning site in the United Kingdom used both data mining and simulation technology to examine the cooling processes used in one area of the plant.
Six surprising ways businesses are impacted by RPA, OCR and NLP
Representatives at the organisation then learned that minor changes at the beginning of a process could have much more substantial effects than people think. That’s due to interconnected events that may have hidden outcomes not noticed before process data mining happens.
Company leaders should strongly consider utilising process data mining to increase their understanding of the purposes of steps in DevOps processes and the functions they serve. Otherwise, they could make haphazard changes with bigger-than-anticipated effects on the results.
3. Let process data mining reveal the gap between the real and ideal
Process mining is the data-driven improvement of business processes, and data scientists often use it to suggest ways to enhance performance. Process data mining works for companies and DevOps teams with processes in place, as well as those that still need to create processes. In the first case, people can compare the best practices for their process with what regularly happens within the team.
But, individuals at the enterprise level can also use process data mining to establish their processes. Information sources such as event logs give details about how and when people use tools.
Process data mining shows people how far away they are from the target of an ideal process, which can also mean it helps people solidify the processes a DevOps team follows. Then, it’s possible to know how to make the most meaningful process-related improvements and discover the things going wrong.
4. Stop only relying on periodic reporting for decision making
Process data mining allows for real-time data collection. The companies that successfully use DevOps rely on release cycle metrics that tell them about progress and quality levels. One helpful aspect of process data mining is that it allows DevOps teams to move away from merely taking actions during specified reporting sessions that may happen on a reoccurring schedule such as every week.
Five ways OCR tech can improve workflow efficiency
Instead, they can use interfaces that show them substantially more recent metrics. Then, decision making occurs outside the times when people formally deliver reports. When it does, it becomes easier to notice issues in the process and swiftly address them.
5. Include process data mining in audit preparation
Auditors were not initially part of DevOps teams, but that’s starting to change. Auditors and engineers initially only interacted when the auditors advised of issues. Then, examinations of the DevOps processes pinpointed what caused those issues. But, when DevOps teams stay aware of audit controls and let auditors continually participate, improvements happen.
Where does process data mining come into play? Auditors who get acquainted with it could achieve their audits more efficiently than if they did not have the information provided through process data mining.
When auditors know about the day-to-day DevOps processes, they can rely on substantially more than what small data samples offer. Some auditors even use machine learning features to uncover things they may miss without those tools.
Best DevOps practices for 2019
Process data mining makes DevOps teams more functional
Most teams don’t know where the problems in their processes exist, or they may go through trial and error to fix known problems.
Process data mining shows specifics about processes and their effects, equipping DevOps professionals with the ability to recognise which changes should occur to help the team excel and avoid wasting resources to figure out how to make improvements.