Managing a company’s full application portfolio is vital for business success. But what to do when it tags along with its high costs, manual processes, inefficient resource allocation, and human errors.
Well, for starters, we can think of an optimized solution for the management of the full application portfolio, which is automation. Automated application management coupled with artificial intelligence (AI) and machine learning can save a company’s costs by up to 40%, which can eventually reduce IT labor costs and improve efficiency as well as productivity.
The companies need to understand that the transition from manual to automated application management can be complex, expensive, and time consuming, at least during the initial phases of a project when most of the startup and planning costs are incurred. A key point to understand about automation is that it is an ongoing process and a part of a continuous improvement strategy, not just one-and-done activity.
The next section will talk about the key steps in the transition from a manual process to automation.
Elements of automation
It involves the identification of time-consuming support activities such as sending status updates, route tickets to the right people, give instant feedback to service desk personnel, etc. This helps in defining the implementation scope and respective treatments for the activities taking up the highest proportion of the support effort. The process of evaluation is both things – one-time initial effort and also an ongoing activity that helps in identifying issues and driving toward increased optimization.
It also involves pattern analysis to sort a specific type of problem with one root cause. The pattern analysis can also identify a lack of communication with users, to be sure they are notified when the problem has been fixed. Capturing metrics around mean time to repair (MTTR) allows businesses to identify a need to use automation to consistency and increase quality.
One step before the automation tools are deployed is that the application portfolio needs to be evaluated and checked if it works effectively with the new tools. It will include coding, modeling, reorganizing the application architecture, or adding APIs (Application Program Interfaces) for overcoming the lack of automation interfaces.
In an ideal scenario, data from various sources is combined in a data lake where the analytics can identify the repetitive patterns. These patterns will suggest where there is a scope of real optimization—may be the application of a heat map to learn which components cause the most common problems over time.
Baselining is the process of checking the performance of the automation program. It involves marking or comparing the performance to a historical metric or baseline. At this step, it is important to identify when applications are performing worse or better than they do usually in present conditions. Add to it, the future instrumentation will also rely on the data collected during the baseline process.
Application change automation and configuration occur at various stages of the application life cycle, such as building, testing, starting, and production. These things take place once; however, minor changes and enhancement happen over time.
The development of new code and correction in the existing script will occur specifically in the case of an operational issue. These things related to repair have to be specific as they are unique and expensive in nature. Also, there should be daily checks, which are like a form of a manual check undertaken to validate the application’s health.
Tooling is part of every process, not just automation of app management. It requires investment and the adoption of next-generation tools. Simply upgrading or augmenting existing tools is not sufficient as the core function of automation here is to improve efficiency, and it is possible only through revamping the toolset.
So, it is suggested to opt for Modern Application Performance Management (APM) tools that go beyond the basic performance monitoring of a single application to understand the dependencies and relationship among the app and app components. The abilities of APM include analyzing the entire application stack or root cause identification and productivity enhancement through data investigation.
Subsequently, it is possible to leverage robotic process automation (RPA) tools that automate human interactions with the applications. There can also be run book orchestrations, which can link incidents with their resolutions.
Investments across these tools and workflows will accelerate the process of automating application management.
Culture sounds a little tricky here, but it has a huge part to play in effective automation adoption. For instance, if there are changes’ requests related to the environment, it can affect the adoption of automation to a great extent. So, everything right from processes, workflows, to approval gates, all need to be redefined to ease the adoption of automation.
On the one hand, where the process of effective testing and demonstration can build stakeholders’ confidence, on the other hand, it can also bring a concern that unmanaged changes will create risk in the environment. Automated approval workflows can apply control gates, which will make the adoption process to move from traditional mode to a highly automated one.
Accomplishment of automated app management
With the above abstract, it is quite clear that the automation of app management for working environments promises to increase capacity by allowing humans not to do work that is well-suited to machines. The quality is also achieved by avoiding mistakes and helping augment human decisions by identifying the root cause of the problems. The right kind of automation increases speed by reducing detection times and speed resolution of problems and incidents, and speed the deployment of code. It will also help in achieving the reduced cost by reducing the effort of manual labor.
Thus, it is important for the CIOs to understand the process of setting up the automated system. For example, putting together an inventory of application and evaluation includes questionnaires, interviews with stakeholders, and reconsideration of datasets. Though the investment in automation is a sustainable model of operation, support of personnel is needed for treatment application, building the toolset, and onboarding applications.
Conclusively, optimization creates an opportunity for organizations to reinvest savings in initiatives creating higher business value and see the ratio of effort spent on operations swing in favor of development projects. As automated application management comes into existence, it will lead to reduced costs year over year.