Application Lifecycle Management
In this article, I will briefly describe the process of application lifecycle management. I will also talk about the role analytics can play in the process and the advantages of including analytics stakeholders early in the process.
Application lifecycle management (ALM) combines all the resources required to manage an application from the inception to release and further on to maintenance of apps once it’s released to public use. In the past, methods like the waterfall approach were inefficient and frequently led to deadline overruns and budget increases, bogging down the project’s progress. However, the integration of various teams to collaborate at different stages proved to be a defining moment, leading to a seamless process.
- Application Governance
Governance describes the process of deciding various attributes of the application; developing the initial idea of the app involves taking inputs from multiple teams and how the application can be molded to fulfill business needs and goals.
Benefits of application governance:
* Aligned business strategy
* Developing a business case
* Offers continuous monitoring
* Funding projects which deliver the highest value
* Clear accountability and control
2. Application Development
After the initial goals are met, the project moves forward to the development process, which involves converting the idea to the finished product. There are two significant steps in the application development process:
- Design: This involves creating the first draft of how the application will look and feel to the consumer. This process involves multiple iterations.
- Engineering: this process involves creating the app. it includes the front-end engineering and back-end engineering teams — collaborating with the design team to convert designs to working applications.
3. Software Testing
Testing is one of the most crucial steps in the ALM but is often overlooked. In the new agile framework, development and testing go hand in hand. The continuous feedback to the software engineering team helps better planning and early bug detection.
4. Operations and Maintenance
After completing the final stages of testing, the product is released for public use. The operations and maintenance team are involved in the ALM from this point onwards till the end of a product lifecycle. Regular updates are also a responsibility of this team.
Analytics in the ALM process
Analytics can help managers answer essential questions during the project lifecycle. In addition, analytics can help stakeholders unearth insights and information which may be hard to see otherwise. However, a lack of planning in this area leads to significant bottlenecks at the final stages of the ALM process and may lead to significant project delays.
Some other advantages of including analytics in the ALM process:
- The project will have a rich amount of data, which helps optimize the functioning of analytics teams and hence, positively affect the functioning of other groups.
- Analytics can help reduce the workforce required by early predictions and meaningful insights.
- Analytics can help streamline and homogenize the decision-making process.
- The use of analytics can help provide everyone involved in the ALM a complete picture and thus help make more informed decisions.
- It has been empirically proven that using analytics from an early stage will lead to better chances of project success.
- Analytics can help provide a continuous report of the changes being done in the product at each stage and, hence, help standardize data-driven decision-making.
- The use of new machine learning techniques can help managers be proactive rather than reactive.
- Analytics can help assess past similar projects, decreasing the chances of failure if the insights are taken into account in the ALM process.
Involving analytics in the early stage of the ALM also helps make an accurate prediction of analytical resources required once the product is released for public use. In addition, analytical techniques can help managers quickly find important needles in massive haystacks of data.
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