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DevOps Automation: Achieve Faster Time to Market in 2024

Updated on Jan 12
5 min read
Written by
Cem Dilmegani
Cem Dilmegani
Cem Dilmegani

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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As the business world got deeper into the “software product economy”, the global revenue of the software development market increased steadily and is expected to continue to grow (see Figure 1). 

Figure 1: Increasing market size of the software development market. Source: Statista

Increasing revenues in the market and recent digital transformation trends after the pandemic paved the way for a more competitive market. It has never been so crucial for enterprises to optimize their software development and deployment process to gain a comparative advantage. DevOps has been a solution for many businesses looking to facilitate the development and production process. However, the goals set by DevOps “philosophy” can only efficiently be reached through automation of some phases. 

What is DevOps Automation?

DevOps automation refers to software development and IT operations teams working in tandem and using automation tools. The purpose of DevOps automation is to minimize the miscommunication between teams and use automation tools to decrease the number of repetitive tasks for allocating more time for more strategic decisions. Such automation aims to optimize the feedback loop and reduce the software development life cycle to provide rapid IT service.

Traditional Method – Waterfall Model

Traditionally, development and operation teams work on different functions and, most of the time, in different physical environments. The teams have different managers, goals, key performance indicators, and strategies. This would cause miscommunications between the groups, slowing down the deployment process. 

Before DevOps automation, the most used classical approach to the development cycle was the Waterfall model. The name referred to the linear nature of the progression for the deployment in the model. The model is divided into several stages, and the output of each step is an input for the next step. Hence, the developers cannot implement any modifications once the specific phase is completed. Such complication is based on the linear-sequential functionality of the Waterfall Model. 

 How did the Waterfall Model work? 

  • Requirement Analysis – The analysis team gathers requirements and finalizes them in a product requirements document that captures the answers to the stages of the process, task division, project milestones, the duration of each phase and the whole process, resource requirements, and key dependencies. 
  • System Design – In this phase, the project workflow is designed. The team decides on what hardware and programming languages they will use and how different parts of the project will operate together for the software.
  • Implementation – The implementation phase is where the team starts designing the project based on the instructions developed in the previous two stages.
  • Testing – At this stage, different modules of codes are integrated and analyzed to ensure the effective operation of the project. Usually, software development teams are responsible for the testing phase. However, in some organizations, this responsibility falls on the quality assurance teams.
  • Deployment – Deployment is when the product is introduced to the buyer/user. 
  • Maintenance – As there may still be bugs after alpha and even beta testing, maintenance is the last phase of the project. 

What are the challenges of implementing the waterfall model?

Apart from the miscommunication caused by two different teams working on the project, a significant downside of the Waterfall model is that testing is done only towards the end of the project. This disables the team to identify the mistakes and fix them early in the project.

Linear progression characteristic of the Waterfall model also discourages creativity at later stages. The software development team has to make all the decisions regarding the system design in the initial phases, and no additions can be made in later stages.

The waterfall model also has problems regarding testing and maintenance. As development and production environments differ, a bug or a problem occurring in the production environment may not be visible to the software development team and contribute to miscommunication. 

Moreover, as the operations team works mainly at the last stages of the process to deploy the product, it is not easy to transfer all the knowledge the development team has about the software to the operations team. This results in co-dependent separate groups that slow down the effective deployment process.

Automation Practices in DevOps as an Alternative

The primary purpose of DevOps is to optimize the process of development, testing, integration, and deployment with automation, iteration, and collaboration. DevOps cycle consists of several functions such as continuous development, continuous integration, and continuous deployment:

  • Continuous development includes architecting the project and implementing/coding it. Through this process, the team brainstorms and plans the project, divides the development process into smaller cycles, and decides on the programming languages, such as Ruby, JavaScript, Python, Ruby, and C/C++, that they will use in the project. Once the coding starts, source code management becomes essential to track the changes made to the code. This is where automation comes in handy to monitor alterations in the source code repository.
  • Continuous integration (CI)- The phase refers to modifications made to the shared repository and continuous tests run to identify bugs with automation tools. CI allows several developers to work on the same source code repository instead of writing the codes in different modules and integrating them at the end of the project to fix any errors, like in the Waterfall Model. 
  • Continuous Testing – Sometimes, before continuous integration, developers use automation testing tools that check the code for bugs in a live environment. The automation tool tests bugs and creates reports on the errors, which the developers then analyze. Automation saves much time for the developers who otherwise would manually test the code and minimize human error.
  • t tools. The finished application codes are deployed to the servers, and update dates are scheduled in advance. 
  • Continuous Monitoring – During this phase, developers monitor data on user activity and focus on solving recurring system problems. It is an essential step in solving the users’ bugs and making the product more user-friendly. Application performance management tools are used to assess the performance of the product.

Benefits of DevOps Automation

  • Faster deployment – Through increased collaboration and automation tools, it is easier to move the product from the development phase to the production phase. 
  • Product quality and improved defect detection- Continuous monitoring via application performance management tools finds the errors faster and does not occupy the development team with manual quality assurance. According to a vendor, developers, on average, spend 75% of their time searching for errors and bugs. Hence, having automated error detection significantly facilitates the process. 
  • Increased build and release speed- Development team can allocate more time to the development of updates or new releases that add more value to the business. According to another vendor, traditional OPS are 41% more time-consuming.
  • Faster “reinforcement” – Continuous deployment allows developers to find the error and change the code without re-creating the development environment. Developers can modify code directly in the production environment. 

For more on automation

To explore different IT and DevOps automation solutions, read our articles:

If you believe your business will benefit from a workload automation solution, check our data-driven list of WLA tools, DevOps tools, and DevOps platforms.

To gain a more comprehensive overview of workload automation, download our whitepaper on the topic:

Explore Workload Automation

And we can guide you through the process:

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Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

Sources:

AIMultiple.com Traffic Analytics, Ranking & Audience, Similarweb.
Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics, Business Insider.
Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
Data management barriers to AI success, Deloitte.
Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
Science, Research and Innovation Performance of the EU, European Commission.
Public-sector digitization: The trillion-dollar challenge, McKinsey & Company.
Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

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