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Agile manifesto was published in 2001 by 17 software developers describing the 12 principles of Agile Software Development. The principles include welcoming improving requirements, even late in the development, and deliver working software continually. These are the basic principle that leads the way in Agile project management.
The process of making a project by breaking it into different stages and ensure constant collaboration with stakeholders in order to accommodate required improvements is known as agile. It is an iterative process. Several iterations lead to process completion. The benefits of the iterations are that adjustments in the project could be formulated throughout the project completion. The agile method breaks the project into small components. Each component is completed iteratively in work sessions by passing from different phases of designing, testing, and quality assurance. These work sessions are called Sprints which are usually of short duration ranging from days up to 4 weeks. The team releases the segments to the customers as soon as they are completed to get their feedback and make the necessary changes if customers demand them. This feedback helps in fixing the flaws quickly by continuous improvement and reduce the chances of large scale failures.
There is a lot of uncertainty involved in data science projects. The plan often goes out of the track due to unforeseen circumstances. Many unexpected problems arise and the project takes longer than what is expected. Traditional as well as agile both methods are used for managing data science projects. Traditional includes CRISP-DM and Waterfall whereas agile methods include Scrum and Kanban. Apart from these, there are some other technologies as well.
CRISP-DM Cross-industry standard process for data mining According to CRISP-DM, Project Management in Data Science involved six iterative phases. First is understanding the problem by asking lots of questions than understanding and identifying the data from multiple sources. Then the data is modified as per requirements which is the most time-consuming step as it involves data cleaning and data transformation. Then comes the core activity that is Data modeling to build and assess a model. The model is then evaluated which includes communication. The final step is the deployment and maintenance including overall project reports and review. This approach is easy to implement due to flexibility and cyclic nature but it does not cover communication issues hence does not work for teams.
The waterfall approach is sequential hence does not work well for the accommodation of changes. This method is not good in Data Discovery processes. Agile methods are better than traditional approaches because of the continuous feedback approach to deal with uncertainties involved in data science projects. Scrum is one of the most used agile methods worldwide in various industries. According to this method, Projects are divided into smaller components which are called sprints, and they last from 2 weeks to 3 months. It includes feedback from customers and hence easy to implement changes. It allows optimizing predictability but due to time bond nature, it does not support longer projects.
The agile method ensures the customer's involvement. Whenever requirements change or project go out of the track, due to continuous feedback from the customer it is easy to go back on track and accommodate those changes. Testing is involved in each iteration so the product could b checked in each phase whether it is in working condition or not.
Data scientists can create blueprints and prioritize tasks based on requirements and goals. It allows the technical team to estimate the overall cost associated with each goal. This helps data scientists and stakeholders to get aligned by constant communication.
Agile helps in aligning data scientists and engineers by bridging the gap between both teams so that data scientists don't have to wait for model deployment and engineers should remain aware of the applied research and data analysis
It is more time consuming and requires more time and energy from everyone involved in the team. Research and agile methods might not always go along. Research in data science requires creative problems solving techniques with guidelines vs strict rules. There is not any single way to perform research as answering one question may lead to several others which might lead research and analysis to last forever without any outcome and the project can become everlasting. Lack of documentation makes it difficult to work for new members who join the team. Due to a lack of details about certain features it a difficult for them to continue work.
For a project to be successful, it is necessary to train clients properly so that they can aid in Product development. Any absence of customer cooperation will affect software quality and success and development companies will also be affected. Getting certifications in agile project management are in-demand in the IT market. Numerous platforms are offering accessible programs for tech personalities. The PMP certification is the online resource that is helping individuals in the tech industry. Also, Agile scrum certification is an excellent resource for learning agile project management conveniently.
Data science work presents value within the insights and patterns they can put out. So for that, it is essential to allow the teams to operate on research collaboratively and iteratively with their stakeholders. Consuming too much time working to get every stakeholder to agree on what the final product is can cause these models to never occur. Consequently, we have an Agile methodology.
Agile is a method by which a team can execute a project by breaking it up into several steps and involving continuous collaboration with stakeholders and constant development and iteration at every stage. The Agile methodology begins with clients describing how the end product will be used and what problem it will solve. The Agile methodology has evolved to offer greatly relevant applications for various domains. It is not just about streamlining the lifecycle of data science development. It is about adjusting the data science team with their various clients by giving precise feedback to settle to business objects.