Building an Agile Data Science Process for Applications in Development Stage
Rajala, Jenna (2022)
Rajala, Jenna
2022
Tietojohtamisen DI-ohjelma - Master's Programme in Information and Knowledge Management
Johtamisen ja talouden tiedekunta - Faculty of Management and Business
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Hyväksymispäivämäärä
2022-04-14
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202204073085
https://urn.fi/URN:NBN:fi:tuni-202204073085
Tiivistelmä
In today’s business, data can be seen as a raw material for a way to create value and identify situations in which it may be valuable. Different technologies are collecting increasingly massive amounts of data, yet many organizations are trying to find better ways to obtain value from the data they possess and compete in the marketplace. Processing huge amounts of data to support decision processes is becoming increasing attention in corporate IT strategies. However, the increase in the use of data and information has outshined the knowledge of how to guide development teams that take on these projects.
The key to successfully realizing data science use cases is clear communication and enterprise-wide digitalization strategy. The most important pain points of data science projects are lack of collaboration between different teams, a lack of people with needed skills and a lack of consistent methods and processes to approach the topic. Current data science processes have some limitations, and there is a need for a new process. Thus, the objective of this research is to build a new process model for data science to enable data science projects to start working with software development teams from the early stages of development. This way data science can support the development process of an application and bring up the future requirements and aspirations for the data already in the development phase.
This research uses design science research strategy, which contains demonstration of the built process. In this research, demonstration is done with a case study for a client organization. The organization is launching a new mobile application, where they offer different services for their customers in their line of business. The application is currently in development stage and will be available for users in 2022. To demonstrate and test this new process in action, customer segmentation model will be created as a part of the mobile application development process.
The research showed that building an initial data science model helps detecting possible shortcomings of the data. It was discovered that developing the MVP data science model first helps identifying possible problems and matters that need further defining at an early stage. Thus, the research showed that the new agile data science process can support the development process of an application. It was also discovered that sparking discussion about the future needs and aspirations for data science in the early stages benefits the development process and the whole organization in general.
The key to successfully realizing data science use cases is clear communication and enterprise-wide digitalization strategy. The most important pain points of data science projects are lack of collaboration between different teams, a lack of people with needed skills and a lack of consistent methods and processes to approach the topic. Current data science processes have some limitations, and there is a need for a new process. Thus, the objective of this research is to build a new process model for data science to enable data science projects to start working with software development teams from the early stages of development. This way data science can support the development process of an application and bring up the future requirements and aspirations for the data already in the development phase.
This research uses design science research strategy, which contains demonstration of the built process. In this research, demonstration is done with a case study for a client organization. The organization is launching a new mobile application, where they offer different services for their customers in their line of business. The application is currently in development stage and will be available for users in 2022. To demonstrate and test this new process in action, customer segmentation model will be created as a part of the mobile application development process.
The research showed that building an initial data science model helps detecting possible shortcomings of the data. It was discovered that developing the MVP data science model first helps identifying possible problems and matters that need further defining at an early stage. Thus, the research showed that the new agile data science process can support the development process of an application. It was also discovered that sparking discussion about the future needs and aspirations for data science in the early stages benefits the development process and the whole organization in general.