DataOps for Product Information Management: A study of adoption readiness
Nguyen Thi Thanh, Phuong (2022)
Nguyen Thi Thanh, Phuong
2022
All rights reserved. This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-202205057280
https://urn.fi/URN:NBN:fi:amk-202205057280
Tiivistelmä
Data Operations (DataOps) is currently being introduced in software-intensive companies, but there are not many companies that have fully adopted DataOps. DataOps is a process-oriented methodology that is people-driven rather than technology-driven. DataOps provide a best practice for data orchestration, automation, and collaboration, that aims to improve productivity and continuous assurance.
The thesis will study the adoption readiness for DataOps in product information management for a cleantech company. The thesis explores and details common problems in data management, such as misinformation, misuse, copying-pasting errors, duplicate, miscommunication, and manual work fatigue. Product information management solutions are often plagued by inconsistencies, user-friendliness issues, multiple document variants, and unclear versioning.
The aim of the thesis is to assist the company to define an effective product information management system. Further, the thesis will detail current challenges and elaborate on how DataOps can be adopted for product data management. The study was conducted through inductive qualitative interviews with eight experts in different teams in the company. The results were obtained by identifying the common points of view among interviewees. The research results are validated by discussing with the experts and by using Natural Language Processing (NLP) modeling for determining commonalities in interview data that provide some objective reasoning.
The insight gained is that product information management should be built using modular standard products and to be built using a numbering scheme that assists in finding the family product number that refers to parent numbers, which may have the same child items or assembly numbers. The dataflow design needs to be shared and implemented across both the vertical and horizontal organization. The scope of work and sales order data can be created manually, the other shared common data should be generated automatically from a central repository or integrated platforms of different teams. The study opens a new opportunity to increase the awareness of data management, tools, and platforms, which can be delivered to end-users through video training, in-class training, feedback forums, and Q&A channels.
The thesis will study the adoption readiness for DataOps in product information management for a cleantech company. The thesis explores and details common problems in data management, such as misinformation, misuse, copying-pasting errors, duplicate, miscommunication, and manual work fatigue. Product information management solutions are often plagued by inconsistencies, user-friendliness issues, multiple document variants, and unclear versioning.
The aim of the thesis is to assist the company to define an effective product information management system. Further, the thesis will detail current challenges and elaborate on how DataOps can be adopted for product data management. The study was conducted through inductive qualitative interviews with eight experts in different teams in the company. The results were obtained by identifying the common points of view among interviewees. The research results are validated by discussing with the experts and by using Natural Language Processing (NLP) modeling for determining commonalities in interview data that provide some objective reasoning.
The insight gained is that product information management should be built using modular standard products and to be built using a numbering scheme that assists in finding the family product number that refers to parent numbers, which may have the same child items or assembly numbers. The dataflow design needs to be shared and implemented across both the vertical and horizontal organization. The scope of work and sales order data can be created manually, the other shared common data should be generated automatically from a central repository or integrated platforms of different teams. The study opens a new opportunity to increase the awareness of data management, tools, and platforms, which can be delivered to end-users through video training, in-class training, feedback forums, and Q&A channels.