Is there a Data Culture Gap?: Expert Interviews on Relationships between Data Quality and Manual Data Collection in culturally-heterogeneous Organisational Decision-Making
2022 (English)Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE credits
Student thesis
Sustainable development
SDG 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all
Abstract [en]
Organisations have recognized the importance of data quality for several decades since poor data quality often results in bad decisions and subsequently economic losses. Data quality issues cost billions of dollars annually and good data quality is as one of the top barriers to strategic business plans. Due to low entry barriers and universality, manual data collection is still a huge part of all data worldwide. Globalised yet centralised organisational decision-making often result in decision-making with locally dispersed decision-maker and data collector.
This positivistic qualitative study investigates the relationship between data quality and manual collection in data-based organisational decision-making. This thesis aims to help data collection managers in culturally-heterogeneous settings with manual data collection to achieve better data quality. The thesis includes a literature review to identify the definition of data quality, suggested measures to improve data quality in manual data collection and relevance of culture. As the theoretical framework for further analysis Haegemans, Snoeck, and Lemahie's (2019) research is used.
Expert interviews are the data collection method and thematic analysis is the method of analysis. As research result, seven relationships between data quality and manual data collection are identified: the level of skills involved during the data collection process, the data collection design, the motivation of the data collector and issuing organisation, cultural norm differences, business value, language and local domain knowledge. Depending on the nature of the relationship, the impact on data quality can be positive or negative. Of these relationships five align with the six attributes detailed in Haegemans, Snoeck, and Lemahie (2019). Additional relationships relevant in culturally-heterogeneous decision-making are “Language” and “Local Domain Knowledge”. For each of the relationships culturally-heterogeneity needs to be taken into account for an optimum data quality. Recommendations to adapt for culturally-heterogeneous decision-making are modifying the framework with a feedback loop between domain expert/local intermediaries and data collector. Another recommendation is to consider the data collection as a group task not an individual task. The languages and social norms are added to form a new framework.
Place, publisher, year, edition, pages
2022. , p. 66
Keywords [en]
Data Quality, Manual data collection, cultural heterogeneity, Information Systems, decision-making, data quality assurance, data quality management, culture, societal norms, language
National Category
Information Systems
Identifiers
URN: urn:nbn:se:lnu:diva-117480OAI: oai:DiVA.org:lnu-117480DiVA, id: diva2:1710270
Subject / course
Informatics
Educational program
Master Programme in Information Systems, 60 credits
Examiners
2022-11-112022-11-112024-08-28Bibliographically approved