Using custom data quality maturity tool for improving data quality in an organization

Abstract

Data is money, and bad data is loss of money. Nowadays, huge amount of data is created, and the amount of data is expected to increase in the future. This data is used for traditional reporting and decision making, but also as a basis for automation, artificial intelligence, and machine learning. It can also be used for automated business operations, and even automated decision making. For these purposes, good quality of data must be ensured because the benefits of data-driven organization can only be achieved with good quality data.

This research was qualitative yet empirical with measurement done via observations in a case company while experimenting and piloting customized data measurement, using a data quality measurement tool. In addition, there was a literature review which covered various aspects of data quality and data quality maturity models. Both data quality dimensions and data quality maturity models were covered in the literature review. Additionally, concrete actions for the possibilities to improve data quality and data quality maturity were included in this thesis.

Results of this study showed that the customized data quality maturity assessment can help case company to improve data quality maturity in the long run. This study was too short for making the data quality improvements visible via the custom data quality maturity tool and actions based on the results of using said tool, but concrete steps to improve data quality and DQ management were indicated by the data quality maturity measurement tool to be achieved via concrete actions in the future. However, data quality awareness and understanding of data quality were improved in the case company during this research. There is also a plan to experiment using this custom data quality maturity tool for KPI status, target settings and continuous measurements.