ERP system's data quality assessment
Pynnönen, Tommi (2019-06-28)
ERP system's data quality assessment
(28.06.2019)
Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty.
suljettu
Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe2019070122509
https://urn.fi/URN:NBN:fi-fe2019070122509
Tiivistelmä
Companies store more and more data in their systems but the management of data quality is still often neglected. Data quality can be defined as the “data’s fitness for use”. In other words, quality of data depends on where the data is used and who is using it. (Otto et al. 2012.) Data is used in all the activities in the organization and if it is of poor quality, it can have negative consequences in organization’s performance. Therefore, poor quality data can become costly for many companies. (Haug et al. 2011.) One important information system is Enterprise Resource Planning (ERP) system, which integrates all the different functional areas inside the organization (Rao, 2000).
The current literature offers multiple techniques for assessment and improvement of data quality and the recent focus has been on defining methodologies that help selecting, customizing and applying data quality anagement methods. (Batini et al. 2009.) The importance of data quality is well understood but difficulties stem from the organization. Organizations may not have the money, time, resources or knowledge to start improving their data quality.
The purpose of this research was to examine how the data quality of an ERP system can be assessed. A significant part of this assessment was to introduce and evaluate a generic data quality rule-package, which purpose was to assess data quality of an ERP system called IFS Applications. These data quality rules are implemented as SQL statements that query the ERP’s database and produce results about the data errors. This research was conducted using design science research (DSR) methodology. Also, a literature review was done concerning ERP data quality and its management.
Findings of this study suggest that a predefined generic rule-package is useful for ERP data quality assessment and improvement. Also, customer-specific data quality rules can be defined and used.
A framework for ERP data quality assessment is provided. First, the ERP environment needs to be thoroughly analyzed. Often used objects and critical processes are observed. After this, data quality rules are implemented to assess the most important data. This data is either master data, reference data or transactional data. The most important data quality dimensions are also introduced. Finally, data quality is monitored and improved by correcting the errors found by the rules. The framework also takes to account the challenges related to to this process. The organization needs to have the organizational capacity such as time, resources and the knowledge to complete the whole data quality improvement project.
The current literature offers multiple techniques for assessment and improvement of data quality and the recent focus has been on defining methodologies that help selecting, customizing and applying data quality anagement methods. (Batini et al. 2009.) The importance of data quality is well understood but difficulties stem from the organization. Organizations may not have the money, time, resources or knowledge to start improving their data quality.
The purpose of this research was to examine how the data quality of an ERP system can be assessed. A significant part of this assessment was to introduce and evaluate a generic data quality rule-package, which purpose was to assess data quality of an ERP system called IFS Applications. These data quality rules are implemented as SQL statements that query the ERP’s database and produce results about the data errors. This research was conducted using design science research (DSR) methodology. Also, a literature review was done concerning ERP data quality and its management.
Findings of this study suggest that a predefined generic rule-package is useful for ERP data quality assessment and improvement. Also, customer-specific data quality rules can be defined and used.
A framework for ERP data quality assessment is provided. First, the ERP environment needs to be thoroughly analyzed. Often used objects and critical processes are observed. After this, data quality rules are implemented to assess the most important data. This data is either master data, reference data or transactional data. The most important data quality dimensions are also introduced. Finally, data quality is monitored and improved by correcting the errors found by the rules. The framework also takes to account the challenges related to to this process. The organization needs to have the organizational capacity such as time, resources and the knowledge to complete the whole data quality improvement project.