Data Management Series |
- Processes span different functions and/or roles, each of them maintaining the data they are interested in, without any agreement or coordination on the ownership. The lack of ownership is in general management’s fault.
- Within an enterprise many systems arrive to be integrated, the quality of the data depending on the quality and scope of the integrations, whether they were addressed fully or only superficially. Few integrations are stable and properly designed. If stability can be obtained in time, scope is seldom changed as it involves further investments, and thus the remaining data need to be maintained manually, respectively the issues need to be troubleshooted or let accumulate in the backlog.
- There are systems which are not integrated but use the same data, users needing to duplicate their effort, so they often focus on their immediate needs. Moreover, the lack of mappings between systems makes data analysis and review difficult.
- The lack of knowledge about the systems used in terms of processes, procedures, best practices, policies, etc. Users usually try to do their best based on the knowledge they have, and despite their best intent, the systems arrive to be misused just to get things done.
- Basic or inexistent validation for data entry in each important entry point (UI, integration interfaces, bulk upload functionality), system permissiveness (allowing workarounds), stability and reliability (bugs/defects).
- Inexistence of data quality control mechanisms or quality methodologies, respectively a Data and/or Quality Management strategy. If the data quality is not kept under review, it can easily decrease over time.
- The lack of a data culture and processes that support data quality.
- People lack consistency and/or the self-discipline to follow the processes and update the data as the processes requires it and not only the data to move to the next or final step. Therefore, the gap between reality and the one presented by the system is considerable.
- People are not motivated to improve data quality even if they may recognize the importance of doing that.
Data quality comes on the managers' agenda, especially during ERP implementations. Unfortunately, as soon as that happens, it also disappears, despite being warned of the consequences poor data quality might have on the implementation and further data use. An ERP implementation is supposed to be an opportunity for improving the data quality, though for many organizations it remains in this state. Once this opportunity passes, organizations need more financial and human resources to reach a fraction from the opportunity missed.