What is data quality?
Data quality means that the data an organization works with is accurate, up-to-date, complete, understandable, and available when needed. In practice, this means, for example, that a citizen has the correct name and address in the register, and that these data are the same across all information systems in the state.
Why is data quality management important in public administration?
Data is the basis for the proper functioning of public institutions. If it is of poor quality, it ultimately brings increased costs to the organization, which can be:
- costs of finding and correcting errors – costs of adjusting systems and processes, testing adjustments or even labor costs of identifying, eliminating errors, or manually cleaning data in systems,
- costs of finding objective information – poor data quality can cause distortion of information and ultimately inefficiency of processes,
- costs of wrong decisions – faulty planning, wrong strategic decisions based on poor quality data,
- costs of losing trust – loss of trust of partners and clients (citizens), costs of implementing and operating alternative procedures and solutions,
- costs of sacrificed opportunities (opportunity costs) – loss of opportunities for alternative use of resources that must be allocated to eliminating errors.The consequence of poor data quality in state information systems may be, for example, that an incorrect address will cause the office’s decision to not arrive on time; duplicate records will reduce the efficiency of officials’ work, or that outdated data will lead to erroneous decisions by agencies or an incorrect assessment of a citizen’s entitlement to state service.
Causes of low data quality in public administration
The following factors most often contribute to the poor quality of data in state systems:
- manual and repeated data entry into various information systems,
- low level of integration between state systems and registers,
- insufficient control over data entry into the system,
- lack of standards and recording methodology or their inconsistent application.
How to Manage Data Quality – Lifecycle
Data quality management is carried out in five basic steps:
Baseline and definition of data quality rules – what is considered “correct” data (e.g. no empty fields, valid email format), setting KPIs and thresholds.
- Data status analysis – data profiling, identification of errors, inaccuracies or duplications, checking the referential integrity of data.
- Identification of the causes of poor data quality (route cause analysis) – revealing the causes of poor data quality in order to implement corrective measures,
- Data cleaning and error prevention – assisted or automated data cleaning, introduction of controls that prevent the recurrence of data problems.
- Monitoring and improvement – regular monitoring and evaluation of data quality, raising awareness of the importance of data quality among the organization’s employees.
How to Measure Data Quality
Data quality can be measured by various indicators, such as:
- accuracy – how much data is factually correct,
- completeness – how many records have all required fields filled in,
- uniqueness – the occurrence of duplicate records in the system,
- timeliness – how quickly data is updated after a change,
- consistency – the consistency of data across different systems.
Conclusion
Quality data is the invisible foundation of efficient and trustworthy public administration. Investment in its management returns in the form of faster processes, lower costs and better service to citizens. Data quality management is therefore not just an IT topic – it is a key part of modern institutional management.
