Why is Data Quality Important?


Reliable information is required to understand demand and plan for service provision – for example, police officers on the ground, staffing of emergency services, patient demand in hospitals, impact of population growth on schools and other educational facilities. Accurate activity data will ensure that services are funded appropriately. “Activity based funding” is being implemented at many public sector agencies. Mis-reporting of activities could have a severe impact on funding. 

Accurate and up to date information helps organisations to ensure that welfare payments and other entitlements are available to those who are eligible. Quality data contributes to employee safety. Good data informs public employees such as police and enforcement officers of the risks in approaching places and people and any need for back up. 

Data Quality Insights

Prevention is better than cure
Invest in improving source data quality – the returns are much more attractive. Errors at the source tend to multiply further down the chain, and become truly problematic at the reporting end. 

Quality is “in the eye of the beholder”
There are two major ‘operational’ and ‘reporting’ perspectives in data usage. For example, statistical analysts might decide a data set is of unacceptable quality, but operational people, who are most familiar with the data and understand the different ways in which they use it at the individual record level, may consider its quality acceptable. 

Process contributes to quality
Training and well-defined procedures are important contributors to data quality. A system that supports the workflow (as contrasted to one that captures data after the fact), is more likely to contain high quality data. 

Technology design can affect quality
The other factor is the design of the system; whether the user interface is intuitive, the classifications are at the correct level, the use of mandatory fields, logical navigation between screens, etc. 

Prioritisation is key
There can never be sufficient resources to check and correct all data, hence the need to prioritise which datasets to focus on. Focus on improving data items that are vital to meeting the organisation’s strategic objectives. 

Rely on data analytics where possible
The most resource intensive method of auditing is record inspection, which should be minimised. Using data analytics is preferable as it is possible to compare data from different settings, systems and timeframes, is repeatable and cost effective. 

We provide Data Analytics and Data Quality services for Organisations Australia wide.