To truly understand the quality of your database, it’s a good idea to run regular audits.
The frequency of your audits really depends on the nature of your business and the volume of data you’re acquiring. But generally speaking, the longer you leave your data between audits, the higher the associated costs of cleaning it will be due to the extent of data degradation.
You can be smarter with your data audits by narrowing the focus across either different products or customer types. This establishes a basis for comparison, helping you internally benchmark performance across business areas and identify any issues and the potential root cause.
Schedule your data audits to run at fixed intervals and top-up your data quality when it falls below a pre-determined level. Taking this approach will regulate your data management investments and audit frequency, enabling a high level of data quality to be self-maintained.
Use data quality dashboards to visualise the data that is important to you, measure it, identify key patterns, and set objectives to improve.
Red, amber, green (RAG) status is a traffic light system and a method that can be used to clearly identify areas that need immediate attention, helping you prioritise the implementation of actionable insights from your data audits.
Agree a process in your team to make these improvements, implement them, then start the process over and measure again. Remember, this should be a cyclical process to enable continuous and sustained improvement.
Our data health check tool generates a data quality benchmark score from an algorithm that securely tests your data against the following measurement criteria:
We compare your customer and prospect records against our own data sources to reveal how accurate, complete and compliant your data is.
We then provide you with a visualised data quality audit, and a unique Data Quality Index Score, giving you insight into how you measure up against other companies who hold similar data.
To help assess the quality of your data, the Data Management Association UK outlines six dimensions that your data should meet: accuracy, completeness, consistency, timeliness, validity and uniqueness.
Customer data should be up-to-date, correct and reflect reality.
Does your data fulfil your expectations of what’s comprehensive?
Data is unique if it appears only once in a dataset.
If data is replicated in multiple places, it needs to be consistent across all datasets.
Timeliness indicates whether the data is available when expected and needed.
Data held should be formatted correctly and truly real, e.g. an email address containing @ is a minimum validation requirement for that data field.