Common issues in data quality and their fixes
Many large organisations invest millions of pounds every year in complex data strategies and data capture/analysis projects. One of the main issues that organisations of all sizes have to deal with is ensuring that their data is of sufficient ‘quality’.
What is meant by data quality?
The term data quality is quite subjective, as it is about how well the data fits the needs of the user/organisation. However, as a general rule, a dataset is regarded as being “good” quality if it is: accurate, up-to-date, consistent, unique, relevant, and comprehensive
The most-common issues with data quality
Duplicated data
Many organisations have data stored in various places which is often actually the same. Duplicated data can often cause issues with customer experiences, and analysis/predictions.
There are specialist tools available to help with duplicate data, but the best way to reduce the occurrences of this are to have regular audits and deduplication projects, ensure strong data entry standards, and adopt a ‘rule-based’ data quality management approach.
Inaccurate, incorrect, and missing data
This can lead to incorrect assumptions, strategies, and decisions. Accuracy is crucial for all organisations – but particularly those that are highly regulated and deal with sensitive data. There are various causes of inaccurate or missing data – with human error and poor data structures both being significant.
Automation may deal with some of these issues, but there are also a range of other specialist data quality solutions that can help.
Data experts
If you feel that your organisation could benefit from the services of a data analysis company, there are a number of these specialists in the UK. Firms such as shepper.com can offer advice and services in this regard.
Inconsistencies in data format
Data inconsistencies are a major issue. Even something as simple as a date can be recorded in multiple different formats. This might cause something to be omitted in data analysis or lead to incorrect results.
The key here is to establish an internal standard that sets out exactly how certain pieces of information are captured and in which format.
