Data Completeness assesses whether necessary fields have values present. It is an important metric for measuring data quality and usability.

What can you do about it?

There are a number of steps that can be taken to identify and address data completeness issues:

1) Identify business-critical fields and processes

First, identify which data fields are essential to your business processes. Consult with a range of stakeholders, including data users and business process owners, to understand the impact of missing data on their operations. It’s worth noting that the introduction of new or modified business processes may require different data fields, so this step should be repeated as required.

2) Data Profiling

Assess the current state of your data by going over the data to look for missing values. This should give you an understanding of the scale of the problem and allow you to start identifying any trends or abnormalities that might be affecting the quality of the data.

3) Define acceptable thresholds for completeness

The next task is to consider is the minimum levels of completeness required for data to be considered valid and useful for analysis and decision-making.

Base these targets on the business needs confirmed by your stakeholders.  Remember that different processes will have varying tolerance levels for incomplete data. E.g. regulatory reporting vs marketing activity.

4) Data Cleansing

Once you've identified missing values through data profiling, the next step is to address them through data cleansing, using techniques such as:

• Imputation – this is the substitution of an estimated value that is as realistic as possible for a missing data item. The substituted value is intended to enable subsequent data analysis to proceed.
• Interpolation – this is the process of using known data values to estimate unknown data values. One of the simplest methods, linear interpolation, requires knowledge of two points and the constant rate of change between them.
• Elimination – exclude the record from processing or analysis

5) Data Integration

This is the process of creating a single, unified dataset by merging data from several sources. It is a valuable technique for improving data completeness, as it can help fill in gaps and provide a more comprehensive view of the data.

6) Data Governance and Quality Management

This involves establishing policies, data audit procedures, measurement metrics, and tools to oversee data quality throughout its life cycle.