What is data quality?

Data quality measures how well a dataset is able to accurately and reliably serve its intended purpose. High-quality data is essential for informed decision-making, conducting precise analysis, and developing effective AI models.

The key attributes of data quality are accuracy, completeness, consistency, timeliness, relevance, reliability, accessibility, and integrity.

Here are 6 checks to keep in mind to ensure high-level data quality for analytics:

1. Identify duplicate data

   Duplicated data can bias judgement toward duplicated entries. In order to ensure data uniqueness, it is essential to identify and remove duplicated data. Duplicated data can be caused by human entry errors, integration mistakes, database migration, or replication errors. Duplicated data is most often found in customer profiles and can cause inaccuracy in tracking customer metrics.

2. Check data completeness

   Many datasets lack mandatory fields, have null values, and are missing values. Ensuring completeness in your dataset is crucial to gain insight for future projects. Incomplete datasets can lead your data products, machine learning models, and data decision-making in the wrong direction. Lack of data completeness can be caused by bugs in data integration pipelines, errors from data providers, human entry errors, and from data collection.

3. Apply formatting checks

   It is crucial that your data is uniform across different datasets. Checking for uniform data formats, values and structures can help ensure consistency. Conflicting information about a single unit can lead to distrust and compliance issues. Inconsistency in your data can be caused by unsynchronized data integration pipelines and partially migrated databases.

4. Ensure business rules are in place

   Business rules ensure validity in datasets. For example, companies will ensure customers input the proper data by disallowing disordered personal information (such as a birthdate) or preventing a customer from continuing if a field is missing. Implementing rules to ensure data validity will improve your firms data quality.

5. Check the relevancy of your data

   Having relevant data is essential for any analytics project. Most companies have too much data, with much of it being irrelevant to their analytics needs. Including a filtering process to limit what data is being used for initiatives can improve data quality. 

6. Validate data integrity

   Data integrity ensures enterprise data can be traced and connected among diverse platforms. The integrity of data can be impacted by human error, inconsistencies of format, and through collection error. Achieving data integrity and maintaining it will save firm resources. Misinformed decisions can lead to costly problems down the road.