Data engineers, analysts, data scientists, and others can find many users seeking data quality advice on subreddits despite what may seem like a common approach to data quality management.
Data quality dimensions can assist data teams in improving and maintaining data reliability. A series of examples will illustrate how the six commonly accepted dimensions of data quality are measured and how they can be used to improve management of data quality.
Information About Quality Dimensions
A data quality dimension is a framework for effectively managing data quality. It helps you understand and measure your organization’s current data quality and set accurate KPIs for data quality.
Quality is determined by the accuracy, completeness, integrity, validity, timeliness, and uniqueness of the data set.
The data team should ensure that these dimensions are met to support downstream business intelligence scenarios and build confidence in the data.
Understanding how reliable data is created can be achieved by breaking down each element of the seven data quality dimensions with examples.
Checkout Data Quality Dimensions with Examples
01. Data Accuracy
It’s the degree to which data accurately describes what it’s intended to illustrate, such as geographic locations on a map or erroneous information in a spreadsheet.
To measure data accuracy, data teams can:
- Measure of precision: Reliability of retrieved data compared to relevant data
- Estimated recall: Indicates the degree of sensitivity by dividing the relevant data by the total
- F-1 score: Computes the frequency of accurate predictions made by a model across all datasets by combining precision and recall
Data teams can also determine data accuracy by:
- Analyses of statistics: A detailed analysis of the trends and patterns exhibited in the data.
- Methods of sampling: Using a sample to make inferences about an overarching dataset.
- Automated data validation processes: Utilizing technology to ensure data accuracy and applicability.
02. Data Completeness
Completeness refers to whether your data covers the full scope of your question and whether gaps, missing values, or biases that influence your outcome have been introduced.
In some cases, missing transactions can result in underreported revenue. Marketing teams can have difficulty personalizing and targeting campaigns if they have gaps in customer data, and any statistical analysis based on missing values could be biased.
Your most essential tables or fields can be assessed quantitatively in a few ways, such as:
- Approach at the attribute level: Measure how many individual attributes or fields a data set lacks
- The record-level approach: Checking a data set’s completeness on a per-record basis
- Sampling data sets: To estimate completeness, sample your data sets systematically
- Profiling data: Display metadata about your data using a tool or programming language
03. Data Timeliness
Data timeliness refers to how up-to-date and available information is at the desired time for its intended purpose. A business can make better decisions based on the most up-to-date information.
Data timeliness influences data quality by determining a company’s information’s reliability and usefulness.
Some key metrics can be used to measure the timeliness of data, including:
- Freshness of the data: The age of the data and the frequency of its refresh
- Latency of data: The time it takes from the moment data is produced to the moment data is available
- Data accessibility: Accessibility to data as well as the ease at which it can be retrieved and used
- Insight time: Measure how long it takes from data generation to actionable insights
04. Data Uniqueness
Unique data ensures that duplicate data is not copied and shared into another record.
Having duplicate data can cause all kinds of problems, from spamming leads and destroying personalization programs to costing databases and damaging reputations (for example, duplicate social security numbers or other user IDs).
Data teams can measure their data uniqueness using uniqueness tests. This method enables data warehouse teams to detect duplicate records and clean and normalize raw data programmatically.
05. Data Validity
Data validity is determined by how well it meets specific criteria, often resulting from prior data analysis.
Profiling the data and seeing where the data breaks can lead to the development of data validity rules and data validation tests. Some of these rules might be:
- Valid column values
- Columns have to look a certain way
- The integrity of the primary key
- The presence of nulls in a column
- A valid combination of values
- Regulations based on computations
- Requirements for chronology
- Rules that can be conditional
06. Data Integrity
Data integrity refers to accuracy and consistency throughout a data’s lifecycle. A piece of data is considered integrity if it is not altered during storage, retrieval, or processing without authorized intervention. The goal is to ensure no content has been changed in transit from point A to point B.
Physical security measures, user access controls, and system checks are all involved in maintaining data integrity:
- To prevent unauthorized access to data, secure environments must be used to store the data.
- Access controls restrict who can modify the data, and error-checking processes fix any changes made accidentally.
- A version control system and audit trails make it easy to maintain data integrity over time.
Monte Carlo Analysis for Data Quality Monitoring
Data teams need key measurements around these dimensions to successfully operationalize data quality management. Manually monitoring these data quality dimensions requires a lot of resources and time.
Data observability tools like Monte Carlo’s can improve each data quality dimension much more efficiently. Observability solutions can alert stakeholders when anomalies or data issues occur, enabling them to determine the exact cause of the problem – wherever it is – and resolve it quickly.