3 key data integrity testing strategies for DW/ BI systems

Data warehousing and business intelligence users assume, and need, trustworthy data.
In the Gartner Group’s Online IT Glossary, data integrity and data integrity testing are defined as follows:

Data Integrity: the quality of the data residing in data repositories and database objects. The measurement which users consider when analyzing the value and reliability of the data.

Data Integrity Testing: verification that moved, copied, derived, and converted data is accurate and functions correctly within a single subsystem or application.

Data integrity processes should not only help you understand a project’s data integrity, but also help you gain and maintain the accuracy and consistency of data over its lifecycle. This includes data management best practices such as preventing data from being altered each time it is copied or moved. Processes should be established to maintain DW/ BI data integrity at all times. Data, in its final state, is the driving force behind industry decision making. Errors with data integrity commonly arise from human error, noncompliant operating procedures, errors in data transfers, software defects, compromised hardware, and physical compromise to devices.
This article provides a focus on DW/ BI “data integrity testing” — testing processes that support:

• All data warehouse sources and target schemas
• Extract, Transform, Load (ETL) processes
• Business intelligence components andfront-end applications

We will cover how key data integrity testing strategies are addressed in each of the above categories.
Other categories of DW/ BI and ETL testing, even though important, are not a focus in this article (e.g., functional, performance, security, scalability, system and integration testing, end-to-end, etc.).

Classifications of Data Integrity for DW/ BI Systems

To build upon Gartner’s definition that you read above, data Integrity is

an umbrella term that refers to the consistency, accuracy, and correctness of data stored in a database.

There are 3 primary types of data integrity: entity, domain, and referential.

1. Entity Integrity ensures that each row in a table (for example) is uniquely identified and without duplication. Entity integrity is often enforced by placing primary key and foreign key constraints on specific columns. Testing may be achieved by defining duplicate or the null values in test data.
2. Domain Integrity requires that each set of data values/columns falls within a specific permissible defined range. Examples of domain integrity are correct data type, format, and data length; values must fall within the range defined for the system; null status; and permitted size values. Testing may be accomplished, in part, using null, default and invalid values.
3. Referential Integrity is concerned with keeping the relationships between tables synchronized. Referential integrity is often enforced with primary key and foreign key relationships. It may be tested, for example, by deleting parent rows or the child rows in tables.

Verifying Data Integrity in Schemas, ETL Processes, and BI Reports

Before we dive into the 3 key data integrity strategies, let’s quickly outline a commonframework (Figure 1) that illustrates the major DW/ BI components that are generally verified in each phase of DH/ BI testing.

Learn how to build a Data Quality issues log (free template included)

It’s important to be on the same page with this as the following 3 key DW/ BI components are presented in this testing framework:

1. Verifications of Source and Target Data Requirements and Technical Schema Implementations

Requirements and schema-level tests confirm to what extent the design of each data component matches the targeted business requirements. This process should include the ability to verify:

• Business and technical requirements for all source and target data
• Data integrity specifications technically implemented (database management systems, file systems, text files, etc.)
• Data models for each implemented data schema
• Source to target data mappings vs. data loaded into DW targets. Examples of sources and associated targets include source data that are loaded to staging targets as well as staging data that are loaded to data warehouse or data mart targets.

Schema quality represents the ability of a schema to adequately and efficiently project ‘information/data’. Schema in this definition refers to the schema of the data warehouse regardless if it is a conceptual, logical or physical schema, star, constellation, or normalized schema. However, this definition is extended here to include the schemas of all data storages used in the whole data warehouse system including the data sourcing, staging, the operational data store, and the data marts. It is beneficial to assess the schema quality in the design phase of the data warehouse.

Detecting, analyzing and correcting schema deficiencies will boost the quality of the DW/ BI system. Schema quality could be viewed from various dimensions, namely:

• Schema correctness
• Schema completeness
• Schema conformity
• Schema integrity
• Interpretability
• Tractability
• Understandability
• Concise representation

2. ETL source and target data integrity tests

Most DW integrity testing and evaluation focus on this process. Various functional and non-functional testing methods are applied to test the ETL process logic for data. The goal is to

• Verify that valid and invalid conditions are correctly processed for all source and target data
• Ensure primary and foreign key integrity
• Verify test correctness of data transformations
• Ensure data cleansing
• Guarantee application of business rules, etc.

A properly-designed ETL system extracts data from source systems, enforces data quality and consistency standards, conforms data so that separate sources can be used together, and finally delivers data in a format that enables application developers to build applications and enables end users to make decisions.

3. BI reporting verifications

BI applications provide an interface that helps users interact with the back-end. The design of these reports is critical for understanding and planning the data integrity tests.
Insights such as what content uses which information maps, what ranges are leveraged in which indicators, and where interactions exist between indicators is required to build a full suite of test cases. If any measures are defined in the report itself, these should be verified as accurate. However, all other data elements that are pulled straight from the tables map should already have been validated from one of the above two sections.

A sample DW/ BI verification framework and sample verifications

DW/ BI data integrity verification is categorized here as follows. Figure 2 shows a verification classification framework for the techniques applicable to sources and targets in data warehouse, ETL process, and BI report applications.

The “what”, “when” and “where” of DW/ BI data integration testing is represented in the following table.

• Column headings represent when and where data related testing will take place
• Rows represent “what” data-related items should be considered for testing

A Sampling of Verifications in the Three Categories of Data Integrity Testing: Schemas, ETL Processes, and BI Reports:

Key Takeaways

• Data in its final state is the driving force behind organizational decision making.
• Raw data is often changed and processed to reach a usable format for BI reports. Data integrity practices ensure that this DW/ BI information is attributable and accurate.
• Data can easily become compromised if proper measures are not taken to verify it as it moves from each environment to become available to DW/ BI projects. Errors with data integrity commonly arise through human errors, noncompliant operating procedures, data transfers, software defects, and compromised hardware.
• By applying the 3 key data integrity testing strategies introduced in this article, you should be able to improve quality and reduce time and costs when developing and maintaining a DW/ BI project.