Data Integrity

Data Integrity(Q461671) is the maintenance of, and the assurance of the accuracy and consistency of, data over its entire life-cycle.

Data Integrity is how accurate and consistent the data is over its entire life-cycle. Data Integrity is a critical aspect to the design, implementation, and utilization of any system that store, process, or retrieve data. Data Integrity is often mistaken as Data Quality and/or Data Security. It needs Data Validation as the prerequisite, and the total opposite of Data Corruption. Data Integrity covers Data Retention processes and requires good Data Management.

In short, Data Integrity ensure the data is recorded exactly as intended; ensure that upon retrieval, the data is the same as when it was originally recorded. Any unintended changes of the data during the storing, retrieving, or processing operation, whether the change is done with malicious intent or not, or caused by unexpected hardware failure or even human error, is a failure on Data Integrity. If the change is done due to unauthorized accesses, it may also the failure of Data Security. These data loss, depending on how important the data, is very critical to any system.

Integrity Types

Physical Integrity

Physical Integrity deals with challenges on how to correctly storing and retrieving the data itself. The challenges may include electromechanical faults, design flaws, material fatigue, corrosion, power outages, natural disasters, and other special environmental hazards, such as ionizing radiation, extreme temperatures, pressures, etc.

The approach on the physical integrity includes redundant hardware, uninterruptible power supply, radiation hardened chips, error-correcting memory, clustered file system, cyptographic hash function, etc. Human-caused data integrity failure are often detected through Damm Algorithm or Luhn Algorithm. On the other hands, computer-caused failures are detected using hash functions.

Logical Integrity

Logical Integrity handles the correctness or rationality of the data, given a specific context. This may includes referencial integrity and entity integrity in a rational database, software bugs, design flaws, and human errors. Logical Integrity must make sure that the data "makes sense" given its environment.

The approach of Logical Integrity includes constraint checkings, foreign key constraints, program assertions, and other run-time sanity checks.

Database-contained Integrity

Data Integrity on database system includes:

• Entity Integrity that concern the concept of Primary Key. It is an integrity rule of "Every table must have a single unique and not null Primary Key."
• Referential Integrity that concern the concept of Foreign Key. It is an integrity rule of "Any Forign Key value can only be in one of two states, a reference to Primary Key in another table in the same database, or null (indicating there are no relationship with those tables at that moment)".
• Domain Integrity is an integrity rule of "All columns in a relational database must be declared upon a defined domain. The primary unit of data in the relational data model is the non-decomposable data item. Domains are pools of actual values appearing in the columns of a table.
• User-defined integrity is a set of integrity rules specified by a user, which do not belong in the entity, referential, or domain integrity.

The goal of these integrity is to increase system stability, performance, re-usability, and maintainability.

Implementation

MHRA (Medicines and Healthcare products Regulatory Agency), FDA (U.S Food and Drug Administration), TGA (the Australian Therapeutic Goods Administration), and PIC/S (Pharmaceutical Inspection Co-operation Scheme) issue data integrity guidance documents and are recommended for pharmaceutical manufacturers related. Data Integrity Requirements are contained in the PIC/S Guide to Good Manufacturing Practice and the Australian Code of GMP, so TGA expects manufacturers to satisfy with these requirements.

Data Integrity in PKC

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