Jorge Anton/ Alvaro Palomera

Technical Definitions

Integrity refers to safeguarding the accuracy and completeness of stored data. Data lacks integrity when it has been changed accidentally or tampered with. Examples of data losing integrity are where information is duplicated in a relational database and only one copy is updated or where data entries have been maliciously altered.
Refers to the validity of data. Data integrity can be compromised in a number of ways:
Errors that occur when data is transmitted from one computer to another
Software bugs or viruses
Hardware malfunctions, such as disk crashes
Natural disasters, such as fires and floods

There are many ways to minimize these threats to data integrity. These include:
Backing updata regularly
Controlling access to data via security mechanisms
Designing user interfacesthat prevent the input of invalid data
Using error detection and correction software when transmitting data
The accuracy and consistency of stored data, indicated by an absence of any alteration in data between two updates of a data record. Data integrity is imposed within a database at its design stage through the use of standard rules and procedures, and is maintained through the use of error checking and validation routines.
Data Integrity in its broadest meaning refers to the trustworthiness of information over its entire life cycle. In more analytic terms, it is "the representational faithfulness of information to the true state of the object that the information represents, where representational faithfulness is composed of four essential qualities or core attributes: completeness, currency/timeliness, accuracy/correctness and validity/authorization."[1[[|]]] The concept of business rules is already widely used nowadays and is subdivided into six categories which include data rules. Data is further subdivided Data Integrity Rules, data sourcing rules, data extraction rules, data transformation rules and data deployment rules.

Our Definitions in "Plain English"

Integrity is an ITGS term which is related to guaranteeing the accuracy and trustworthiness of stored data. It is said that a data lacks integrity when it has been changed
accidentally or altered on purpose. For example, data integiry can be compromised if a software has a bug or malware that may alter the data, if the hardware malfunctions or when the information is duplicated in a relational database and only one copy is updated or where data entries have been maliciously changed.

Images/Videos and Explanations
This video
external image bullseye.gif

This target represents "accuracy" apsect of integrity. The information must be relevant and accurate to its corresponding field.

external image diabetesbluecircle.png

A circle represents the completeness and uncorrupted state of the information. Information must be fully inputed and must not have bugs.

Examples/Positives of Integrity

  1. Related Databases
  2. Using Anti-virus
  3. Updates to fix bugs
  4. RDMS
  5. Validation techniques

Non-examples/Negatives of Integrity

  1. Accidentally saving wrong changes
  2. Having malware on your computer
  3. Flat-file databases
  4. Low encryption (easy for a Hacker to change information)

Relevant News Articles

“A recent report by the National Academy of Science makes recommendations for ensuring the integrity of research data. Critically, it also highlights the urgent issues regarding the preservation of large datasets.”

“The quest for meaningful, usable data is accelerating as a P4P surge sweeps healthcare”

“Retired Officers Raise Questions on Crime Data”