Data integrity vs. data quality: Is there a difference?


Jul 10, 2023

Briefly, sure. After we discuss knowledge integrity, we’re referring to the overarching completeness, accuracy, consistency, accessibility, and safety of a company’s knowledge. Collectively, these elements decide the reliability of the group’s knowledge. Information high quality makes use of these standards to measure the extent of knowledge integrity and, in flip, its reliability and applicability for its supposed use. Information high quality and integrity are important to a data-driven group that employs analytics for enterprise choices, gives self-service knowledge entry for inside stakeholders and supplies knowledge choices to prospects.

Information integrity

To attain a excessive degree of knowledge integrity, a company implements processes, guidelines and requirements that govern how knowledge is collected, saved, accessed, edited and used. These processes, guidelines and requirements work in tandem to:

  • Validate knowledge and enter
  • Take away duplicate knowledge
  • Present knowledge backups and guarantee enterprise continuity
  • Safeguard knowledge by way of entry controls
  • Keep an audit path for accountability and compliance
A corporation can use any variety of instruments and personal or public cloud environments all through the info lifecycle to take care of knowledge integrity via one thing often called knowledge governance. That is the follow of making, updating and persistently imposing the processes, guidelines and requirements that stop errors, knowledge loss, knowledge corruption, mishandling of delicate or regulated knowledge, and knowledge breaches.

The advantages of knowledge integrity

A corporation with a excessive degree of knowledge integrity can:

  • Enhance the chance and pace of knowledge recoverability within the occasion of a breach or unplanned downtime
  • Defend in opposition to unauthorized entry and knowledge modification
  • Obtain and preserve compliance extra successfully
Good knowledge integrity can even enhance enterprise choice outcomes by growing the accuracy of a company’s analytics. The extra full, correct and constant a dataset is, the extra knowledgeable enterprise intelligence and enterprise processes turn into. Because of this, leaders are higher outfitted to set and obtain targets that profit their group and drive worker and shopper confidence.

Information science duties equivalent to machine studying additionally tremendously profit from good knowledge integrity. When an underlying machine studying mannequin is being skilled on knowledge information which might be reliable and correct, the higher that mannequin will probably be at making enterprise predictions or automating duties.

The several types of knowledge integrity

There are two primary classes of knowledge integrity: Bodily knowledge integrity and logical knowledge integrity.

Bodily knowledge integrity is the safety of knowledge wholeness (which means the info isn’t lacking necessary data), accessibility and accuracy whereas knowledge is saved or in transit. Pure disasters, energy outages, human error and cyberattacks pose dangers to the bodily integrity of knowledge.

Logical knowledge integrity refers back to the safety of knowledge consistency and completeness whereas it’s being accessed by completely different stakeholders and functions throughout departments, disciplines, and places. Logical knowledge integrity is achieved by:

  • Stopping duplication (entity integrity)
  • Dictating how knowledge is saved and used (referential integrity)
  • Preserving knowledge in a suitable format (area integrity)
  • Making certain knowledge meets a company’s distinctive or industry-specific wants (user-defined integrity)
How knowledge integrity differs from knowledge safety

Information safety is a subcomponent of knowledge integrity and refers back to the measures taken to forestall unauthorized knowledge entry or manipulation. Efficient knowledge safety protocols and instruments contribute to robust knowledge integrity. In different phrases, knowledge safety is the means whereas knowledge integrity is the purpose. Information recoverability — within the occasion of a breach, assault, energy outage or service interruption — falls beneath the realm of knowledge safety.

The results of poor knowledge integrity

Human errors, switch errors, malicious acts, inadequate safety and {hardware} malfunctions all contribute to “unhealthy knowledge,” which negatively impacts a company’s knowledge integrity. A corporation contending with a number of of those points dangers experiencing:

Poor knowledge high quality

Low-quality knowledge results in poor decision-making due to inaccurate and uninformed analytics. Decreased knowledge high quality can lead to productiveness losses, income decline and reputational harm.

Inadequate knowledge safety

Information that isn’t correctly secured is at an elevated danger of a knowledge breach or being misplaced to a pure catastrophe or different unplanned occasion. And with out correct perception and management over knowledge safety, a company can extra simply fall out of compliance with native, regional, and international laws, such because the European Union’s Basic Information Safety Regulation.

Information high quality

Information high quality is actually the measure of knowledge integrity. A dataset’s accuracy, completeness, consistency, validity, uniqueness, and timeliness are the info high quality measures organizations make use of to find out the info’s usefulness and effectiveness for a given enterprise use case.

Find out how to decide knowledge high quality

Information high quality analysts will assess a dataset utilizing dimensions listed above and assign an total rating. When knowledge ranks excessive throughout each dimension, it’s thought-about high-quality knowledge that’s dependable and reliable for the supposed use case or utility. To measure and preserve high-quality knowledge, organizations use knowledge high quality guidelines, also referred to as knowledge validation guidelines, to make sure datasets meet standards as outlined by the group.

The advantages of fine knowledge high quality

Improved effectivity

Enterprise customers and knowledge scientists don’t must waste time finding or formatting knowledge throughout disparate programs. As an alternative, they’ll readily entry and analyze datasets with better confidence. Further time is saved that may have in any other case been wasted on appearing on incomplete or inaccurate knowledge.

Elevated knowledge worth

As a result of knowledge is formatted persistently and contextualized for the consumer or utility, organizations can derive worth from knowledge which will have in any other case been discarded or ignored.

Improved collaboration and higher decision-making

Excessive-quality knowledge eliminates incongruency throughout programs and departments and ensures constant knowledge throughout processes and procedures. Collaboration and decision-making amongst stakeholders are improved as a result of all of them depend on the identical knowledge.

Decreased prices and improved regulatory compliance

Excessive-quality knowledge is straightforward to find and entry. As a result of there isn’t a must re-create or observe down datasets, labor prices are diminished, and guide knowledge entry errors turn into much less doubtless. And since high-quality knowledge is straightforward to retailer within the right setting in addition to accumulate and compile in obligatory reviews, a company can higher guarantee compliance and keep away from regulatory penalties.

Improved worker and buyer experiences

Excessive-quality knowledge supplies extra correct, in-depth insights a company can use to offer a extra customized and impactful expertise for workers and prospects.

The six dimensions of knowledge high quality

To find out knowledge high quality and assign an total rating, analysts consider a dataset utilizing these six dimensions, also referred to as knowledge traits:

  1. Accuracy: Is the info provably right and does it mirror real-world information?
  2. Completeness: Does the info comprise all related and accessible data? Are there lacking knowledge components or clean fields?
  3. Consistency: Do corresponding knowledge values match throughout places and environments?
  4. Validity: Is knowledge being collected within the right format for its supposed use?
  5. Uniqueness: Is knowledge duplicated or overlapping with different knowledge?
  6. Timeliness: Is knowledge updated and available when wanted?
The upper a dataset scores in every of those dimensions, the better its total rating. A excessive total rating signifies {that a} dataset is dependable, simply accessible, and related.

Find out how to enhance knowledge high quality

Some widespread strategies and initiatives organizations use to enhance knowledge high quality embrace:

Information profiling

Information profiling, also referred to as knowledge high quality evaluation, is the method of auditing a company’s knowledge in its present state. That is accomplished to uncover errors, inaccuracies, gaps, inconsistent knowledge, duplications, and accessibility boundaries. Any variety of knowledge high quality instruments can be utilized to profile datasets and detect knowledge anomalies that want correction.

Information cleaning

Information cleaning is the method of remediating the info high quality points and inconsistencies found throughout knowledge profiling. This consists of the deduplication of datasets, in order that a number of knowledge entries don’t unintentionally exist in a number of places.

Information standardization

That is the method of conforming disparate knowledge property and unstructured large knowledge right into a constant format that ensures knowledge is full and prepared to be used, no matter knowledge supply. To standardize knowledge, enterprise guidelines are utilized to make sure datasets conform to a company’s requirements and wishes.


Geocoding is the method of including location metadata to a company’s datasets. By tagging knowledge with geographical coordinates to trace the place it originated from, the place it has been and the place it resides, a company can guarantee nationwide and international geographic knowledge requirements are being met. For instance, geographic metadata may help a company make sure that its administration of buyer knowledge stays compliant with GDPR.

Matching or linking

That is the strategy of figuring out, merging, and resolving duplicate or redundant knowledge.

Information high quality monitoring

Sustaining good knowledge high quality requires steady knowledge high quality administration. Information high quality monitoring is the follow of revisiting beforehand scored datasets and reevaluating them primarily based on the six dimensions of knowledge high quality. Many knowledge analysts use a knowledge high quality dashboard to visualise and observe knowledge high quality KPIs.

Batch and real-time validation

That is the deployment of knowledge validation guidelines throughout all functions and knowledge varieties at scale to make sure all datasets adhere to particular requirements. This may be accomplished periodically as a batch course of, or constantly in actual time via processes like change knowledge seize.

Grasp knowledge administration

Grasp knowledge administration (MDM) is the act of making and sustaining an organization-wide centralized knowledge registry the place all knowledge is cataloged and tracked. This provides the group a single location to rapidly view and assess its datasets no matter the place that knowledge resides or its sort. For instance, buyer knowledge, provide chain data and advertising and marketing knowledge would all reside in an MDM setting.

Information integrity, knowledge high quality and IBM

IBM gives a variety of built-in knowledge high quality and governance capabilities together with knowledge profiling, knowledge cleaning, knowledge monitoring, knowledge matching and knowledge enrichment to make sure knowledge customers have entry to trusted, high-quality knowledge. IBM’s knowledge governance resolution helps organizations set up an automatic, metadata-driven basis that assigns knowledge high quality scores to property and improves curation by way of out-of-the-box automation guidelines to simplify knowledge high quality administration.

With knowledge observability capabilities, IBM may help organizations detect and resolve points inside knowledge pipelines quicker. The partnership with Manta for automated knowledge lineage capabilities allows IBM to assist purchasers discover, observe and stop points nearer to the supply.

Be taught extra about designing the appropriate knowledge structure to raise your knowledge high quality right here.

Senior Product Supervisor, Watson Data Catalog

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