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

Briefly, sure. Once 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. Knowledge high quality makes use of these standards to measure the extent of information integrity and, in flip, its reliability and applicability for its meant use. Knowledge high quality and integrity are very important to a data-driven group that employs analytics for enterprise choices, presents self-service knowledge entry for inside stakeholders and supplies knowledge choices to clients.

Knowledge integrity

To attain a excessive degree of information 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 through entry controls
• Preserve 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 data governance. That is the apply of making, updating and constantly implementing the processes, guidelines and requirements that forestall errors, knowledge loss, knowledge corruption, mishandling of delicate or regulated knowledge, and knowledge breaches.

The advantages of information integrity

A corporation with a excessive degree of information integrity can:

• Improve the probability and velocity of information recoverability within the occasion of a breach or unplanned downtime
• Defend towards unauthorized entry and knowledge modification
• Obtain and preserve compliance extra successfully

Good knowledge integrity also can enhance enterprise resolution outcomes by rising the accuracy of a company’s analytics. The extra full, correct and constant a dataset is, the extra knowledgeable enterprise intelligence and enterprise processes develop into. In consequence, leaders are higher geared up to set and obtain objectives that profit their group and drive worker and client confidence.

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

The several types of knowledge integrity

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

Bodily knowledge integrity is the safety of information wholeness (that means the info isn’t lacking vital info), 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 information.

Logical knowledge integrity refers back to the safety of information consistency and completeness whereas it’s being accessed by totally different stakeholders and purposes 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 an appropriate 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

Knowledge safety is a subcomponent of information 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. Knowledge recoverability — within the occasion of a breach, assault, energy outage or service interruption — falls below the realm of information safety.

The results of poor knowledge integrity

Human errors, switch errors, malicious acts, inadequate safety and {hardware} malfunctions all contribute to “dangerous 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. Lowered knowledge high quality can lead to productiveness losses, income decline and reputational harm.

Inadequate knowledge safety

Knowledge 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 world laws, such because the European Union’s Normal Knowledge Safety Regulation.

Knowledge high quality

Knowledge high quality is basically the measure of information 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.

Learn how to decide knowledge high quality

Knowledge high quality analysts will assess a dataset utilizing dimensions listed above and assign an general rating. When knowledge ranks excessive throughout each dimension, it’s thought-about high-quality knowledge that’s dependable and reliable for the meant use case or software. 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 a substitute, they will readily entry and analyze datasets with better confidence. Extra 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 constantly and contextualized for the person or software, 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.

Lowered prices and improved regulatory compliance

Excessive-quality knowledge is simple to find and entry. As a result of there isn’t a have to re-create or monitor down datasets, labor prices are diminished, and guide knowledge entry errors develop into much less possible. And since high-quality knowledge is simple to retailer within the appropriate atmosphere in addition to acquire and compile in necessary stories, 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 supply a extra customized and impactful expertise for workers and clients.

The six dimensions of information high quality

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

1. Accuracy: Is the info provably appropriate and does it mirror real-world information?
2. Completeness: Does the info comprise all related and obtainable info? Are there lacking knowledge parts or clean fields?
3. Consistency: Do corresponding knowledge values match throughout places and environments?
4. Validity: Is knowledge being collected within the appropriate format for its meant 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 general rating. A excessive general rating signifies {that a} dataset is dependable, simply accessible, and related.

Learn how to enhance knowledge high quality

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

Knowledge profiling

Knowledge 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.

Knowledge cleaning

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

Knowledge 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 desires.

Geocoding

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 world geographic knowledge requirements are being met. For instance, geographic metadata may also 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.

Knowledge high quality monitoring

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

Batch and real-time validation

That is the deployment of information validation guidelines throughout all purposes and knowledge sorts 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 offers the group a single location to shortly view and assess its datasets no matter the place that knowledge resides or its sort. For instance, buyer knowledge, provide chain info and advertising and marketing knowledge would all reside in an MDM atmosphere.

Knowledge integrity, knowledge high quality and IBM

IBM presents 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 data governance solution helps organizations set up an automatic, metadata-driven basis that assigns knowledge high quality scores to property and improves curation through out-of-the-box automation guidelines to simplify knowledge high quality administration.

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

Learn more about designing the right data architecture to elevate your data quality here.