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

In brief, sure. Once we discuss knowledge integrity, we’re referring to the overarching completeness, accuracy, consistency, accessibility, and safety of a corporation’s knowledge. Collectively, these components decide the reliability of the group’s knowledge. Knowledge high quality makes use of these standards to measure the extent of knowledge integrity and, in flip, its reliability and applicability for its meant use. Knowledge high quality and integrity are important to a data-driven group that employs analytics for enterprise choices, gives self-service knowledge entry for inner stakeholders and supplies knowledge choices to prospects.

Knowledge integrity

To attain a excessive degree of knowledge integrity, a corporation 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 by means of one thing generally known as knowledge governance. That is the follow of making, updating and constantly imposing the processes, guidelines and requirements that forestall 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:

• Improve the chance and velocity of knowledge recoverability within the occasion of a breach or unplanned downtime
• Defend in opposition to unauthorized entry and knowledge modification
• Obtain and keep compliance extra successfully

Good knowledge integrity can even enhance enterprise determination outcomes by growing the accuracy of a corporation’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 outfitted to set and obtain targets that profit their group and drive worker and client confidence.

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

The various kinds of knowledge integrity

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

Bodily knowledge integrity is the safety of knowledge wholeness (that means the info isn’t lacking necessary 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 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 an appropriate format (area integrity)
• Guaranteeing knowledge meets a corporation’s distinctive or industry-specific wants (user-defined integrity)

How knowledge integrity differs from knowledge safety

Knowledge safety is a subcomponent of knowledge integrity and refers back to the measures taken to stop unauthorized knowledge entry or manipulation. Efficient knowledge safety protocols and instruments contribute to sturdy 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 beneath the realm of knowledge safety.

The implications of poor knowledge integrity

Human errors, switch errors, malicious acts, inadequate safety and {hardware} malfunctions all contribute to “dangerous knowledge,” which negatively impacts a corporation’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. Diminished knowledge high quality can lead to productiveness losses, income decline and reputational injury.

Inadequate knowledge safety

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

Knowledge high quality

Knowledge high quality is basically 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.

How one can decide knowledge high quality

Knowledge 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 of high-quality knowledge that’s dependable and reliable for the meant use case or software. To measure and keep 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 excellent 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 larger confidence. Extra time is saved that will 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.

Diminished prices and improved regulatory compliance

Excessive-quality knowledge is straightforward to find and entry. As a result of there is no such thing as a must re-create or observe down datasets, labor prices are diminished, and guide knowledge entry errors develop into much less possible. And since high-quality knowledge is straightforward to retailer within the right atmosphere in addition to acquire and compile in obligatory studies, a corporation 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 corporation can use to supply 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 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 right 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 larger its total rating. A excessive total rating signifies {that a} dataset is dependable, simply accessible, and related.

How one can enhance knowledge high quality

Some widespread 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 corporation’s knowledge in its present state. That is achieved 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 belongings and unstructured huge 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 corporation’s requirements and desires.

Geocoding

Geocoding is the method of including location metadata to a corporation’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 corporation can guarantee nationwide and international geographic knowledge requirements are being met. For instance, geographic metadata may also help a corporation 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 follow of revisiting beforehand scored datasets and reevaluating them primarily based on the six dimensions of knowledge high quality. Many knowledge analysts use an information 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 achieved periodically as a batch course of, or constantly in actual time by means of 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 kind. For instance, buyer knowledge, provide chain info and advertising knowledge would all reside in an MDM atmosphere.

Knowledge 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 shoppers 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 belongings and improves curation by way of 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 shoppers discover, observe and forestall points nearer to the supply.

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