Difference between revisions of "Single source of truth"

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A [[wikipedia:Single source of truth|single source of truth]], often abbreviated as [[SSOT]] is a discipline or pragmatic approach to store data in only '''one''' place. This '''one''' place, doesn't mean that the data has only one copy, but the way data being retrieved and presented are always going through a single channel, often managed by a singular version control system.
{{WikiEntry|key=Single source of truth|qCode=7523885}}, often abbreviated as [[SSOT]]/[[SPOT]] (a.k.a. [[Single Point of Truth]]) is a discipline or pragmatic approach to store data in only '''one''' abstract place. This '''one''' place, doesn't mean that the data has only one copy, but the way data being retrieved and presented are always going through a single channel(See [[3-2-1 Backup Rule]] )<ref>{{:3-2-1 Backup Rule}}</ref>, often managed by a version control system.
 
=Excerpts from Wikipedia=
 
In [[wikipedia:information science|information science]] and [[wikipedia:information technology|information technology]], '''single source of truth''' ('''SSOT''') architecture, or '''single point of truth''' ('''SPOT''') architecture, for [[wikipedia:information system|information system]]s is the practice of structuring [[wikipedia:information model|information model]]s and associated [[wikipedia:database schema|data schema]]s such that every [[wikipedia:data element|data element]] is [[wikt:master copy#Noun|mastered]] (or edited) in only one place, providing [[wikipedia:canonical form#Computing|data normalization to a canonical form]] (for example, in [[wikipedia:database normalization|database normalization]] or content [[wikipedia:transclusion|transclusion]]). Any possible linkages to this data element (possibly in other areas of the relational schema or even in distant [[wikipedia:federated database|federated database]]s) are by [[wikipedia:Reference (computer science)|reference]] only. Because all other locations of the data just refer back to the primary "source of truth" location, updates to the data element in the primary location propagate to the entire system, providing multiple advantages simultaneously: greater [[wikipedia:efficiency|efficiency]]/[[wikipedia:productivity|productivity]], easy prevention of mistaken inconsistencies (such as a duplicate value/copy somewhere being forgotten), and greatly simplified [[wikipedia:version control|version control]]. Without SSOT architecture, rampant [[wikt:fork#process|forking]] impairs clarity and productivity, imposing laborious maintenance needs.
 
Deployment of an SSOT architecture is becoming increasingly important in enterprise settings where incorrectly linked duplicate or de-normalized data elements (a direct consequence of intentional or unintentional [[wikipedia:denormalization|denormalization]] of any explicit data model) pose a risk for retrieval of outdated, and therefore incorrect, information. Common examples (i.e., example classes of implementation) are as follows:
 
* In [[wikipedia:electronic health record|electronic health record]]s (EHRs), it is imperative to accurately validate patient identity against a single referential repository, which serves as the SSOT. Duplicate representations of data within the enterprise would be implemented by the use of [[wikipedia:pointer (computer programming)|pointer]]s rather than duplicate database tables, rows, or cells. This ensures that data updates to elements in the authoritative location are comprehensively distributed to all [[wikipedia:federated database|federated database]] constituencies in the larger overall [[wikipedia:enterprise architecture|enterprise architecture]]. EHRs are an excellent class for exemplifying how SSOT architecture is both poignantly necessary and challenging to achieve: it is challenging because inter-organization [[wikipedia:health information exchange|health information exchange]] is inherently a [[wikipedia:computer security|cybersecurity]] competence hurdle, and nonetheless it is necessary, to prevent [[wikipedia:medical error|medical error]]s, to prevent the wasted costs of inefficiency (such as duplicated work or rework), and to make the [[wikipedia:primary care|primary care]] and [[wikipedia:medical home|medical home]] concepts feasible (to achieve competent [[wikipedia:transitional care|care transitions]]).
* [[wikipedia:Single-source publishing|Single-source publishing]] as a general principle or ideal in [[wikipedia:content management|content management]] relies on having SSOTs, via [[wikipedia:transclusion|transclusion]] or (otherwise, at least) substitution. Substitution happens via [[wikipedia:library (computing)#Object libraries|libraries of objects]] that can be propagated as static copies which are later refreshed when necessary (that is, when refreshing of the [[wikipedia:cut, copy, and paste|copy-paste]] or [[wikipedia:import and export of data|import]] is triggered by a larger updating event, such as a new scientific advance or a piece of breaking news).<!--Tip/example: The Wikipedian instantiation of this distinction, as applied to Wikipedian templates, is that of [[Help:Transclusion]] versus [[Wikipedia:Substitution]].--> [[wikipedia:Component content management system|Component content management system]]s are a class of [[wikipedia:content management system|content management system]]s that aim to provide competence on this level.
 
Ideally, SSOT systems provide data that are authentic (and [[wikipedia:authentication|authenticatable]]), relevant, and [[wikipedia:reference (computer science)|referable]].


=The Entropy Pool=
=The Entropy Pool=
To relate Single source of truth in physical reality, the use of [[entropy pool]] across many networked computing system is essential to this goal.
To relate Single source of truth in physical reality, the notion of a [[shared clock]], or more technically, the idea of a common [[entropy pool]], is essential to achieve this goal. A well-known example in setting up one large [[entropy pool]] is [[Bitcoin]]'s [[blockchain]].


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<noinclude>
=References=
=References=
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==Related Pages==
==Related Pages==
*[[Logically related::Data]]
*[[Logically related::Data]]
*[[Logically related::Data Governance]]
*[[Logically related::Governance]]
*[[Logically related::Version Control]]
*[[Logically related::Version Control]]
*[[Logically related::SSOT]]
*[[Logically related::SSOT]]
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*[[Logically related::Data Science]]
*[[Logically related::Data Science]]
[[Category:Meta Data]]
[[Category:Meta Data]]
[[Category:DevOps]]
</noinclude>
</noinclude>

Latest revision as of 04:24, 14 June 2022

Single source of truth(Q7523885), often abbreviated as SSOT/SPOT (a.k.a. Single Point of Truth) is a discipline or pragmatic approach to store data in only one abstract place. This one place, doesn't mean that the data has only one copy, but the way data being retrieved and presented are always going through a single channel(See 3-2-1 Backup Rule )[1], often managed by a version control system.

Excerpts from Wikipedia

In information science and information technology, single source of truth (SSOT) architecture, or single point of truth (SPOT) architecture, for information systems is the practice of structuring information models and associated data schemas such that every data element is mastered (or edited) in only one place, providing data normalization to a canonical form (for example, in database normalization or content transclusion). Any possible linkages to this data element (possibly in other areas of the relational schema or even in distant federated databases) are by reference only. Because all other locations of the data just refer back to the primary "source of truth" location, updates to the data element in the primary location propagate to the entire system, providing multiple advantages simultaneously: greater efficiency/productivity, easy prevention of mistaken inconsistencies (such as a duplicate value/copy somewhere being forgotten), and greatly simplified version control. Without SSOT architecture, rampant forking impairs clarity and productivity, imposing laborious maintenance needs.

Deployment of an SSOT architecture is becoming increasingly important in enterprise settings where incorrectly linked duplicate or de-normalized data elements (a direct consequence of intentional or unintentional denormalization of any explicit data model) pose a risk for retrieval of outdated, and therefore incorrect, information. Common examples (i.e., example classes of implementation) are as follows:

Ideally, SSOT systems provide data that are authentic (and authenticatable), relevant, and referable.

The Entropy Pool

To relate Single source of truth in physical reality, the notion of a shared clock, or more technically, the idea of a common entropy pool, is essential to achieve this goal. A well-known example in setting up one large entropy pool is Bitcoin's blockchain.


References

  1. The 3-2-1 backup rule
    1. There should be 3 copies of data
    2. On 2 different media
    3. With 1 copy being off site

    References

    <references/>

    Related Pages

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Related Pages

References


Related Pages