{"id":534,"date":"2022-02-11T13:39:09","date_gmt":"2022-02-11T13:39:09","guid":{"rendered":"http:\/\/definehub.com\/?page_id=534"},"modified":"2024-03-27T13:42:44","modified_gmt":"2024-03-27T11:42:44","slug":"data-auditing-process-in-define","status":"publish","type":"page","link":"https:\/\/definehub.com\/en\/data-auditing-process-in-define\/","title":{"rendered":"Data Auditing Process in Define"},"content":{"rendered":"
Home<\/a><\/span><\/span><\/div>

Data Auditing Process in Define<\/strong><\/h1><\/div>

Ensures the quality of your information<\/h3><\/div>

To ensure interoperability between software systems, it is of paramount importance Data Templates and their elements are clearly and uniformly described. Therefore, we have put in place a number of functionalities and processes in Define that help users create quality content. These functionalities and processes are aligned with relevant standards like EN ISO 23386, EN ISO 23387 and PAS 14191.<\/p>\n

To ensure high-quality data across all users and guaranteeing our Data Templates reflect the reality of the construction process in the real world, Define is following the processes of both \u202fContent Approval and Data auditing<\/strong>. While the Content Approval is an EN ISO 23386 compliant process where Domain experts judge the relevance of new concepts proposed by other users, the Data auditing process is an internal process, where Define\u2019s Data auditors ensure consistent structuring and overall health of Define\u2019s database. In this section, we will focus mainly on the Data auditing process but will also refer to some of the main aspects of the Content Approval process.<\/p>\n<\/div>

What is Data Auditing in Define?<\/h3><\/div>

All users of Define are responsible for maintaining the quality of their database in the system. But in addition to this, the Define experts take further measures to support the overall quality of the Properties and Data Templates created in the platform. This is done by executing an in-house process called Data Auditing. This is a mandatory step for users of Define and it is meant to guarantee that all users rely on the same quality of Data Templates.<\/p>\n<\/div>

Data Quality Measure 1: Prevention of duplicates<\/h3><\/div>

One of the major goals of the Data auditing process is to prevent the occurrence of concepts that represent the same piece of information e.g., several properties that represent the performance characteristic \u201cresistance to fire\u201d.
\nSuch concepts are called duplicates and must be avoided because they:
\n– result in additional effort for all Define Users;
\n– artificially inflate the Define database;
\n–\u00a0interfere with interoperability, since two users may want to exchange the same information but use different concepts to represent it;
\nTake a look at example 1, where all three properties below have similar full names, making them potential duplicates in need of deliberation:<\/p>\n<\/div>

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In the case of a confirmed duplicate in Define, additional actions by Data auditors will follow like deactivation of the duplicate, its replacement with the correct concept or proposal on how to be edited so that all concepts in Define stay unique.<\/p>\n<\/div>

Data Quality Measure 2: Data Consistency<\/h3><\/div>

Another major goal of Data auditing is to ensure a clear and consistent representation of all concepts across all use cases.<\/p>\n

As there are multiple Content experts (users who create new draft concepts) working independently in Define, this sometimes might result in incomplete or erroneous data input, leading to unclear or inconsistent concepts. Another factor is the evolving nature of the industry resulting in older concepts being inconsistent with newer ones.<\/p>\n

Besides avoiding duplicates, users are responsible for creating consistent content and data auditors are responsible for the prevention of inconsistencies.<\/p>\n<\/div>

Approval Process Workflow<\/h3><\/div>

By signing a contract agreement, clients agree that any concepts they wish to create in Define will be subject to Data auditing in order to guarantee the quality of their data. However, they may decide to input concepts without an Approval process by a Domain expert, although it is not recommended and Define experts can be allocated to perform this service on the client\u2019s behalf. A Domain expert must examine any new concept drafts before they become official concepts. This process ensures that concepts are relevant to the construction industry and the particular use case they are created for.
\nIn the graph below you can find the Approval process workflow.<\/p>\n<\/div>

<\/a><\/span><\/div>

\u2022 The Approval Process is initiated when a Content expert completes a draft concept and sends it to be approved by a Domain expert.
\n\u2022 The Domain expert\u2019s responsibility is to judge whether the new concept is factually true and relevant to the use case according to their field of expertise (e.g. structural design, environmental evaluation, thermal analysis etc.).
\n\u2022 The Domain expert is eligible to reject or request more information from the Content expert to make a better judgement. Define has the necessary functionalities in place that allow this to be done inside the system.
\n\u2022 Once all the necessary information is collected, the Domain expert approves the Draft to become a concept.<\/p>\n<\/div>

Data Auditing Process<\/h3><\/div>

As previously mentioned, the Data Auditing process is in place to ensure the quality of data across all users and Define clients agree that any concepts they wish to create in Define will be subject to Data auditing, before officially recorded in Define.<\/p>\n

In the following workflow, you can track a typical Data auditing process from its initiation when a Content expert completes a draft concept and sends it to be approved.<\/p>\n<\/div>

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\u2022 The Content expert completes a draft concept and sends it to be approved by a Data auditor and Domain expert.
\n\u2022 Data auditors check each draft for inconsistencies and compare it against the existing database content whether any confirmed duplicates exist. They must examine any new concept drafts before they become official concepts.<\/p>\n

\u2013 If the proposal follows consistency guidelines, the draft concept is sent for expert approval by a Domain expert. The Domain expert checks if the proposal follows the credible sources and if \u201cyes\u201d, it is published in Define.
\n\u2013 If any inconsistencies or confirmed duplicates are found the Data auditor rejects the proposal or suggests how to be fixed and agrees with the Content expert. In case the Content expert decides to keep the draft version, it is listed for Private use*.
\n* A concept for Private use can only be used in the Content expert\u2019s own context and not shared with other contexts or solutions. Regardless, Define experts strongly recommend following the Data auditor\u2019s proposals and avoid Private use whenever possible.
\nPlease note that regular users are always welcome to contribute to the Data auditing process. In case users find any duplicates or inconsistencies, they can reach out to our Support team:<\/p>\n<\/div>

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\u2022 If a user finds a duplicate, he can reach out to Define Support team.
\n\u2022 Data auditor analyses the report and in case:<\/p>\n

\u2013 no issues are found, returns a statement to the user.
\n\u2013 issues are found, he updates the item accordingly.<\/p>\n<\/div>

To sum up<\/h3><\/div>

The EN ISO 23386 Content approval process is implemented in Define and the additional in-house Data auditing processes help customers to end up with healthy data structures, avoiding errors, data loss and breakdown of information exchange. This feature of Define sets it apart from other similar platforms for data management in construction and ensures that only high-quality content circulates in the system.<\/p>\n<\/div><\/div><\/div><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"yoast_head":"\nData Auditing Process in Define - Define Hub<\/title>\n<meta name=\"description\" content=\"To ensure high-quality data across all users, Define is following the processes of both\u202f Content Approval and Data auditing.\" \/>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Data Auditing Process in Define - Define Hub\" \/>\n<meta property=\"og:description\" content=\"To ensure high-quality data across all users, Define is following the processes of both\u202f Content Approval and Data auditing.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/definehub.com\/en\/data-auditing-process-in-define\/\" \/>\n<meta property=\"og:site_name\" content=\"Define Hub\" \/>\n<meta property=\"article:modified_time\" content=\"2024-03-27T11:42:44+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"8 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/definehub.com\/en\/data-auditing-process-in-define\/\",\"url\":\"https:\/\/definehub.com\/en\/data-auditing-process-in-define\/\",\"name\":\"Data Auditing Process in Define - 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