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Data quality assurance

<div id="content"><p tabindex="0">In the following video, you will delve deeper into the powerful feature of quality seals. This feature enables the establishment of a rigorous governance process by assigning clear responsibilities and action items on a regular basis.</p><div class="kalturaVideoLessonWrapper"><div aria-label="Quality seals" class="kalturaVideoLessonContainer" data-testid="video" id="1_zrsv9543"></div></div></div>

Quality seals

Learning to Use the LeanIX Tool for Successful Enterprise Architecture

Learn how to configure Quality Seals in Linux and enhance data reliability. This video covers the usage, states, attributes, and collaboration involved in data quality assurance.<br><br># This description was auto-generated #

9. Quality Seal.mp4

The tab next to fields is Quality Seals. Quality Seals serve as a configurable mechanism designed to conduct regular data checks, thereby enhancing the reliability of data in Linux. In this course, you will learn how to configure Quality Seals. The actual usage is performed by the responsible person per fax sheet, guiding them through a predefined data quality assurance routine. Within Quality Seals, you can configure two things, the quality state and the mandatory attributes. Quality Seals is a powerful mechanism that combines collaborative editing with clear responsibilities and a rigorous governance process. The main concept is simple. It assigns accountability to the responsible or accountable user to approve the quality of a fact sheet whenever other users make a change to it. Moreover, you can define specific time frame for when the quality shield should break and automatically transition to the check needed state. The following states are available. Check needed, approved, draft, and rejected. In the tool configuration, you have the option to define the time cadence for when this quality seal should trigger regularly. When the seal breaks, the fact sheet owner is responsible for reviewing the data, making updates if necessary, and changing the state from check needed to approved. Open quality states and define a renewal cadence that suits your needs. We recommend requesting approval every six months and enabling the draft mode underneath. Consequently, the quality seal automatically breaks every six months and the state switches to check needed, notifying the responsible person for each fact sheet that a data check is required. The state is then set manually to be approved. If someone makes changes to the fact sheet within the six months, the quality state is automatically set to check needed. If you want to allow applications in the draft state to be rejected, also enable the Rejected checkbox in the metamodel configuration. The quality seal can be extended by mandatory attributes which introduce new states or phases of the quality seal. Click Mandatory Attributes. You can now define certain attributes as mandatory. You specify which tags, fields, subscriptions, or relations must be filled in, in order to change the quality state to approved. If no information is stored within the attributes defined as mandatory, the state cannot be set to approved. Unused or read-only fields should not be defined as mandatory. This configuration tab will only be active when the draft state is enabled. As we did earlier within the quality state section, within the box you will see all the active and possible attributes that can be set as mandatory. You can define multiple attributes as such. As a best practice, we suggest setting business criticality, lifecycle, functional, and technical fit as mandatory fields for applications. The number of mandatory attributes should be chosen wisely to avoid overwhelming the responsible person. In the permission tab, you can specify which user roles can view and edit information within a FACCY type. The audit log tab records every configuration change made by an admin. Throughout this course, won't extensively cover these two tabs. For more details, please refer to our user documentation. Let's briefly recap what effective data quality assurance entails. To achieve your desired outcomes in your enterprise architecture practice, success heavily relies on precise data quality. As you now know, sustaining data quality involves collaborating with various stakeholders. These collaborators can be assigned to specific roles, outlining the responsibilities for a particular fact sheet. By leveraging the metamodel configuration, you can establish robust long-term data governance and consequently ensure reliable data quality assurance. For learning more about how you can leverage quality seal, different quality states, and mandatory attributes, please read more on our documentation page.