Self-service Business Intelligence Tools For Data Cleansing Techniques

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Self-service Business Intelligence Tools For Data Cleansing Techniques – Series of articles. This series focuses primarily on the Power BI workload in Microsoft Fabric. For an introduction to the series, see Planning your Power BI deployment.

. The data architecture is often maintained by a single team of centralized BI experts, while reporting responsibility rests with developers within departments or business units.

Self-service Business Intelligence Tools For Data Cleansing Techniques

Self-service Business Intelligence Tools For Data Cleansing Techniques

Typically, there are many more report builders than semantic modelers (formerly known as datasets). These report builders can exist in any area of ​​the organization. Since self-service reports often need to produce content quickly, a blended approach allows them to focus on producing reports that support timely decision-making without the added effort of building a semantic model.

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The managed self-service BI scenario is the first of the self-service BI scenarios. See the article Power BI use cases for a complete list of self-service BI scenarios.

For the sake of brevity, this article does not address some aspects described in the collaboration scenarios and content delivery topic. For complete coverage, read these articles first.

The following diagram shows a high-level overview of the most common user actions and Power BI components that support managed self-service BI. The main goal for many report builders is to reuse centralized shared semantic models. To achieve this, this scenario focuses on decoupling the model development process from the reporting process.

We encourage you to download the scenario diagram if you wish to incorporate it into your presentation, documentation or blog post or print it as a wall poster. Because it’s a Scalable Vector Graphics (SVG) image, you can scale it up or down without losing quality.

Microsoft Power Bi Services

Semantic modelers develop models using Power BI Desktop. For semantic models intended for reuse, it is common (but not required) for the creators to belong to a centralized team that supports users across organizational boundaries (such as IT, business BI, or Center of Excellence).

Development of data models is done in Power BI Desktop. Extra effort is made to create a well-designed and user-friendly template because many self-service report builders will use it as a data source. Modelers can use DAX queries to develop and explore the model under development.

When ready, semantic modelers publish their Power BI Desktop file (.pbix) or Power BI project file (.pbip)

Self-service Business Intelligence Tools For Data Cleansing Techniques

The semantic model is published in a workspace dedicated to storing and securing shared semantic models. Since the semantic model is intended for reuse, it is endorsed (certified or promoted, as appropriate). The semantic model is also marked as visible to further encourage reuse. The line view in the Power BI service can be used to trace the dependencies that exist between Power BI elements, including reports associated with the semantic model.

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Semantic model discovery in the OneLake data center is enabled because the semantic model is marked as visible. Discovery makes the existence of a semantic model visible in the data center to other Power BI content creators looking for data.

Report creators use the OneLake data center in the Power BI service to search for discoverable data elements, such as semantic models.

If report creators do not have permission, they can request building permission for the data elements. This starts a workflow to request planning permission from an authorized approver. Once approved, the report creator can reuse the data elements to create new reports.

Report creators create new reports using Power BI Desktop. Reports use a live connection to a shared semantic model.

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Report builders develop reports in Power BI Desktop. In addition to the report, report creators can use custom themes, images, and visuals, and can create report-level metrics.

Published reports remain associated with shared semantic models stored in another workspace. Any changes to the shared semantic model affect all reports associated with it.

Other self-service report builders can create new reports using the existing shared semantic model. Report creators can choose to use Power BI Desktop, Power BI Report Builder or Excel.

Self-service Business Intelligence Tools For Data Cleansing Techniques

Some data sources may require a local data gateway or VNet gateway to update the data, e.g. those found on a private organizational network.

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The task is to minimize the number of semantic models. This scenario is about shared semantic models that help achieve a

For simplicity, the scenario diagram represents only a shared semantic model. However, it is usually not practical to model all of the organization’s data into a single semantic model. The other extreme is creating a new semantic model for each report, which less experienced content creators often do. The goal of managed self-service BI is to find the right balance, lean towards relatively few semantic models and create new semantic models when it makes sense to do so.

When the semantic model is decoupled from reporting, it facilitates the separation of effort and responsibility. A centralized team (such as IT, BI or Center of Excellence) typically maintains a shared semantic model, while reporting is maintained by subject matter experts in business units. However, it is not necessary. For example, this pattern can be adopted by any content creator who wants to achieve reuse.

For simplicity, data streams are not represented in the scenario diagram. For information about data streams, see the self-service data preparation scenario.

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The semantic model conveys to creators that the data is reliable and meets the organization’s quality standards. ONE

The semantic model emphasizes that the owner of the semantic model believes that data is valuable and valuable for others to use.

It is best practice to have a consistent, repeatable and rigorous content approval process. Certified content should indicate that the quality of the data has been validated. It should also follow change management rules, have formal support and be fully documented. Since certified content has passed strict standards, expectations for reliability are higher.

Self-service Business Intelligence Tools For Data Cleansing Techniques

OneLake’s data center helps report creators find, explore, and use semantic models across the organization. In addition to supporting semantic models, it is crucial to enable the discovery of semantic models to promote their reuse. A visible semantic model in the data center for report creators looking for data.

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If a semantic model is not configured to be discovered, only Power BI users with build permission can find it.

A report builder can find a semantic model in the data center that they want to use. If they do not have planning permission for the semantic model, they can request access. Depending on the semantic model access request settings, an email will be sent to the owner of the semantic model or personal instructions will be presented to the person requesting access.

A Power BI Desktop live connection connects a report to an existing semantic model. Live connections avoid the need to create a new data model in the Power BI Desktop file.

When using a live connection, all data that the report creator needs must reside within the connected semantic model. However, the custom managed self-service BI scenario describes how a semantic model can be extended with additional data and calculations.

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There are several advantages to publishing reports to a different workspace than where the semantic model is stored.

First, there is clarity about who is responsible for managing content in which work area. Second, report creators have permission to publish content to a report workspace (via the workspace’s admin, member, or contributor roles). However, they only have read and compile permissions for specific semantic models. This technique allows row-level security (RLS) to take effect when necessary for users assigned the viewer role.

When you publish a Power BI Desktop report to a workspace, RLS roles are applied to members assigned the viewer role in the workspace. Even if viewers have compile permission for the semantic model, RLS still applies. See Use RLS with workspaces in Power BI for more information.

Self-service Business Intelligence Tools For Data Cleansing Techniques

When a shared semantic model is used by many reports, those reports can exist in many workspaces. The line view helps identify and understand downstream dependencies. When planning a semantic model change, first perform an impact analysis to understand which dependent reports may require editing or testing.

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Typically, a data gateway is required when accessing data sources located within the organization’s private network or virtual network. The local data gateway becomes relevant when a Power BI Desktop file is published to the Power BI service. The two purposes of a gateway are to update imported data or to view a report that queries a direct connection or a DirectQuery semantic model.

. In default mode, the data gateway supports direct connection and DirectQuery operations (in addition to scheduled data update operations).

The activity log records user activities that occur in the Power BI service. Power BI administrators can use the activity log data collected to perform audits to help them understand usage patterns and adoption. Activity logging is also valuable in supporting governance efforts, security audits and compliance requirements. With a managed self-service BI scenario, tracking the use of shared semantic models is especially useful. A high report-to-semantic model ratio indicates good reuse of semantic models.

In the next article in this series, we discuss ways to adapt and extend a shared semantic model to meet additional types of requirements. All companies operate on data – information generated from the many sources internal and external to your company. E

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