Organizations can choose to deliver and manage their Power BI deployment through IT and standard project workflows or to empower certain business users to take advantage of Self-Service BI capabilities with tools such as Power BI Desktop and Excel. In many scenarios, a combination of IT resources, such as the On-premises data gateway and Power BI Premium capacity, can be combined with the business users' knowledge of requirements and familiarity with data analysis and visualization.

Organizations may also utilize alternative deployment modes per project or with different business teams based on available resources and the needs of the project. The greatest value from Power BI deployments can be obtained when the technical expertise and governance of Corporate BI solutions are combined with the data exploration and analysis features, which can be made available to all users. The scalability and accessibility of Power BI solutions to support thousands of users, including read-only users who have not been assigned Power BI Pro licenses, is made possible by provisioning Power BI Premium capacity, as described in the final three chapters of this book.

The Corporate BI delivery approach in which the BI team develops and maintains both the Power BI dataset (data model) and the required report visualizations is a common deployment option, particularly for large-scale projects and projects with executive-level sponsors or stakeholders. This is the approach followed in this chapter and throughout this book, as it offers maximum control over top BI objectives, such as version control, scalability, usability, and performance.

However, as per the following Power BI deployment modes diagram, there are other approaches in which business teams own or contribute to the solution:

A Power BI dataset is a semantic data model composed of data source queries, relationships between dimensions and fact tables, and measurement calculations. The Power BI Desktop application can be used to create datasets as well as merely connect to existing datasets to author Power BI reports. The Power BI Desktop shares the same data retrieval and modeling engines as the latest version of SQL Server Analysis Services (SSAS) in tabular mode and Azure Analysis Services, Microsoft's enterprise BI modeling solution. Many BI/IT organizations utilize Analysis Services models as the primary data source for Power BI projects and it's possible to migrate Power BI Desktop files (.pbix) to Analysis Services models, as described in Chapter 13, Scaling with Premium and Analysis Services.

Self-service approaches can benefit both IT and business teams, as they can reduce IT resources, project timelines, and provide the business with a greater level of flexibility as their analytical needs change. Additionally, Power BI projects can be migrated across deployment modes over time as required skills and resources change. However, greater levels of self-service and shared ownership structures can also increase the risk of miscommunication and introduce issues of version control, quality, and consistency.

In the Self-Service Visualization approach, the dataset is created and maintained by the IT organization's BI team, but certain business users with Power BI Pro licenses create reports and dashboards for consumption by other users. In many scenarios, business analysts are already comfortable with authoring reports in Power BI Desktop (or, optionally, Excel) and can leverage their business knowledge to rapidly develop useful visualizations and insights. Given ownership of the dataset, the BI team can be confident that only curated data sources and standard metric definitions are used in reports and can ensure that the dataset remains available, performant, and updated, or refreshed as per business requirements.

In the Self-Service BI approach, the BI organization only contributes essential infrastructure and monitoring, such as the use of an On-premises data gateway and possibly Power Premium capacity to support the solution. Since the business team maintains control of both the dataset and the visualization layer, the business team has maximum flexibility to tailor its own solutions including data source retrieval, transformation, and modeling. This flexibility, however, can be negated by a lack of technical skills (for example, DAX measures) and a lack of technical knowledge such as the relationships between tables in a database. Additionally, business-controlled datasets can introduce version conflicts with corporate semantic models and generally lack the resilience, performance, and scalability of IT-owned datasets.

Larger organizations with experience of deploying and managing Power BI often utilize a mix of deployment modes depending on the needs of the project and available resources. For example, a Corporate BI solution with a set of standard IT developed reports and dashboards distributed via a Power BI app may be extended by assigning Power BI Pro licenses to certain business users who have experience or training in Power BI report design. These users could then leverage the existing data model and business definitions maintained by IT to create new reports and dashboards and distribute this content in a separate Power BI app to distinguish ownership.

Another common scenario is a proof-of-concept (POC) or small-scale self-service solution developed by a business user or a team to be transitioned to a formal, IT-owned, and managed solution. Power BI Desktop's rich graphical interfaces at each layer of the application (query editor, data model, and report canvas) make it possible and often easy for users to create useful models and reports with minimal experience and little to no code. It's much more difficult, of course, to deliver consistent insights across business functions (that is, finance, sales, and marketing) and at scale in a secure, governed environment. The IT organization can enhance the quality and analytical value of these assets as well as provide robust governance and administrative controls to ensure that the right data is being accessed by the right people.

The following list of fundamental questions will help guide a deployment mode decision:

1. Who will own the data model?
   • Experienced dataset designers and other IT professionals are usually required to support complex data transformations, analytical data modeling, large data sizes, and security rules, such as RLS roles, as described in Chapter 4, Developing DAX Measures and Security Roles
   • If the required data model is relatively small and simple, or if the requirements are unclear, the business team may be best positioned to create at least the initial iterations of the model
   • The data model could be created with Analysis Services or Power BI Desktop
2. Who will own the reports and dashboards?
   • Experienced Power BI report developers with an understanding of corporate standards and data visualization best practices can deliver a consistent user experience
   • Business users can be trained on report design and development practices and are well-positioned to manage the visualization layer, given their knowledge of business needs and questions
3. How will the Power BI content be managed and distributed?
   • A staged deployment across development, test, and production environments, as described in Chapter 8, Managing Application Workspaces and Content, helps to ensure that quality, validated content is published. This approach is generally exclusive to Corporate BI projects.
   • Sufficient Power BI Premium capacity is required to support distribution to Power BI Free users and either large datasets or demanding query workloads.
   • Self-Service BI content can be assigned to Premium Capacity, but organizations may wish to limit the scale or scope of these projects to ensure that provisioned capacity is being used efficiently.

A set of standard questions within a project template form can be used to initiate Power BI projects. Business guidance on these questions informs the BI team of the high-level technical needs of the project and helps to promote a productive project kickoff.

By reviewing the project template, the BI team can ask the project sponsor or relevant subject matter experts (SMEs) targeted questions to better understand the current state and the goals of the project.

The primary focus of the project-planning template and the overall project planning stage is on the data sources and the scale and structure of the Power BI dataset required. The project sponsor or business users may only have an idea of several reports, dashboards, or metrics needed but, as a Corporate BI project, it's essential to focus on where the project fits within an overall BI architecture and the long-term return on investment (ROI) of the solution. For example, BI teams would look to leverage any existing Power BI datasets or SSAS tabular models applicable to the project and would be sensitive to version-control issues.

The template is comprised of two tables. The first table answers the essential who and when questions so that the project can be added to the BI team's backlog. The BI team can use this information to plan their engagements across multiple ongoing and requested Power BI projects and to respond to project stakeholders, such as Vickie Jacobs, VP of Group Sales, in this example:

The following table is a list of questions that describe the project's requirements and scope. For example, the number of users that will be read-only consumers of Power BI reports and dashboards, and the number of self-service users that will need Power BI Pro licenses to create Power BI content will largely impact the total cost of the project.

Likewise, the amount of historical data to include in the dataset (2 years, 5 years?) can significantly impact performance scalability:

A business analyst inside the IT organization can partner with the business on completing the project ingestion template and review the current state to give greater context to the template. Prior to the project kickoff meeting, the business analyst can meet with the BI team members to review the template and any additional findings or considerations.