PL 600 Module 6

Major Categories of Reporting and Analytics

Operational reporting – Data comes from Microsoft Dataverse directly, viewed and
interacted with in the context of the Power App
Self-service BI – Data is exported from Microsoft Dataverse or could be refreshed on a
schedule
Enterprise BI – Data is extracted for use in broader enterprise reporting tools, this
could be done to allow integration of data from other sources

1. Operational Reporting:
 Tools & Examples: Views, Charts, and Dashboards.
 Use Case: This is used for day-to-day management and tracking of
operations. It involves real-time reporting and is commonly used to
monitor ongoing processes and immediate business conditions.
2. Self-service BI (Business Intelligence):
  Tools & Examples: Manual Export to Excel, Power BI Service, Refresh
from Dataverse.
 Use Case: Enables end users to create their own reports and dashboards
without needing the intervention of IT or data teams. It's ideal for dynamic
environments where users need to explore data and create custom
reports on the fly.
3. Enterprise BI:
 Tools & Examples: Web APIs for ETL (Extract, Transform, Load),
Microsoft Dataverse to ADLS (Azure Data Lake Storage).
 Use Case: Used in scenarios requiring robust data integration, large-scale
data storage, and advanced analytics capabilities. It supports
comprehensive business intelligence efforts that require heavy data
processing and integration across various data sources.
When to Use Each Category
 Operational Reporting: Use when you need to keep a constant eye on business
processes and operational performance. This is crucial for roles like operations
managers who need up-to-date information to make immediate decisions.
 Self-service BI: Ideal for empowering non-technical users to analyze data and
make informed decisions without relying on specialized IT resources. This fosters
a culture of data-driven decision-making throughout the organization.
 Enterprise BI: Best suited for organizations needing deep insights from large
datasets that involve complex transformations and integrations. This is essential
for strategic decisions that affect the entire business trajectory, involving
stakeholders like data scientists and strategic planners.
Each category addresses different needs within an organization, from operational
monitoring to strategic decision-making, ensuring that users at all levels can access the
data they need in a form that is useful to them.

What should you look for in requirements as you evaluate how to implement the report
or visualization?

When evaluating how to implement a report or visualization, there are several key
aspects to consider within the requirements:
Data Considerations:
  Data Source: Identify the source of the data for your report. Is it stored in a
database (like SQL Server, Dataverse), a cloud storage service (like Azure Blob
Storage), or somewhere else? Understanding the data source will influence the
tools and techniques you can use.
 Data Volume and Format: Consider the size and complexity of the data. Is it a
small dataset or a large volume of data? Is it structured data (tables with rows
and columns) or unstructured data (text files, images)? These factors will impact
the performance and scalability of your report.
 Data Refresh Frequency: How often does the data need to be refreshed? Daily,
weekly, or real-time? This will determine how you schedule and automate report
updates.
 Data Security: Are there any security considerations for the data? Does it
contain sensitive information that requires access control or encryption?
Report and Visualization Needs:
 Target Audience: Who is the intended audience for this report? This will
influence the level of detail, complexity, and overall design of the report.
 Report Goals: What are the key questions or insights the report aims to answer?
What story are you trying to tell with the data?
 Visualization Types: What types of visualizations are best suited to convey the
information effectively? Bar charts, line graphs, pie charts, or more complex
visualizations like maps or heatmaps?
 Interactivity: Do users need to interact with the report (e.g., drill down into
specific data points, filter the data)?
Technical Considerations:
 Existing Tools and Skills: What reporting and visualization tools are already
available within your organization? Do you have the necessary expertise to use
them effectively?
 Integration Needs: Does the report need to integrate with other systems or
applications?
 Deployment and Sharing: How will the report be deployed and shared with
users? Will it be a web-based report, a downloadable file, or embedded within
another application?
Additional Considerations:
 Accessibility: Ensure your report is accessible to users with disabilities,
following WCAG guidelines.
  Performance: The report should load and render quickly, especially when
dealing with large datasets.
 Maintainability: Consider how easy it will be to maintain and update the report
over time, as data sources or requirements evolve.
By thoroughly examining these aspects of the requirements, you can make an informed
decision about the best approach for implementing your report or visualization. This will
ensure it effectively meets the needs of your users and delivers valuable insights from
your data.

Operational Reporting on the Power Platform

Operational reporting within the Microsoft Power Platform typically involves generating
reports that provide insights into the daily operations of a business. These reports are
crucial for managers and operational staff who need real-time data to make quick
decisions. On the Power Platform, operational reporting can leverage tools like Power
BI, dashboards, and charts directly within Dynamics 365 or other integrated Microsoft
services.
Considerations for Viability of Built-in Platform Reporting
When evaluating whether to use built-in platform options for reporting within the Power
Platform, you should consider several key factors:
1. Ease of Access: Built-in tools are accessible directly within the app, providing a
seamless experience for users who are already working within the platform.
2. Skills Required: These tools usually require standard skills that most end-users
can easily acquire, which lowers the barrier to entry compared to more complex
BI tools.
3. Data Currency and Security: Built-in options ensure that the data is always
current and adhere to the platform's security model, which is critical for
maintaining data integrity and compliance.
4. Specific Needs and Limitations: While built-in tools are convenient, they might
offer only simple visualizations and standard filtering options. They may also lack
capabilities to handle historical data analysis.
Strategy for Utilizing Reporting Tools
Depending on the reporting needs and the audience, you can choose a strategic
approach to utilize the built-in and alternative reporting tools:
1. Ad-hoc Reporting: For ad-hoc needs, where users require quick and occasional
insights, using a combination of 'Advanced Find' and 'Immersive Excel' can be
effective. Advanced Find allows users to search and filter data extensively, while
Immersive Excel lets them manipulate and analyze these data sets within a
familiar tool.
2. Regular Users: For everyday users who need periodic updates and overviews,
leveraging out-of-the-box dashboards and charts is practical. These provide
summarized data insights for regular monitoring and decision-making without
requiring complex setup.
3. Exportable Reports: When reports need to be printed or exported, creating
Word and Excel templates is advisable. These templates can be pre-formatted to
align with business reporting standards, making it straightforward for users to
generate consistent documents and spreadsheets.
Practical Application
In practice, choosing the right tool from the Power Platform’s suite depends heavily on
the specific business context and the user's role. For instance, operational managers
may rely heavily on real-time dashboards for daily metrics, while analysts might use
Power BI for deeper data exploration and historical analysis. Meanwhile, sales teams
might prefer simple Excel and Word templates for reports they need to share externally.
By understanding each tool’s capabilities and limitations, you can tailor the reporting
strategy to meet both the immediate and strategic needs of your organization, ensuring
that all stakeholders have access to the right information at the right time.

What should you consider when trying to understand available data for Power BI
visualizations?

When preparing to develop Power BI visualizations, understanding the available data is

critical. Here’s a detailed look at the considerations you need to keep in mind:
Source and Reference Data
First, ascertain whether all your data is coming from Microsoft Dataverse or if there are
other sources involved. Dataverse typically integrates well with Power BI, providing a
streamlined experience. However, if you're incorporating data from other sources, you
might face challenges like varying data formats or integration issues. It's also beneficial
to identify any reference data that could enrich your analysis, such as external
benchmarks or industry standards, which can provide context for your visualizations.
Data Volume and Refresh Rate
Understanding the volume of data is crucial. Large data volumes can impact
performance, requiring strategies such as aggregating data at the source, using
dataflows, or optimizing your Power BI model for performance. The frequency of data
refresh is another critical factor. Real-time data needs can dictate the architecture,
potentially necessitating direct queries to the source rather than relying on data imports.
For datasets that require frequent updates, ensure your infrastructure supports the
necessary refresh rates without degradation in performance.
Data Relationships and Key Fields
Knowing how data is related helps in modeling it effectively in Power BI. Identify key
fields that connect different tables or datasets. These fields are essential for creating
relationships in your Power BI model. You should also pinpoint which fields are key
indicators (KPIs) that stakeholders track. This helps in prioritizing these fields in your
reports and dashboards.
Data Sparsity and Exclusion
Evaluate how sparse the key data fields are. Sparsity refers to the amount of missing or
null values in your data. High sparsity might require data cleaning or imputation
techniques to ensure accurate analysis. Decide also what data can be left behind. Not
all data pulled into Power BI is necessary for your visualizations, and excluding
irrelevant data can improve both performance and clarity in reports.
Gotchas and Caveats
1. Performance vs. Complexity: More complex visualizations can be resource-
intensive and slow down report performance. Keep visualizations as simple as
possible without compromising on the needed insights.
2. Security and Compliance: When pulling data from various sources, ensure
compliance with data security and privacy regulations. Power BI has features like
row-level security, but these need to be configured properly to safeguard
sensitive information.
3. Data Freshness: Depending on the source, there can be a lag between data
creation and availability in Power BI. This lag can lead to decisions made on
outdated information if not properly managed.
Additional Considerations
When designing your Power BI solution, consider the end-user experience. Reports
should not only be accurate but also intuitive and easy to use. User training on how to
use Power BI effectively can dramatically enhance the value gained from your BI
implementation.
In summary, understanding your data thoroughly, considering integration points,
performance implications, and user needs will help you build effective and efficient
Power BI visualizations. This preparation ensures that your reports are both valuable
and performant, driving better business decisions.
Working with Microsoft Dataverse Data
When working with data from Microsoft Dataverse, it's essential to establish a robust
connection and effectively manage data transformation for optimal use in Power BI. The
connection to Dataverse can be made using either the legacy OData protocol or the
more recent TDS (Tabular Data Stream) protocol, which supports DirectQuery.
DirectQuery is particularly useful because it allows Power BI reports to query the data
directly in Dataverse in real-time, without needing to store a copy of the data in Power
BI. This means that your reports are always up-to-date with the latest data from the
source.
Once the connection is established, the next critical step is cleaning and transforming
the data, which is fundamental to effective data modeling. This involves selecting the
appropriate tables and columns needed for your analysis and ensuring that fields are
set to the correct data type to avoid data type mismatches that could lead to errors in
reporting. Filtering the data to include only the relevant records and setting up the table
structures and relationships accurately are also crucial steps. Employing DAX (Data
Analysis Expressions) is essential for creating measures and calculated columns that
can add valuable insights to your reports.
The necessity for data cleanup and transformation stems from the need to tailor the raw
data from Dataverse to meet specific reporting requirements. Clean and well-modeled
data ensures high performance of your Power BI reports, improves user experience by
providing data in a useful and understandable format, and prevents errors in data
analysis.
Working with Dates in Power BI
Handling dates in Power BI, especially when dealing with data sourced from Microsoft
Dataverse, requires careful consideration since all Dataverse date and time fields are
stored in Coordinated Universal Time (UTC). This can lead to discrepancies if not
properly managed, particularly for users in different time zones.
For standard use, you might opt to use the dates as they are, directly from Dataverse.
However, for more advanced analysis, it is advisable to create a dedicated calendar
table in Power BI. This table can then be linked to your date fields, which offers several
advantages. First, it allows for more sophisticated date-based slicing and sorting, such
as grouping data by financial quarters or specific date ranges that are not immediately
available from the raw data. Second, a calendar table enables the use of more complex
DAX date functions, like calculating Year-To-Date (YTD) or Month-To-Date (MTD)
values, which are essential for time series analysis and comparing periods.
Creating a calendar table is straightforward in Power BI but requires maintenance to
ensure it covers all the dates in your data. This setup not only improves the accuracy of
time-based calculations but also enhances the overall flexibility of your reporting,
allowing for dynamic time comparisons and trend analysis.
By effectively managing the connection to Microsoft Dataverse and skillfully handling
date information in Power BI, you can build powerful, accurate, and timely business
intelligence solutions. These steps, while sometimes complex, are crucial in leveraging
the full potential of Power BI to provide actionable insights and drive business decisions.
Security Considerations in Power BI with Microsoft Dataverse
Security within Power BI, particularly when integrated with Microsoft Dataverse, is
multifaceted, involving both the security model and data sharing protocols.
Security Model:
Power BI has its own security model that operates independently of Microsoft Dataverse
when data is imported. This means that during an import, the security roles and
hierarchy defined in Dataverse do not apply. Instead, Power BI handles security
internally. However, when using the Tabular Data Stream (TDS) protocol or DirectQuery,
Dataverse's security roles are respected. This is crucial because it ensures that users
see only the data they are permitted to access according to their roles within Dataverse.
This integration maintains data governance and compliance across platforms.
Sharing Data:
Sharing dashboards and reports within Power BI that source data from Microsoft
Dataverse requires careful handling to maintain security and data integrity. Each
dashboard must be manually shared within Power BI, and the data within these
dashboards refreshes based on the permissions of the report owner, not the viewer.
This means that data visibility can vary unless proper roles and permissions are
configured. Additionally, sharing Power BI files (PBIX) that connect to Dataverse
requires users to re-authenticate to ensure security. Power BI components are designed
to be aware of the Dataverse solution, which simplifies integration and maintenance.
Importantly, no additional privileges are required beyond those necessary for access to
the respective platforms.
Understanding Row Level Security (RLS) in Power BI
Row Level Security (RLS) is a powerful feature in Power BI that allows for fine-grained
access control to data within reports. RLS works by applying DAX formulas to filter data
at the row level based on the user accessing the report. This means different users can
access the same report but will see only the data they are authorized to view based on
the RLS policies.
Implementation of RLS:
Setting up RLS involves creating roles within the Power BI service and defining DAX
expressions that specify which data rows users in these roles can access. For example,
an RLS role might restrict a regional manager to seeing only data from their region.
Importantly, RLS rules are not applied to the report owner or individuals with
administrative privileges in the workspace, ensuring that these users can always access
complete data sets for management and debugging purposes.
Practical Implications:
Implementing RLS is essential for organizations that need to maintain strict data
security standards and compliance, especially when dealing with sensitive or
confidential information. It allows businesses to leverage the full power of Power BI for
organization-wide analytics without compromising individual data security. However, it
requires careful setup to ensure that the DAX expressions accurately reflect the
intended security policies and do not inadvertently expose sensitive data.
In summary, understanding and implementing security and RLS in Power BI, especially
in conjunction with Microsoft Dataverse, are critical for maintaining data integrity,
compliance, and ensuring that sensitive information is appropriately shielded while still
enabling powerful data analytics capabilities.

ecurity Scenario Discussion in Power BI

The provided scenario outlines a typical hierarchical data access requirement found in
many organizations, particularly in sales. It highlights the need for differentiated access
levels across various roles within the organization, from salespeople to executives. Let’s
break down the scenario, assess its effectiveness, and discuss potential enhancements.
Scenario Analysis
1. Salespeople: Restricted to accessing only their data. This limitation ensures
privacy and security, preventing unauthorized access to sensitive data of other
salespeople.
2. Sales Managers: Access is limited to their team's data. This is suitable for
overseeing team performance and conducting comparative analysis without
overstepping privacy bounds.
3. Executives: Given access to all data, which is necessary for strategic planning
and organization-wide decision-making.
Implementation Using Row Level Security (RLS)
RLS in Power BI can effectively enforce these security rules. By defining security roles
within Power BI and using DAX formulas to filter data access based on the user's role,
each category of employees can be shown only the data relevant to them. This method
is efficient but relies heavily on the correct setup of roles and DAX expressions to
prevent data leaks.
Potential Improvements and Considerations
 Dynamic Data Masking: Besides RLS, considering dynamic data masking could
add a layer of security, especially for sensitive information that does not prevent
its display but needs to be obscured.
 Audit Trails: Implementing audit trails can help monitor and analyze access
patterns, potentially flagging unauthorized attempts to access restricted data.
 Data Minimization: Avoiding the inclusion of sensitive fields unless absolutely
necessary. For instance, consider whether salespeople need to see customer
contact details or just transactional data.
Additional Security Settings/Options
 Pivot Data in Query: This involves aggregating data at the source, thereby
limiting the detail and focusing on summarized insights that are less sensitive.
 Limit Export Options: Restricting the ability to export data from Power BI
reports can prevent data from being moved out of the secure BI environment,
reducing the risk of data leakage.
Case Study: Healthcare Organization Implementing RLS
Background
A healthcare provider wanted to ensure that patient data was securely handled within
their reporting solutions. They needed to comply with HIPAA regulations while allowing
different levels of staff access to patient data based on their roles.
Implementation
 Row Level Security: RLS was implemented where doctors could only see data
related to their patients, department heads could access data for all patients
within their department, and administrative staff could only access non-sensitive
patient data for logistical purposes.
 Data Masking: Sensitive information, such as Social Security Numbers (SSNs),
was dynamically masked to further secure patient confidentiality.
 No Export: The export of data was disabled for all users to maintain data within
the controlled environment of Power BI.
Outcomes
This setup not only complied with HIPAA regulations but also ensured that the staff
could perform their roles effectively without unnecessary exposure to sensitive data.
The healthcare provider was able to improve their data-driven decision-making while
enhancing data security.
This case study reflects the potential of RLS and other security features in Power BI to
create a robust, secure BI environment tailored to the specific needs of an organization,
much like the sales organization scenario provided. By focusing on both access control
and data protection, organizations can safeguard sensitive information while still
leveraging powerful data analytics capabilities.

Power BI Data Alerts let you set up automatic notifications based on specific conditions
in your reports and dashboards. Here's a breakdown:
 Triggers: You define a trigger based on data values in your Power BI visuals
(charts). These visuals can be Single Number Tiles, KPI Tiles, or Gauge Tiles.
 Conditions: You specify the condition that triggers the alert. For example, the
"Estimated Revenue" in an "Opportunities" chart must be greater than $10
million.
 Notifications: When the condition is met, you can choose to receive notifications
in two ways:
 o In Power BI Service: A pop-up appears within the Power BI service itself,
alerting you on your computer.
o Email: An email notification is sent to your preferred email address.
Key Points:
 Real-time: Alerts trigger whenever the condition is met, keeping you updated in
real-time.
 Power Automate Integration: These alerts can be integrated with Power
Automate, a workflow automation tool. This allows you to take further actions
when an alert is triggered, like sending automated messages or updating other
systems.
 Availability: Data alerts can be set up from both Power BI dashboards and the
Power BI mobile app.
In simpler terms:
Data alerts act like automated "data watchers" in your Power BI reports. You tell them
what to watch (specific values in charts) and what to do when something significant
happens (send notifications). This way, you're alerted immediately when important
events occur in your data, allowing you to react quickly and take necessary actions.

Power BI ALM considerations

Let's break down the concept of Power BI Application Lifecycle Management (ALM) and
how it relates to the recent addition of Power BI resources as solution components:
ALM (Application Lifecycle Management):
ALM refers to the practices and processes involved in managing the entire lifecycle of
an application, from its initial conception and development to deployment, maintenance,
and eventual retirement. It ensures a smooth and controlled flow for your applications,
minimizing errors and promoting efficiency.
Power BI Resources:
These are the building blocks of your Power BI reports and dashboards. They include:
 Datasets: The underlying data models containing the information displayed in
your reports.
  Reports: Visual representations of your data using charts, graphs, and other
elements.
 Dashboards: Collections of reports and other visualizations presenting a
comprehensive overview of your data.
Previously:
 Power BI resources were primarily created and managed within Power BI
workspaces.
 These workspaces weren't directly integrated with Microsoft Power Platform
environments, which include tools like Power Apps, Power Automate, and
Dataverse.
Recent Change:
 Now, Power BI resources can be added as solution components. This means
they can be included within solutions developed in the Microsoft Power Platform
environment.
What this means:
This change allows for a more holistic approach to managing Power BI resources within
the larger context of your application development process. Here are some potential
benefits:
 Improved Version Control: Track changes made to Power BI resources
alongside other application components within the solution.
 Streamlined Deployment: Deploy Power BI resources along with other Power
Platform components when releasing new versions of your application.
 Enhanced Governance: Establish consistent policies and controls for managing
Power BI resources within the same framework as other application elements.
However, it's important to note:
 Power BI workspaces remain the primary location for creating and editing Power
BI resources.
 You can't directly develop Power BI reports or dashboards within the Microsoft
Power Platform environment.
In summary:
The ability to add Power BI resources as solution components bridges the gap between
Power BI and the broader Power Platform ecosystem. It enables a more integrated
approach to application development and management, potentially leading to improved
efficiency, consistency, and governance for your Power BI resources
Enterprise BI

Enterprise BI (Business Intelligence):

Enterprise BI refers to a comprehensive strategy and set of tools that organizations use
to collect, analyze, and transform data into actionable insights. The goal of Enterprise BI
is to empower businesses to make data-driven decisions that improve performance and
achieve strategic objectives.
Here are some key aspects of Enterprise BI:
 Data Integration: Consolidating data from various sources like databases,
applications, and external feeds into a central repository.
 Data Warehousing: Storing and managing large volumes of historical data for
analysis.
 Data Analytics: Leveraging tools and techniques to analyze data, identify
patterns, and uncover trends.
 Data Visualization: Presenting insights in a clear and compelling way using
charts, graphs, and dashboards.
 Reporting: Creating reports to communicate key findings and metrics to
stakeholders.
Benefits of Enterprise BI:
 Improved Decision Making: Data-driven insights can inform strategic decisions,
optimize operations, and identify new market opportunities.
 Increased Efficiency: Automated data analysis and reporting can save time and
resources compared to manual processes.
 Enhanced Collaboration: Shared access to data and insights fosters better
communication and collaboration across departments.
 Reduced Costs: Data-driven insights can help identify areas for cost savings
and improve resource allocation.
Dataflows in Power BI:
Dataflows are a crucial component of Power BI and play a vital role in the Enterprise BI
process. They are essentially cloud-based Extract, Transform, Load (ETL) services.
Here's how they work:
 Extract: Dataflows connect to various data sources (databases, files, web
services) and extract the raw data.
  Transform: Dataflows allow you to clean, filter, and shape the data to prepare it
for analysis.
o This might involve removing duplicates, formatting data types, or applying
calculations.
 Load: The transformed data is then loaded into a data model within Power BI,
which is optimized for analysis and visualization.
Dataflow Capabilities in Power BI:
 Support for Various Data Sources: Dataflows can connect to a wide range of
data sources, both on-premises and in the cloud.
 Data Transformation Capabilities: Dataflows offer a variety of data
transformation functionalities, allowing you to prepare your data for analysis
effectively.
 Scheduling and Automation: You can schedule dataflows to run periodically,
ensuring your data model stays up-to-date with the latest information.
 Incremental Refresh: Dataflows can perform incremental refreshes, which only
update the portions of the data that have changed, optimizing performance.
 Data Lineage Tracking: Dataflows provide data lineage tracking, allowing you to
see the origin and transformation steps of your data, enhancing data quality and
trust.
Overall, dataflows in Power BI are powerful tools that simplify and automate the
data preparation process, making Enterprise BI initiatives more efficient and
successful.

Dataset vs. Dataflow and Azure Synapse Link: Understanding the Data Pipeline
These three concepts all play a crucial role in working with data, but they serve different
purposes within the data pipeline:
1. Dataset:
 Think of a dataset as a named view of your data residing in a specific data
source.
 It acts like a pointer, referencing the location and structure of your data without
actually storing the data itself.
  Datasets are often used in conjunction with data transformation or analysis tools.
 For example, in Power BI, you can create a dataset that points to a table in a
database or a file in Azure Blob Storage.
Key Points:
 Points to data location: Defines where the data resides.
 Doesn't store data: Doesn't hold the actual data itself.
 Used for referencing: Used by tools to access the data.
2. Dataflow:
 A dataflow is a data transformation service. It's a process that takes data from
various sources, transforms it according to your specifications, and prepares it for
further analysis.
 Dataflows are often cloud-based (like Power BI dataflows) and offer
functionalities like:
o Extracting data: Connecting to different data sources and fetching the
raw data.
o Transforming data: Cleaning, filtering, shaping, and manipulating the
data to meet your needs.
o Loading data: Delivering the transformed data to a destination, such as a
data warehouse or an analytical model.
 They essentially act as an ETL (Extract, Transform, Load) pipeline, preparing
your data for analysis.
Key Points:
 Transforms data: Cleans, filters, and prepares data.
 Connects to sources: Fetches data from various locations.
 Delivers to destination: Loads transformed data for analysis.
3. Azure Synapse Link:
 Azure Synapse Link is a cloud-based analytical service that provides a unified
environment for data warehousing, data lakes, and data integration.
 It acts as a central hub where you can store, manage, and analyze your data
from various sources.
 Here's how Azure Synapse Link comes into play:
 o You can connect data sources to Azure Synapse Link using tools like
dataflows.
o Dataflows can then transform and prepare your data within the Synapse
workspace.
o You can store the transformed data in a data warehouse within Synapse
Link for historical analysis.
o Azure Synapse Link also offers data exploration and visualization
capabilities.
Key Points:
 Unified environment: Combines data warehousing, data lakes, and data
integration.
 Central data hub: Stores and manages data from various sources.
 Connects with dataflows: Uses dataflows for data transformation.
In simpler terms:
 Dataset: Imagine a dataset as a library card that tells you where a specific book
(your data) is located.
 Dataflow: Think of a dataflow as a process for cleaning, organizing, and
preparing a book (your data) for reading (analysis).
 Azure Synapse Link: Consider Azure Synapse Link as a large library (data
warehouse) with tools to connect, organize, and analyze all your books (data) in
one place.
By understanding the distinct roles of datasets, dataflows, and Azure Synapse Link, you
can effectively build a data pipeline that efficiently retrieves, transforms, and prepares
your data for analysis, ultimately leading to valuable insights.

Power Platform Center of Excellence

The Microsoft Power Platform Center of Excellence (CoE) Starter Kit is a free resource
pack designed to help you jumpstart your organization's journey towards effective
Power Platform adoption and governance. It provides a collection of tools, templates,
and best practices to establish a strong CoE foundation.
What's Included in the Starter Kit:
  Templates: The kit provides pre-built templates for:
o Governance policies and procedures
o Communication plans
o Training materials
o Application inventory and approval workflows
 Data Model: A sample data model built on Microsoft Dataverse helps you track
and manage resources within your Power Platform environment.
 Power BI Reports: Pre-configured Power BI reports offer insights into your
Power Platform usage, including app inventory, user activity, and resource health.
 Documentation and Guidance: The kit includes comprehensive documentation
and step-by-step guidance to help you deploy and configure the resources
effectively.
Benefits of using the Starter Kit:
 Faster Setup: The pre-built templates save you time and effort in developing
essential CoE components from scratch.
 Best Practice Implementation: Leverage Microsoft-recommended practices for
governance, training, and application lifecycle management.
 Data-Driven Insights: Gain valuable insights into your Power Platform adoption
and usage through pre-built Power BI reports.
 Reduced Risk: Standardized policies and procedures help minimize risks
associated with uncontrolled application development.
How it Works:
1. Download the Starter Kit: The kit is readily available for download from the
Microsoft Power Platform documentation website https://learn.microsoft.com/en-
us/power-platform/guidance/coe/starter-kit.
2. Deploy the Solution: Import the provided solution package into your Power
Platform environment (likely using Power Apps or Microsoft Dataverse).
3. Configure Settings: Follow the included documentation to customize settings
and tailor the templates to your organization's specific needs.
4. Start Using the Resources: Utilize the pre-built reports, policies, and
communication plans to manage and promote Power Platform adoption within
your organization.
Important Considerations:
  Customization: While the kit provides a solid foundation, you may need to
customize some templates and reports to fully align with your organization's
structure and requirements.
 Ongoing Maintenance: The CoE itself requires ongoing management and
updates as your Power Platform usage evolves.
 Technical Expertise: Some level of technical expertise within your team may be
necessary for deployment and configuration.
Overall, the Microsoft Power Platform CoE Starter Kit is a valuable resource that
streamlines the setup process for your organization's CoE. It provides a strong
starting point with best practices and tools to ensure effective governance,
promote user adoption, and ultimately maximize the value you get from Microsoft
Power Platform.