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FarhanJeelani

The Power of No-Code/Low-Code Data Analysis on Embedded Report Development

The Power of No-Code/Low-Code Data Analysis on Embedded Report Development to create Data-Driven Decision-Making Culture

In today's world, data is the key to unlock limitless opportunities for organizations. For small and medium-sized organizations, analyzing data can be laborious, time-consuming, and expensive. Luckily, data analysis has been simplified with the advent of no-code and low-code data analysis tools which has given everyone, regardless of experience, the ability to peer into data and discover the power that insight can provide to the decision-making process. The democratization of data allows organizations to build a culture of data-driven decision-making which can help propel them to meet and surpass their goals.

 

No-Code or Low-Code Data Analysis

Prior to the advent of no-code and low-code applications, data analysis was handled by analysts with a specialized skillset that included the ability to code. With the rising importance of and reliance on data, it became apparent that the ability to analyze it should be shared more broadly than just the specialized few. This eventually led to the creation of no-code and low-code applications that allowed users of all experience levels to analyze data without the need for coding skills.

No-code applications enable users to build data analysis workflows through user-friendly graphical user interfaces (GUIs), whereas low-code data analysis applications require some coding knowledge, to allow users to perform advanced analysis tasks. Regardless of needing the ability to code or not, both no-code and low-code data analysis applications have lower learning curves, making them accessible to everyone.

LOW-CODE DEVELOPMENT

  • Helps digital product teams create apps using visual tools with no upfront coding needed.
  • Streamlines app development.
  • Frees up developers from the heavy coding needed for screen design, UX flows, theming/branding, and eliminates the need for manually coding in HTML and CSS.
  • Leads to cost reduction because apps can be built quickly in-house with tittle to no coding knowledge necessary.
  • Using the development method requires that developers finish an application before it can be deployed.

 

There are many different types of no-code and low-code data analysis tools available today. Any organization seeking to leverage data to build a data-driven decision-making culture should determine the applications that best fit organizational needs and the skills of those who will be using them. The most popular no-code data analysis application is Microsoft Power BI, which enable organizations to analyze their data without the need for coding skills. With either platform, users can create powerful visuals to monitor trends, integrate with third party apps, use natural language query tools, and receive real time insights.

 

 

 

Microsoft Power BI

Microsoft Power BI has a robust set of features that make it a powerful, yet easy to use data analysis application. Some of Power BI's features include automatically generated reports using a drag-and-drop dashboard builder, real time data modeling and analysis, customizable visualizations, and a self-serve analytics feature with cognitive insights. Additionally, Power BI integrates with Office 365 and offers cloud storage and the ability to share reports and dashboards as well as mobile app support for both iOS and Android devices. To ensure secure data accessibility, Microsoft Power BI offers advanced security features such as encryption of all stored data in Azure. All these features provide a seamless experience for creating interactive dashboards and sharing them with other team members and outside customers.

 

 

 

Overall, Microsoft Power BI offer powerful no-code solutions that allow businesses to access valuable insights quickly and easily while maintaining security standards throughout the process. Both platforms can be integrated into third party applications allowing teams to create more effective workflows within their organizations while keeping up with ever changing business needs.

 

The Advantages and Disadvantages of No-Code and Low-Code Data Analysis Tools:

 

Like all things, no-code and low-code data analysis tools have their advantages and disadvantages and it is important for organizations to weigh them before selecting a data analysis application. The following list of advantages/disadvantages are generalized across all applications, which means that individual applications may have some or all the advantages/disadvantages listed below.

 

Advantages of no-code and low-code applications include:

  • Cost effectiveness.
  • A simplified application to access and analyze data.
  • An intuitive user interface facilitating ease of learning and use.
  • Little to no coding skills required.
  • Automated alerts and notifications.Data access security

     

Disadvantages of no-code and low-code applications

  • Complex data analysis tasks could be hampered without coding
  • Lack of application flexibility/customizability
  • Potential for more errors because of reliance on pre-defined workflows
  • Potential need for some coding in low- code applications could limit usability
  • Lack of access to professional data analysts

 

 

Data is playing a larger role in decision-making, so it is important for organizations to consider the advantages and disadvantages of data analysis applications before making their selection. In the end, organizations that wish to be successful need to choose a data analysis application that can be used easily and effectively to ensure that they are able to gain insights into the wealth of data they already have and continue to collect.

 

Conclusion

For an organization to be successful in today’s fast-paced business environment, it needs to create a culture of data-driven decision-making—the impact of that culture will be felt across an organization’s operations and short- and long-term strategies. When employees can easily access data and easily gain insights from it, they are able to make more informed decisions, resulting in better outcomes for an organization. Data democratization can enhance organizations in many ways by increasing organizational transparency, supporting the creation of a culture of collaboration and innovation, and fostering an environment where decision-making is based on data.

 

No-code and low-code data analysis applications have democratized data allowing just about anyone within an organization to use data to improve decision-making without the need for a small pool of data experts. The use of these applications has had the added benefit of freeing up IT departments to be able to focus on broader technology initiatives and challenges rather than devoting time and resources to work on data analysis requests from around the organization.

 

No-code and low-code data analysis applications lower costs, support collaboration, assist an organization in making informed decisions, democratize the access to data, and drive overall change within an organization. The advantages of these tools far exceed the disadvantages, and organizations that adopt them are pursuing opportunities without incurring a massive cost of entry.

 

 

Project Overview:

This project aims to deliver a comprehensive Business Intelligence (BI) solution for a customer outside the organization. The primary challenge is to create reports & dashboards while ensuring data security through Row-Level Security (RLS), all without requiring the customer to have Power BI Pro licenses. To achieve this, the customer will sign in using the organization's platform credentials, and these credentials will be used for RLS and viewing the reports & dashboards.

 

Project Objectives:

  • Dashboard and Report Development: Creation of interactive, visually compelling Power BI reports & dashboards, tailored to the customer's data needs.

 

  • Row-Level Security (RLS): Implement robust RLS mechanisms to ensure that users only access data pertinent to their role or responsibilities.

 

  • Single Sign-On (SSO): Enable SSO for the customer, allowing them to use their organization platform credentials to access Power BI and dashboards seamlessly.

 

  • Licensing Optimization: Minimize the requirement for Power BI Pro licenses for the customer, allowing cost-effective access.

 

Key Project Phases:

Requirement Gathering:

Collaborate closely with the customer to understand their specific data requirements and user roles.

Dashboard Design:

Design reports &  dashboards to meet the customer's needs, focusing on data visualization and user-friendliness.

Data Preparation:

Ensure data sources are clean, secure, and ready for integration into Power BI.

Row-Level Security (RLS) Implementation:

Create and configure RLS rules to limit data access based on user roles.

Ensure that RLS is compatible with the single sign-on approach.

Single Sign-On (SSO) Setup:

Integrate the organization's authentication system with Power BI to allow SSO for the customer.

Ensure that SSO credentials work seamlessly for both authentication and RLS.

 

Dashboard Development:

Build the dashboards using Power BI, integrating them with RLS and SSO functionalities.

Testing and Quality Assurance:

Thoroughly test the dashboards, RLS, and SSO integration to ensure seamless functionality.

Address any bugs or issues.

 

Deployment and User Training:

Deploy the dashboards and SSO system for the customer.

Provide training and documentation to help users navigate the new system.

Monitoring and Support:

Implement monitoring tools (Microsoft Fabric Monitoring Hub) to ensure the continuous performance and security of the system.

Provide ongoing support to address any user concerns or system maintenance.

Benefits and Outcomes:

The customer can access reports & dashboards tailored to their needs without the need for Power BI Pro licenses, resulting in cost savings.

Data security is enhanced through RLS, ensuring that each user only sees relevant information.

The integration of SSO simplifies user access and improves the overall user experience.

 

Implementation:

Power BI Desktop App can be installed via the Microsoft Store and allows Developers to edit BI Reports and publish to the Test Workspace.

FarhanJeelani_0-1737188191815.png

Create two Premium capacity workspaces :

  • Test - Created with A1 SKU to create the Power BI Deployment Pipeline and test environment. This capacity can be used for DEV/TEST purpose.
  • Staging – Created with A4 SKU (Based upon the usage).   This is the “PROD” workspace and at this stage has the name “Staging” but name can be changed to PROD with no impact apart from the name.

 

Reports will need to be published to Staging/PRODR using the Power BI service by logging in at https://app.powerbi.com/ .

Each Report is stored in a single PBIX file. Each PBIX file contains everything needed to edit, configure, and publish a Report.

 

Power BI Gateway

The following explains how to add data sources to the Gateway.

  1. Login to Power BI Service with Valid Credentials and click on three dots on top left corner the Go to Settings -> Manage Gateways.

 

  1. To remove the Data Source, select the Data Source you want to remove and click on Remove button.  To schedule/refresh reports after removing the source from gateway, you will need to add the source again and assign gateway to each report to enable refresh. You can click on the Check Status icon, to check whether the data source in reachable or not.  (Currently unreachable)

 

  1. To add new source to the gateway, click on the new icon on left corner.

 

 

Embedded Reports overview

Microsoft Power BI Embedded allows embedding of Power BI content such as reports and dashboards into organization web & mobile applications. The below is the high-level context diagram of Power BI Embedded for Organization.

FarhanJeelani_2-1737188424786.png

  1. Power BI Desktop is used for authoring the reports, Power BI Desktop can access Application Database Server using on-prem gateway.
  2. MS on-prem gateway software is installed on a Virtual Machine that is in the close proximity to Data Source, the gateway will use outbound connection to communicate to Azure and Power BI Service.
  3. The reports can be published into Workspaces in Power BI Service
  4. The reports & dashboards that are published are in a Workspace.  The Workspace acts as a container where team members can create reports and dashboards and collaborate with other team members.
  5. As per the current state all the organization users’ logon to application using credentials managed by organization application and access control to different reports subjected to user role will stay as same within organization application.
  6. Power BI reports are embedded into organization applications using Azure AD Service Principals. The Service Principals are granted Admin access to specific workspaces. The Row Level Security based on user Identity is implemented at Power BI Service.

 

Key Design Decisions

Below are some key solution decisions made for Embedded Power BI Content to organization Application.

Authentication Method:  Service Principal Authentication method is used and is the recommended method of Authentication for “Embed for your customer scenario”, as Company (outside customers) end users will not sign in to Power BI or hold a Power BI licence.

Security: Azure AD App Service Principal object is used to authenticate Organization Web Application against Organization Power BI Service.   With Service Principal we can use Application Secret or Certificate based Authentication.  Service Principle with Secret is the method implemented.

Granting Access: A Security Group has been created with a Service Principal added as a member. The Security group has been granted access to Individual Workspaces in Power BI Service.

 

The Embedded POC with Row Level Security

The following diagram explains the Row Level Security:

FarhanJeelani_3-1737188457784.png

Please check Part 2 of this blog