SAC and Power BI – more than just front-end tools 

SAP and Microsoft are well established software providers with highly evolved and successful BI tools – SAP Analytics Cloud and Power BI, which cater to a wide audience including developers, business users and power users.

Even though, we start off by calling them front-end tools, they are more than just visualization platforms for creating and developing reports and dashboards.

Let us briefly explore the tools here and learn more about them side-by-side. How do they compare?

Basic introduction to the tools

SAC (SAP Analytics Cloud) is a SaaS (Software as a Service) cloud solution which is completely browser based and does not require any desktop application to be installed. This makes the usage less complex for the end users in comparison to other BI tools on the market, including Microsoft Power BI.

SAC has two major components:

  • Stories: catering to self-service reporting needs
  • Analytics Designer: for more complex scenarios and used by developers

It supports data ingestion and exploration, dashboard and report creation, predictive analytics and planning functionalities.

PBI (Microsoft Power BI) is also a SaaS solution from Microsoft which offers all the features such as data preparation, data discovery, visualization capabilities, predictive and advanced analytics. However, in contrast to SAC, Power BI does not offer much in terms of planning functionalities.
Power BI is both desktop (Power BI Desktop) and browser based (Power BI Services).

Power BI Desktop is a free and easy to install application which is embedded for all O365 users. Its adoptability is extremely high amongst all types of users due to the familiarity they feel in terms of user interface and tool navigation.

Since PBI is a part of a big ecosystem of Microsoft products like Azure Synapse Analytics, Azure Data Factory etc, the communication and interactivity with different Microsoft products is well supported.

A more detailed look at the tools

Data goes through various phases in an analytics lifecycle before we can draw value from it and get to a stage of decision making, namely:

  • Data Acquisition
  • Data Preparation and Modification
  • Data Visualization
  • Data Analysis
  • Collaboration

And in today’s scenario, accessibility of reports and dashboards on mobile devices is another important criterion for most organizations, when choosing a BI tool to help generate insights at any time and place.

How do the tools measure up?

We rated the tools based on certain key features the tools have or are missing in each category of the data analytics lifecycle. These features can help both the business and developers to make an informed decision when choosing a tool for their specific business intelligence needs.


Data Acquisition 

Data Preparation

Data Visualization

Data Analysis



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Check out our analytics lifecycle webinar series:

Webinar 1: A Datavard SWOT analysis between Microsoft Power BI and SAP Analytics Cloud

Webinar 2: Data connections & Acquisitions

Webinar 3: Data Modelling & Preparation

Webinar 4: Visualization

Webinar 5: Data Analysis

Webinar 6: Geo Maps

Webinar 7: Collaboration

Webinar 8: Mobile

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