The World of Data Analytics: Power BI vs. GoodData
Written by Anja Duricic |
Every BI tool out there promises to deliver attractive dashboards and visualizations of your data. Some, like GoodData and Power BI, are on a broader mission to foster data-driven cultures and empower every end-user to make business-critical decisions based on data. However, not all data analytics solutions are created equal, and the same goes for GoodData and Power BI.
With seemingly minor differences, choosing the right data analytics solution from one of these two may seem a daunting task: A solution that covers all your needs, from deployment and data integration to front-end experience. You will need to keep in mind your present requirements as well as consult your long-term technical and business strategy to make a final decision; no mean feat.
With the above in mind, this series of articles aims to shed some light on the differences between GoodData and Power BI through the examination of some of the most often cited use cases by our customers. These include:
What Are GoodData and Power BI’s Approaches to Analytics?
Both GoodData and Power BI offer enterprise-grade, self-service data analytics designed to help you turn insights into actions. However, GoodData goes far beyond the simple premise of delivering analytics to everyone: taking the shape of a consumer-focused tool offering composable data and analytics consumable by anyone, anywhere. Moreover, your existing applications can be leveraged as building blocks for the creation of self-service data analytics that can connect data from any source, and be integrated with productivity applications. The API-first approach, data governance, SLAs, and common semantic layer — further discussed in the next blog post — are what ensure the delivery of consistent metrics, and enable the development of composable UI.
Unlike Power BI, a solution that requires reliance solely on its own ecosystem and consequently solves a smaller part of the data analytics problem (data visualization), GoodData’s architecturally agnostic approach ensures access to dynamic, real-time data in a solution that can scale efficiently. This capability in particular is what our customers often cite as one of the key requirements in their evaluation process, thus ensuring a 3rd party data analytics platform won’t hinder their growth.
Implementation: Important Considerations
When implementing a new solution, regardless of your use case, what you need to take into account first and foremost are deployment and data integration options. The market perennial Power BI, with its handful of different versions, such as Power BI Desktop and Power BI Pro, poses some significant limitations to large and complex use cases in this area, notably, its Microsoft-Azure-only optimization. With Power BI’s services and underlying databases favoring Azure, your technical teams will lack the option to leverage other public cloud providers which may be more aligned with your existing tech infrastructure. Moreover, if your data isn’t solely sitting in the cloud, you will also need to install Power BI On-Premise Data Gateways for the analytical app to run smoothly.
Apart from the cloud-based Azure service, Power BI also offers a desktop component, albeit unavailable for Mac computers, that is necessary for authoring and publishing the content to share it and make it available for consumption. Having to rely on a desktop app can often cause unnecessary complexity for end-users and unwanted burdens for administrators: with decreased flexibility and, as a result, increased total cost of solution ownership.
From experience, our customers at GoodData tell us that flexibility of deployment and data integration options form their top criteria when choosing a solution that will suit their long-term requirements. You can choose to deploy GoodData in the public cloud or on-premises with the ability to connect to numerous data sources; ranging from cloud data warehouses to storage buckets or messaging queues. The more data sources the tool supports the more inclusive it becomes, as it will break silos between teams who may be used to using different data sources that are most convenient to them. As such, they will get the opportunity to collaborate and contribute to the painting of a broader picture around the answers they seek in the data. Moreover, GoodData’s forward-thinking approach to data analytics ensures that the partnerships we establish with our customers prove valuable in the long term, and are aligned with both their technical and business strategies.
A Future-Proof Data Analytics Solution
None of this is to say that Power BI will fail to deliver its promises but rather points to the fact that it may not be the best solution for the use cases already mentioned. Alternatively, if you are someone who already uses Azure, Office 365, or Excel and requires analytics for your internal teams or a standalone BI department, Power BI may work just fine for you. On the contrary, customers looking for an embedded and distributed analytics solution, or those who wish to build a custom analytical app with benchmarking, often note that a solution best able to deliver to their expectations well into the future is the one that will:
- Be architecturally flexible and easily scalable
- Have strong versioning and governance capabilities
- Offer advanced embedding and white labeling options
- Be easy to use for both technical and business users.
Thus, the following articles in this series, covering embedded and distributed analytics and benchmarking-enabled custom analytical apps, will dive deeper into the selection criteria to be considered, and how GoodData and Power BI compare.
Ready To Try GoodData?
See for yourself how GoodData compares to Power BI and build a proof of concept with your own data sample. Start your free trial and begin building your first GoodData data insights, commitment-free. Or, schedule a demo with GoodData experts who will walk you through all of GoodData’s capabilities and answer any questions you may have.
Written by Anja Duricic |
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