Google Data Studio Pros and Cons: A Detailed Analysis




Data Studio is a business intelligence tool from Google provided as a completely managed web service. Data Studio provides an intuitive interface to explore and build insights using data. Data can also be molded in dashboards and reports.  It comes free of cost along with the Google cloud account and is a critical element of Google Analytics suite.

Data Studio also provides scheduling capabilities to manage data freshness. It is built on top of the Google app ecosystem and offers tight integrations to Google based data sources like BigQuery, Google Analytics, Google Sheets, etc.

Apart from the Google-based data sources, it also supports a long list of on-premise and cloud-based data sources.

This blog post is intended to be a complete analysis of some of the most important Google Data Studio pros and cons that you need to know. Here is an outline of what you will cover in this blog:

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Introducing Google Data Studio

A critical element of evaluating the effectiveness of a business intelligence tool is the extent of data sources and connector support. Before going into the pros and cons, let us spend some time on types of connectors and data sources supported by Data Studio. 

Connectors are tailored to a specific cloud service or database. Some of the connectors pull in all the fields from the connected data sources. Others pull only a specific set of fields. Google Analytics, GoogleAds, etc are examples for such connectors. Connectors could either be built-in ones provided by Google, partner implemented ones or community provided ones. Connectors need to be authorized by the users to access data. Once a connector is initialized and authorized, Data Studio considers it as a data source.

Data sources can be based on live connections or scheduled extracts. The difference is that reports and dashboards based on live connected data sources are refreshed every time they are accessed. Obviously, this comes with a performance impact and it can take ages to load, transform, and render a report in such cases. This is solved to some extent by using data extracts. Extracts are snapshots of data stored in Google cloud storage. Extracts can be scheduled to provide reasonable data freshness.

Data Studio offers data sources to be combined and reports are designed based on that. It is called blended data sources. Blending can be done based on multiple dimensions. It is also possible to blend a data source to itself if the need arises. 

Pros of Google Data Studio

In this section let us dive into some Google Data Studio benefits.

1. Cloud-based and Completely Managed

Unlike most popular business intelligence tools like Power BI, Tableau, etc. Data Studio was designed from ground up as a cloud-based service. It is a completely managed service which means the user does not have to manage any kind of infrastructure or installation. 

2. Tight Integration with Google’s Ecosystem

The biggest advantage offered by Data Studio is its ability to integrate seamlessly with Google applications like Google Analytics, Big Query, Google Sheets, etc. So if your ETL architecture is primarily built on top of Google applications, you will save a lot of time when integrating with Data Studio.

3. Easy to Use

Data Studio offers a very easy to use UI that will help anyone acquainted with Google products to start creating reports and dashboards within a few clicks. In that sense, it offers a very flat learning curve. 

4. Access and Sharing Controls

Being from the Google family, it inherits the granular access control and sharing mechanisms that are typical of the Google office suite. Sharing reports and dashboards to other users and restricting access with a high degree of granularity is very easy in Data Studio.

5. Support for Live Connections

When compared to other BI tools like Power BI, Tableau, etc, Data Studio is designed on the premises of a live data connection. This means there is no elaborate logic or scheduled jobs required to manage the freshness of data. Anytime a report or dashboard is accessed or refreshed in UI, it will fetch the latest data. If the resultant performance hit seems too much for you, it is possible to adjust the fetch using cache settings. 

6. Free of Cost

Data Studio is offered free of cost at this point and is bundled with Google cloud services. The storage cost for data and processing costs for transformation is outside of this. 

Cons of Google Data Studio

Now let us look at some limitations of Google Data Studio.

1. Lack of Real-time Updates in the Dashboard

Even though the live connection is supported for most data sources, there is no built-in method at the moment to keep a dashboard or report view auto refreshed. So if you want to project a real-time updated dashboard to motivate your team, you will have to look beyond built-in Data Studio features. A workaround using a third-party browser extension is possible to accomplish this use case. If you are interested, you can find out more about this here.

2. No Support for Excel

Being a Google product, Data Studio disregards standard business intelligence data formats like Excel and prefers Google based services instead. Excel can be supported by converting it to a CSV file or a Google Sheet

3. Lack of Comprehensive Function Support

Data Studio is still nowhere close to the number of built-in data processing functions supported by Tableau and Power BI. It even misses some basic functions like SUMX present in Power BI that helps to compute the sum of columns considering both rows and columns. 

4. Slow Speed in Case of Live Connection

One consistent feedback about Data Studio is that loading the dashboard becomes exponentially slow with the increase in complexity of functions that are part of the view. This is a side effect of the live connection mechanism and the workaround is to use a scheduled extract in cases where performance is critical.

5. No On-premise Deployment Option

For organizations with strict data security requirements, the lack of an on-premise option is a big setback. Such organizations will prefer business intelligence tools like Tableau, Power BI, etc. The latter tools provide desktop installation support and the ability to access data within their internal network. 

6. Lack of Native Connector Support for Cloud-based Data Sources

Data Studio lacks native connector support for some of the most frequently used cloud-based data sources like Hubspot. Even though there are a partner and community-based connectors to cover some of them, these are paid offerings.

7. Complex Visualizations Not Possible

Even though it is easy to set up basic visualizations in Data Studio, it does not support the kind of flexibility and customizability that is offered by tools like Tableau. So organizations with analytical needs and expert analysts may find Data Studio lacking in visualizations.

When to Use Data Studio

Distilling all of the above Google Data Studio pros and cons, let us try to arrive at a set of indicators that can help you decide whether to choose Data Studio for your use case or not. You should prefer Data Studio if your use case meets most of the below conditions.

  • You are primarily dependent on the Google app ecosystem and your architecture is based on Google services.
  • Your requirement does not need complex dashboards or deeper interactivity with reports.
  • You do not have a dedicated team of analysts expert enough to take advantage of additional functions provided by tools like Tableau.
  • Your compliance requirements are not broken when data is taken outside your internal network.

Most of the cons mentioned in the above analysis can be solved by using a managed ETL tool like Hevo. Hevo can manage data movement to Data Studio from almost all kinds of data sources and perform complex transformations on the move to set up data for reporting. 

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What are your thoughts on the Google Data Studio pros and cons mentioned in the blog? Share your suggestions in the comments!

Vivek Sinha
Director of Product Management, Hevo Data

Vivek Sinha has more than 10 years of experience in real-time analytics and cloud-native technologies. With a focus on Apache Pinot, he was a driving force in shaping innovation and defensible differentiators, including enhanced query processing, data mutability support, and cost-effective tiered storage solutions at Hevo. He also demonstrates a passion for exploring and implementing innovative trends within the dynamic data industry landscape.

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