Google Data Studio Reviews

Looker serves as our main business intelligence reporting tool by creating dashboards and reports. We create interactive dashboards that help users understand the changes in data and the current trends occurring day-to-day.

For example, daily at the end of the day, once we receive site closure for all the sites in our restaurant project, we refresh our PDTs and load the data into Looker dashboards using DAGs. We have created an automated dashboard that loads the data directly into it. It has a map function, a bar graph, and also a pie chart, from which users can understand the sales of the previous day with the help of visualization. This is a day-to-day report.

The main use case is creating dashboards, reports, and giving business insights to the business users.

One of the best features that Looker offers is LookML, which allows us to develop dashboards directly rather than dragging and dropping. We can also customize some fields that are not available in the table by combining different tables using LookML. Compared to other reporting tools, Looker is somewhat more customizable and interactive.

With the help of Views and Explore in Looker, we can combine different tables and create a unique column that is not available in the table, and then use that column in our reporting dashboard.

Since we brought Looker into our project, it has improved data visibility and enabled faster interaction between us and our client. Rather than simply using free form or Essbase, seeing customizable and visualized data in Looker allows the business users to understand the trends in the data more easily rather than just seeing numbers. It has helped us save a lot of time. The automated dashboards that we develop allow us to run the dashboard for the coming years with only some modifications, which has reduced some manpower and working hours.

When I joined this project, there were around eight to nine people for developing Looker dashboards. Once the development part is completed for almost all cases, the next part is just monitoring the dashboards and making some minor changes required to align with business goals. After development, the team was reduced to three or four people, so the manpower has significantly reduced, and time taken has decreased as well. For a business user to understand data from free form or Essbase, it usually takes thirty minutes to an hour to go through the previous day's trend. However, with the visualization tools in Looker, they can easily understand the data and compare it to the previous one or two years using a bar graph, pie chart, or other graphs.

The return on investment has been very good. The number of employees has reduced to half, and time is saved by nearly two to three hours a day because of automated reports. We are just monitoring the reports to ensure they are released. This has saved nearly two hours per day, and the team has been reduced to half.

One thing is that Looker is not user-friendly to non-technical users. Someone with knowledge of Looker can understand it, but a person without any technical background finds it somewhat advanced or difficult to comprehend. Additionally, the pricing clarity when creating custom reports is not clearly mentioned.

Another area for improvement is the pricing structure, and the performance is not optimal on large data sets.

I have been part of this field since I started in January 2025, and it has been around one year and three months.

I have not experienced any software issues with Looker.

I rate Looker's scalability a nine because of how easily we can scale by simply combining tables.

The customer support is adequate since it is related to GCP. It is a vast organization, so the support is delivered as soon as possible. One thing to consider about Looker is the documentation part. Anyone with minimal knowledge of Looker can easily go through the documentation and understand the steps and the UI.

We have used Essbase or free form for reporting, but now we are transforming our reporting into Looker.

The return on investment has been very good. The number of employees has reduced to half, and time is saved by nearly two to three hours a day because of automated reports. We are just monitoring the reports to ensure they are released. This has saved nearly two hours per day, and the team has been reduced to half.

We have evaluated other options such as Power BI, Tableau, and also considered continuing with Essbase and free form. However, upon comparison, we found that Looker is superior to all of them.

My advice to others looking into using Looker is that if they are new users with some technical knowledge of SQL or Python, they can understand LookML code or use the explore tab to drag and drop the table columns easily. However, if they are not technical, I advise them to first go through a course or the documentation before starting with Looker.

I have expressed all the thoughts I would to share about Looker for this review. I would rate this product an eight overall.

My main use case for Google Data Studio is dashboards and reporting. For every month, in some cases, it pulls fresh data from a brand new campaign that got launched and ensures that it is visually represented and also easy to monitor. From a monthly reporting perspective, I look at the data and provide an analysis on what is there, writing that into the report for each page and each segment.

The scope of that workflow is small, so anybody in my department uses it. I would say that I do approximately 90% of it, including most of the reports for our clients and building out most of the dashboards.

The best features of Google Data Studio for the way I work are all of the Google integrations, naturally. It is super easy to use and to import, and it is used every month for 99% of our reports. Additionally, it is free.

Google Data Studio has impacted my organization positively through all of the above aspects, such as saving time, improving reporting quality, and making things more consistent for clients, which is why we stick with it.

In terms of time saved, my team probably saves close to 40 hours per week due to using Google Data Studio instead of the old manual process.

Google Data Studio has changed how my team works together by providing visibility and consistency. We naturally collaborate a lot, but it is definitely nice to have one resource that we can all look at and understand.

The biggest friction point or frustration I have encountered with Google Data Studio is the lack of automatic integrations. For example, with Meta, you have to pay for a premium integration for it to automatically upload, or you have to export it manually and then import it using Google Sheets. Not having everything be automatic is definitely the biggest pain point and issue.

I wish Google Data Studio had better filters, as they do not work that great, and the blended data options are really finicky and difficult to manipulate and figure out.

I have been familiar with Google Data Studio for over five years.

Adoption across my small team goes well, and everyone uses it in the same way. It is one or two templates with a few different report templates that get copied every month, and then we update our respective clients.

Before Google Data Studio, I know that the team would export Excel sheets and write up things in a Word document and export it as a PDF.

When I first implemented Google Data Studio, it did not take long to get up and running in a way that worked for my clients. I did not need any formal training, as it was super intuitive, so there was nothing necessary there. It was just a matter of connecting the various platforms and understanding how to do that.

I have always considered Google Data Studio, although recently we have been looking at Agency Analytics for dashboards.

I stick with Google Data Studio because of the price point, since it is free. However, ultimately, we do need more platform automation, and that is where Agency Analytics is coming into play.

My advice for someone considering Google Data Studio who has a similar workflow to mine is to start off from one of the templates that are available. It is a good place to start so you can understand how to manipulate the data, how to import the data, and different ways that you can view things. I would rate this product an 8 overall.

We use the solution for operational reporting.

Looker allows for multiple customizations directly at the reporting level. It allows me to manipulate column names, and types and to implement data-level security.

Some basic feature that is available in other reporting tools is missing. Looker has the ability to show more than 5,000 rows for operational reporting.

Some reporting tools allow users to scroll down to see more than 5,000 rows, but in Looker, you have to download the entire dataset. Looker should consider adding a scroll-down option to allow users to view large datasets on screen without downloading them.

Looker has some options for granting users access as viewers. However, viewers cannot download the entire dataset. Only superusers can download the whole dataset on the Explore screen. This is a big limitation, as you cannot give any user viewer access. You can give access to superuser access, which is a cost to the company.

I have been using Looker as a consultant for two years.

The product’s stability is not excellent. For example, the filter drop-down is working one day, and the other day, the filter drop-down will not work. Now you’ve to close all my screens, clear cache, etc., and then it will start working again. The report doesn't break down, but it is not 100% stable either.

I rate the solution’s stability a six or seven out of ten.

Looker has put a limitation on the number of records to be shown.

280 users are using Looker.

The technical support is really bad. There is no way to save a screen recording. If there is an issue with the report, I cannot download multiple snapshots and send them. I need to send them one PNG file at a time. There would be a better way to share issues. Their responses over the chat are no easy way to replicate the solution or the issue.

The customer needed to report on their SAP data in a cloud system, and they wanted to migrate their data from SAP to a cloud solution and do reporting on that. They also wanted to expose their data set to multiple tools, such as Tableau. Looker was not the main reason we chose this solution. BigQuery was the main reason we chose Looker because Looker is now part of the Google Cloud Platform. We chose BigQuery over AWS Redshift, Snowflake, and Azure Synapse Analytics because it was more affordable.

The initial setup is not difficult, but it is not easy. For a person using it for the first time, they will take some time to navigate, but if you have a person who has used other reporting tools for them, it will be straightforward.

When we started, it was affordable, but now they have revised the prices. It is expensive now.

Looker is a developer's delight because it offers many customization options. However, there is much room for improvement from the end user's perspective.

Overall, I rate the solution a seven out of ten.

I use Looker in my company to analyze the data from Google Analytics and also the data from our internal source.

The most valuable feature of Looker is that it is very quick with Google Data Studio. The solution offers good integration between Google Data Studio and Looker's visualization part.

I can say many things about the parts that need to be improved in Looker. The main area of concern in Looker is probably related to blending the data from the different sources, including the data present internally in the company and on the cloud. Blending data from different sources is not something that can be done easily.

I have been using Looker for six years. I don't remember the version of the solution. I am unsure if the last release of the solution happened in 2022 or 2023. My company has a partnership with Looker.

It is a pretty stable solution because it is a cloud-based product. Compared to other cloud-based products on the market, Looker is pretty much stable.

Stability-wise, I rate the solution an eight out of ten.

Scalability-wise, I rate the solution an eight out of ten.

Five or six people in my company, including the manager, use Looker.

I have contacted the technical team of Looker several times in the past, but I have never been very satisfied with their support. Looker's technical team takes too much time to respond to my queries. I usually found the solutions to my issues on forums or through other communication channels.

I rate the technical support a five out of ten.

Neutral

In my company, we use other products apart from Looker. In my previous company, we usually used to combine reports with the help of Looker and other BI tools because it's pretty much easy to integrate with Google.

The product's initial setup phase is very easy compared to other BI tools like Tableau, PowerBI, and BusinessObjects.

The product's installation deployment phase

The solution is deployed on the cloud services provided by Google.

Small companies can experience return and investment from the use of Looker as it offers easy and good integration capabilities, and the time needed for the adoption of the tool in a company is very short, making it one of the best things about Looker. With other tools apart from Looker, it takes too much time to adapt and to start sharing the reports. From the beginning, I have known Google Data Studio and Looker.

The price of Looker usually depends on the solution's provider, but it is usually cheaper than the other products in the market. Looker is offered at different prices for different companies.

I recommend Looker over other BI tools to those planning to use them in their company. The leaders and management in an organization have various reasons to choose other tools apart from Looker. You can manage a lot of reports with Looker. Looker is also easy to use and share with others in a company, while certain factors like the levels of authorizations and so on are helpful in the product when considering elements like security issues.

I rate the overall product an eight out of ten.

We use it mainly for embedding dashboards, data governance, and handling complex solutions. 

For current projects, we have four Looker instances, including dev staging, two productions for external users, one for internal, with CI/CD pipelines and automated tests. It's a comprehensive solution, not just one instance.

Features like Symantec layer, LookML, and Looker API are significant. The semantic layer and LookML provide powerful capabilities. Embedding API is also useful. 

The documentation is efficient. So far, it's effective. Also, support for different database dialects and the holistic vision is good for advanced analytics; it's not only numbers but also being able to program something. 

It supports advanced analytics beyond just numerical data.

Integrations with other BI tools could be better. Big companies are using different tools and different stacks. 

Also, the plan and roadmap should be more transparent. This would allow end users to correlate or plan company features and align them with Looker's plans and features.  

Support is generally good, particularly the online support where you can quickly chat with an engineer to resolve questions. However, for more complicated issues, like a cache issue we encountered for embedded networks, the resolution can lag. Issues can remain unsolved for a while. The process of raising a product feature can also be lengthy.

I have been using it for five years. I use the latest version. 

I would rate the stability a ten out of ten. I didn't face any issues with stability. 

I would rate the scalability an eight out of ten. We have around 4,000 viewers, and around 200 internal admins and developers.

Support is pretty good. They provide online support, so we can quickly hop on a call.  However, for more complicated issues, like a cache issue we encountered for embedded networks, the resolution can lag. Issues can remain unsolved for a while.

Positive

We used Data Studio, which is also on the Google Stack, but that was four or five years ago.

It was the company's choice, primarily because of Symantec Wear and their plans for the gateway, but it wasn't so good.

The company chose Looker over another data analytics platform because of Looker's significant awareness feature. However, the significant awareness feature is not yet perfect, and we had to do some workarounds to be able to use it.

The initial setup depends on the company. If they have appropriate specialists, it's straightforward. But it can be challenging for different departments within the same company that haven't used Looker before.

The initial deployment was fast, especially for an MVP version. However, the overall deployment time depends on the company's roadmap. For instance, just to roll out the instance, it's pretty fast.

There is a return on investment for big companies. For smaller companies and startups, it is not worth to consider.

It's not cheap, but it's not expensive for big companies.

So, for small businesses, it might not be a good choice, but for medium and larger businesses, it's okay.

Be ready to face a learning curve. For companies, understand it's not a one-week or one-month setup like Data Studio, Tableau, or Power BI. Invest in long-term projects, and management should understand the time commitment. 

It's worth it for big companies, but smaller ones should carefully consider their needs.

Overall, I would rate the solution a nine out of ten. 

We use the solution to visualize the data differently. Sisense can directly query large data and visualize them in real-time. In Looker, You have to write preexisting scripts to fetch consolidated data from external sources and store it in Google Sheets. If your data is stored in a Google database, you can query it in real-time. Otherwise, you have to integrate your data with Google Sheets or other supported sources. Looker can handle more than a few thousand rows of data, but it may be slow to query large datasets.

You can divide visualization into sections, drill down, and filter inside a table. These features are not available in Sisense.

The solution could have more integrations with different data sources and support real-time querying. Currently, it has integration with Google Sheets.

Looker's visualization capabilities are not yet ready to be shared with clients. The current visualizations are basic and only intended for internal users. The styling and design aspects of the visualizations are still under development. The Visualization could be customized.

I have been using Looker as an end user for two months.

The product has some stability issues. Looker has problems reading data formats that change due to changes to the underlying dataset. It can struggle to cope with these changes.

I rate the solution’s stability a six out of ten.

The solution is not scalable because the dashboard will resume daily, and there is no automated way of duplicating the dashboard.

60-70 users are using this solution in our organization.

I rate the solution’s scalability a four out of ten.

We were using Google Sheets mainly. Sisense was much easier to migrate to Looker. Superset do not allow you to query the data on dashboards. Looker was more technical than the different solutions and has robust data visualization and querying capabilities.

The initial setup is straightforward. Deployment was very fast, taking less than a month. It was straightforward because everyone could access it directly, and there was no data onboarding. We already knew how to use it effectively and were able to start using it within the first week after we decided to deploy it.

For deployment, we have decided on a format in which we want to visualize the data according to different clients with customizations. We have developed a set of rules that define the structure of the visualization of the dashboard. Once we have finalized the format, we will roll it out to the larger team, and everyone can build their dashboard within a few days.

I rate the solution’s setup an eight out of ten.

We deployed in-house with a large team effort. The initial onboard deployment and upload were successful. We took help from 20-30 people for the larger onboarding, but it was relatively faster and took only 3-4 days.

The solution is open-source. It is free for all.

Everybody maintains their dashboard. There is very minimal maintenance required to be handled. Maintenance is almost nothing.

The solution is quite intuitive. There is nothing to do. There are demos available on YouTube.

They take considerable effort to scale Looker. For any company that wants to visualize its data in Google Sheets, I recommend moving to Looker Studio. It's much easier to keep updating the data visualizations and be more organized with your visualizations. Google Sheets is good for storing raw data, but anytime someone is using Google Sheets or Excel to visualize data, I recommend that they go to the cloud. It's a very intuitive, very natural process. There's almost no extra effort involved. It is a worthwhile investment.

Overall, I rate the solution a nine out of ten.

At our work, we deploy services, especially for payments, and get feedback on the integrated percentage of these deployments. These services are scattered across different locations, so I also handle administrative tasks, integrating AI for subscription management and addressing globalization concerns. We're focused on securing our operational environment and streamlining account management. There seem to be some challenges in the process, and I utilize Looker for analysis and resolution.

Recently, I've found more value in what I'm currently exploring. It's quite effortless to navigate through various applications and review their updated data in real-time. While using Looker, I encountered a hitch in the application setup process; it took a bit of time and effort to configure, but once it was up and running, the results were remarkably impressive.

Stability needs improvement. 

I have been using Looker for the last four years.

My experience over the past six and a half months has been quite intense. The challenges I faced were with Looker, which was crucial for the app we were developing. Initially, there were some issues in setting it up, and without resolving these, we wouldn't have been able to proceed. It was necessary to break down and address these hurdles to move forward.

I would rate the scalability an eight and a half out of ten. 

I've only had a single encounter with it. The folks handling it did respond. I assured them that I hadn't faced many challenges. If I did encounter any, I'd go back and check. 

The accessibility concerns are significant. We have approximately forty-five users, which makes the cost of licensing quite expensive. This aspect holds paramount importance for us. There are some specific reports we need to share. We use Power Desktop within our domain and intend to explore other options. That's why we use Looker—it provides easier and seamless access for individuals outside the licensing sphere.

It is easy to use.

It is cheap. 

Our data usage is in Google, using SQL. However, the dataset size varies significantly. While some sets are not extensive, others fall into the category of large enterprise-level data, impacting costs. It's not a singular size; it varies. We're considering transitioning to Looker due to performance issues, specifically in this context. 

I would rate the solution an eight. We have various pros and cons to consider. Right now, the key advantage is the easy access and availability. Sharing information is seamless, and it facilitates quick comparisons with near-immediate updates—it's almost live. We don't need instances for file logs; reports are accessible right there. Visualizing data is also straightforward. In terms of drawbacks, the experience is similar to Looker, functioning as an IPaaS.

I have used the solution for real estate and customer behavior analytics. Currently, I am working on a tax project.

The product is better than any other BI tool. It is really helpful. If my data model is strong and I can code most of it in Google BigQuery, it's much easier to play around and perform a little data science in LookML. The product is easy to use.

Looker just shows whatever we have in SQL databases. It would be helpful if the product could retrieve user data and store it in Google BigQuery. The product does not have documented material. It does not have any active live projects that users could work through.

I have been using the solution since 2021.

The tool’s stability is the best. I haven't encountered any issues where we had to roll back.

The code works pretty fine. I haven't encountered issues with scalability. I rate the scalability a four out of ten. We have more than 250 users in the tax project, 25 to 60 in the real estate and mortgage project, and more than 300 in the financial projects.

Previously, we have worked on Power BI and Tableau.

The initial setup is pretty straightforward. I rate the ease of setup a three out of ten. The solution is deployed on the cloud. The deployment takes place under sprints. The sprints are two to three weeks long, depending on the project.

We are using a legacy mode. My manager helped me learn the solution. I learned it in two weeks. If we have a real-time project within the company, it makes sense for new users to start working on the tool. Overall, I rate the tool an eight and a half out of ten.

I have been using Looker to help the data team create some models. I have also created views for reports and dashboards.

We can centralize all our data models. I can bring data from different products and use it. It's easy because I can create views using SQL. I can make the reports available to different teams and create dashboards. I like that we can centralize different databases.

The response time can be a little bit better. I know that it depends on my data ingestion and the kind of database I am using. The integration with different databases must be improved.

I have been using the solution for the last three years. I am using the latest version of the solution.

I rate the tool’s stability a nine out of ten. We did not have any problems.

I rate the tool’s scalability an eight out of ten. I'm constantly working and learning with it. It's a powerful tool. Around 300 people from the product and business intelligence teams use the solution in our organization.

I never had to contact the support team, but I know that they are available to us. It was never necessary in the last three years.

The initial setup was pretty hard. Now, I know how to do it. When I face some issues, I can work around them. Now, it is pretty easy for me. The solution has good documentation. We can use the documentation to solve the different issues we face. We use CI/CD. We open a branch there and collect reviews. Normally, the deployment takes one day because we have to wait for the reviews to ensure everything is fine.

I have a good experience with the solution. I tell my friends from other companies to migrate to Looker. People who want to use the solution must get a demo and play around with it. It is very intuitive. We must have some technical skills and knowledge of the data and architecture. When we bring data from different teams, we must know how they are using it. Overall, I rate the product a nine out of ten.

We use Looker to create dashboards for our stakeholders. It's a BI tool that allows us to aggregate and visualize data so we can draw conclusions from it. Around 200 to 300 developers and business analysts are working on these tools.

Looker allows you to generate the most optimal SQL queries in a DC through UI actions. We had signed a contract with Google Cloud to use BigQuery. That was the primary reason we adopted Looker. It works better with BigQuery than any other BI platform. We also like how this tool was developed. It was designed with an eye toward microservices architecture.

The APIs are exposed at every level, so it's highly modular. We can use the APIs and build whatever we want. We thought Looker was the go-to tool for us because we have the flexibility to play around.

Looker doesn't connect to Excel, which is a huge disappointment because a lot of data is presented in Excel. Also, it can't consume data directly from REST APIs, which is necessary. Looker needs to expand its horizons when it comes to data sources. The inability to connect to different data sources is hampering our use cases. Currently, it only has an ODBC connection that connects to a database. It needs to connect to other data sources, such as Excel, APIs and different platforms.

You can't create very attractive visuals with the built-in features, but I think they're providing some extensions to develop different visualizations. Looker can't create multiple sheets. You can only create one dashboard. It's not that you're able to toggle through different sheets to navigate different visuals. It's just one and people have to scroll through the dashboard. So scrolling is not something that people generally like to do, rather than segregating them in multiple tabs of sheets.

I have been using Looker for six months.

Looker is a stable product, but it's still growing at this point of time. There are other BI platforms outside, which are good competitors which have been in the market for a very long duration of time, so just clicks on Tableau. In comparison to that, Looker is at this point of time, still a baby. However, the advantage that I see with this is the ability to gel with BigQuery, which is a data warehouse offered by Google Cloud platform.

And since this tool is ingrained within it, it's able to query large amounts of data and very small durations of time. If one has two deal with large volumes of data, which I categorically could tell us big data and has to create visuals, Looker is the tool to go. Otherwise, if it's for small volumes of data, there are other BI platforms which can be leveraged for this.

I rate Looker support eight out of 10. I had to reach out to Looker support for specific use cases that were not readily available in the documentation. I asked the support team if there is a way to use a particular API to extend the native functionality. They respond quickly. They may not always have the right answer, but they're responsive. 

Positive

It's not that difficult to spin up Looker. Basic configuration takes about a day, but it can take a lot of time to onboard different teams and enforce security policies.

I am not 100% sure about the price, but Looker is slightly less expensive in comparison to Power BI. The tool isn't cheap, but it's roughly in the same range as its competitors. I think they give larger organizations discounts for high use.

I rate Looker six out of 10, considering the features are limited relative to its competitors. We use Looker because it can query large data sets optimally, but it needs to add some features that other products offer.

I recommend new users try the Google Cloud Skills Boost platform  If you're using Looker for the first time, you might get lost trying to use some features. It's better to go through these labs, which are available for free on Qwiklabs and also on the Google Skills Boost platform. Users can do a couple of labs to understand what the tool has to offer and evaluate whether this is the right tool for them.