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.

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.

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.

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 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.

I'm aware of Power BI and Tableau.

If I compare Tableau, Power BI, and Looker and Looker has a little bit of a disadvantage over Tableau. Tableau, in terms of how things have been created, is better from a user perspective. It is a little costly, however, at the same time, the kind of interactivity, the kind of interaction it gives to the business, is useful. Power BI is a little cheaper compared to Tableau in terms of subscription. Power BI also is great for companies that have a lot of databases like Microsoft SQL Server or Microsoft Azure.

Previously, we have worked on Power BI and Tableau.

I have experience with Tableau and Power BI. With Looker, it is easy to sign up, and its onboarding process is straightforward. There is no need for me to download any app for Looker if I use it online, but for the other tools, most of the time, I have to download the app if I want to access more features or capabilities, and at times, I may have no idea about it. The spread of features and how much Looker offers is much less compared to what the other BI tools offer, which I feel are more advanced.

Our current (still in use) solution did not meet basic requirements.

We've previously used at least two other solutions. Looker was better for several reasons. Number one, it makes it easy for 'non-techie' people to do "techie" things. LookML is powerful and intuitive. Looks and dashboards are easy to complete on the fly even if you don't use LookML. Number two, we really like the API stuff and the ability to use "Looker data" in other apps lends itself to solving the "one-source-of-the-truth" puzzle BI folks often face. Number three, their support is fantastic. It was pretty useless with others, and is really great with Looker.

At Knewton, we had our own custom-rolled solution with D3 before. It was great, but not agile. A REST API for data access and a framework for visualization. An engineer was always required for new dashboards. We wanted to empower self-service analytics.

We used to have an on-premise hosted solution. We switched because the embed functionality was lacking, user-based access filtering was painful, and the visualizations were not very clean or interactive. Looker addressed all of those issues for us.

This was our first BI product.

We previously just ran our analytics out of Google Sheets with charts based on queries of our database each week. We reached a size where this was no longer feasible and we needed a more robust data warehouse and BI stack.

We previously used Omniture which was effective for click-stream analysis but limited in exposing other types of data, like product state data (I.e. how many users are currently subscribed to our newsletter?). We also considered Tableau, but it lacks a semantic/programmatic middle later and so is nowhere near as customizable. I wanted a tool that would be extremely flexible so I felt like we'd never be truly 'stuck' in what kind of dataset we could provide to end users without implanting large ETL projects.

We previously used RJ Metrics. I was not part of decision to switch.