New integrations unite Looker with other Google BI tools

As part of a project the tech giant is calling Big BI, Google is unifying the analytics vendor's capabilities with those of Data Studio and Connected Sheets.

Google today unveiled integrations between Looker and both Data Studio and Connected Sheets to form a more unified BI platform.

Looker, a cloud-based analytics vendor founded in 2012 and based in Santa Cruz, Calif., was acquired by Google for $2.6 billion in June 2019.

Looker offers traditional self-service BI capabilities such as data visualization, along with a suite of application development tools. One of its primary differentiators, meanwhile, is the advanced data governance it provides via its LookML semantic layer, which enables organizations to standardize their data.

At the time Google acquired Looker, however, the tech giant already offered self-service BI capabilities with Data Studio, which was launched in 2016 and is part of its Google Analytics 360 suite. And subsequently, Google developed Connected Sheets, a spreadsheet interface for viewing BigQuery data it launched in June 2020.

Looker continued to evolve following its acquisition by Google, and was even integrated with Google capabilities such as the Google Marketing Analytics Suite. But until now, it remained separate from Google's other BI tools.

Unified BI tools

Connected Sheets for Looker and the ability to access Looker data models within Data Studio changes that, joining the capabilities of the three BI tools together as part of a project Google is calling Big BI.

Connected Sheets for Looker will enable Looker users to access the no-code capabilities of Connected Sheets to develop data assets such as pivot tables and charts. The ability to access Looker data models within Data Studio, meanwhile, will enable Data Studio users to use data models developed in Looker and easily import analyses from Looker into Data Studio.

And perhaps most importantly, users of Data Studio and Connected Sheets will get the data governance capabilities of Looker, according to Gerrit Kazmaier, vice president and general manager of databases, data analytics and Looker at Google Cloud.

"What this means is our customers can get the flexibility and scale of Data Studio for self-service reporting and the trust, efficiency and governance of Looker's platform," he said during a media session on April 4 ahead of Data Cloud Next, a virtual user conference the tech giant is hosting today.

"We are also integrating Google's Connected Sheets so that our customers have a choice between self-service dashboards, a spreadsheet-like experience in Sheets, or the governed platform capabilities of Looker," he continued.

Big BI is basically taking investments we have had … and bringing them into a unified portfolio. This will enable organizations to have self-service tools and also centralized metrics and have a common understanding of the business across the organization.
Sudhir HasbeSenior director, product management, Google Cloud

Similarly, Sudhir Hasbe, senior director of product management for Google Cloud, noted that the integrations among Looker, Data Studio and Connected Sheets will give Google's BI customers the choice of using their preferred BI platform while giving them access to the capabilities of the others within Google's analytics ecosystem.

"Big BI is basically taking investments we have had -- Data Studio, Connected Sheets, Looker -- and bringing them into a unified portfolio," he said. "This will enable organizations to have self-service tools and also centralized metrics, and have a common understanding of the business across the organization."

Outside perspective

Analysts, meanwhile, also said the new integrations will be valuable for users of Google's various BI tools.

David Menninger, an analyst at Ventana Research, said the most significant benefit is the access Data Studio and Connected Sheers users now have to Looker's semantic model.

"Semantic models have tremendous value to organizations," Menninger said. "Those that have successfully implemented a semantic model are more than twice as likely to be satisfied with their analytics, and three times as likely to be comfortable allowing self-service analytics.

"So, the integrations mean that more people in an organization can take advantage of the semantic model," he continued.

Despite the impact that broader access to Looker's semantic model might have on self-service analytics, Doug Henschen, an analyst at Constellation Research, said the new integrations will indeed benefit users. However, the integrations don't represent giant advancements for Google's analytics ecosystem.

According to Henschen, integrating Looker into Google rightly took priority over the past couple of years. That meant delivering Looker as a service on Google Cloud, integrating Looker with Google's data management services and plotting a roadmap that made sense for Looker within Google rather than as an independent BI vendor.

The integrations among Looker, Data Studio and Connected Sheets, meanwhile, are useful but not imperative.

"These are fairly simple and obvious integration points that will simplify compatibility and data interaction among Google Services," Henschen said. "These announcements address nice-to-haves, versus the must-have integration points that were addressed early on."

Google and Looker

While the new integrations may be more about useful improvement rather than momentous advancement, they do continue to show how Looker can blend into the Google ecosystem and that the tech giant's acquisition of the analytics vendor was ultimately a successful one.

Google, which already had data visualization capabilities with Data Studio, is indeed benefitting from Looker's semantic model, according to Henschen. In addition, he noted that Looker's application development capabilities add value.

How an organization can analyze data in Connected Sheets for Looker
A gif displays how an organization can analyze data in Connected Sheets for Looker, a new integration that unites two of Google's BI platforms.

"What Google wanted was Looker's powerful, centralized modeling capabilities and the API-centric platform for driving data and insights into action within applications," he said.

Henschen added that the combined entity makes Google's BI capabilities more competitive with Microsoft Power BI, which has a 36% share of the BI software market, compared with 20% for Tableau, 11% for Qlik and 6% for Looker, according to the website TrustRadius.com.

"Power BI is the most established and widely used of the BI/analytics products offered by the major cloud vendors, partly because it's so affordable," Henschen said. "[But] AWS with QuickSight and Google with Looker are continuing to refine their services and strategies, and I think [AWS and Google] will have opportunities as customers do more on their clouds and face upgrade and renewal cycles."

Similarly, Menninger called Looker a good fit for Google.

Like Looker's platform, much of the Google Cloud portfolio has historically been targeted at developers, he noted. And that audience has the know-how to create semantic models.

"Google recognized the strengths of Looker and is now more fully realizing those strengths bringing them to the larger audiences of Data Studio and Connected Sheets," Menninger said. "They've also done a good job integrating Looker with the data platform offerings such as BigQuery. It may have taken a while, but integrating acquisitions is always a challenge."

Beyond Looker

In addition to the integrations among Looker, Data Studio and Connected Sheets, Google unveiled a host of other new and enhanced capabilities during its Data Cloud Summit.

Among them, the tech giant unveiled a data lake storage engine called BigLake in preview; the general availability of Vertex AI Workbench, a single environment for data and machine learning systems where users can perform analytics, data science and machine learning; and the formation of a data cloud alliance among Google Cloud, Confluent, Databricks, Dataiku, Deloitte, Accenture, Elastic, Fivetran, MongoDB, Neo4j, Redis and Starburst to make data easier to move and access across business systems.

"The biggest challenges are around the growing size of data," Hasbe said. "We want to make sure that we remove barriers from getting the value from all of the data that's there."

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