Less than two years after taking the helm at Google Cloud, Chief Executive Thomas Kurian has made his playbook for competing with Amazon Web Services Inc. and Microsoft Azure clear: Build for a multicloud future, democratize analytics and press the advantage offered by BigQuery.

That last strategy has proved to be a major factor in Google Cloud’s recent financial resurgence. Designed from the start as a cloud-native database, BigQuery was singled out in parent company Alphabet Inc.’s October earnings report as a key contributor to several major customer wins and a 45% jump in revenue for Google Cloud during the last quarter.

BigQuery’s emergence as an attractive data warehouse and analytics option has given Google Cloud a much-needed tailwind. However, it was Kurian’s first major acquisition last year, the largest under his tenure to date, that may turn out to be the most significant factor in propelling the organization’s cloud success.

In June 2019, Google paid $2.6 billion to acquire the data analytics tool provider Looker Inc., in a proposed deal that became final in February. Looker offered its customers, 350 of which were also Google Cloud users, a way for enterprises to derive insights quickly from massive datasets. That enterprise imperative has become all the more apparent lately, most recently with the blockbuster initial public offering of stock by cloud data warehouse upstart Snowflake Inc., which aims to make the storage and analysis of data much easier.

But even though many observers viewed the Looker acquisition as a way for Google to bolster its analytics platform, there was perhaps a more significant motivation behind its multibillion-dollar investment. The company was building a critical bridge into the red-hot world of developers, cloud-native application development and APIs.

“A lot of people think about Looker as a business intelligence platform, but it’s actually much more than that,” said Debanjan Saha (pictured), vice president and general manager of data analytics at Google, who spoke with Paul Gillin, SiliconANGLE senior editor and host of theCUBE video studio.

“What is unique about Looker is the semantic model that Looker can build on top of data assets,” Saha explained. “Once you have the data model, you can create a data API, an integrated development environment on top of which you can build your custom workflows, your custom dashboard, your custom data application. That is where we are moving.”

Giving developers options

Following the purchase of Looker, Google made two additional moves in the past year to enhance its data analytics portfolio. Both were revealing in terms of the positioning of Google Cloud.

In July, Google announced the private launch of BigQuery Omni, an extended offering that will ultimately allow developers to use BigQuery across multiple clouds, including AWS and Azure. The message here is that Google wants to give developers an option to analyze data locally and avoid moving it between platforms where it most assuredly resides, recognizing that this is now a multicloud world. And it will use its big bet on the Anthos platform to make that happen.

“More than 80% of the customers we talk to use multiple clouds and that trend is probably not going to change,” Saha said. “Sometimes it’s because of compliance, sometimes it’s because they want different tools on different platforms. We are a big believer in a multicloud strategy and that’s what we are trying to do with BigQuery Omni.”

The second major move occurred in August, when Looker unveiled new data analytics features. In addition to business intelligence and analytics platform upgrades, Looker released a new development framework into general availability that allowed developers to customize data experiences and embed them into enterprise apps.

Google’s intent is to move beyond dashboards and empower developers to customize the data they need for specific tasks.

“I don’t think people want the old dashboards anymore,” Saha explained. “They want their data experience to be immersive within the workflow and within the context in which they are using the data. A lot of customers are now using the power of Looker and BigQuery and other platforms we have and building this custom data app.”

Data democratization

Google is also leveraging the power of its public cloud to promote the democratization of data science. This is a clear theme that has been carefully integrated into the company’s blog posts and the writings of Saha himself over the past two years.

From Google’s perspective, improved data access through BigQuery allows data analysts to get insights more easily by using familiar tools such as SQL. BigQuery’s serverless backend makes self-service business intelligence a simpler and more scalable proposition for any amount of data or number of users. That’s the power offered by public cloud, Saha says.

“Large enterprises can afford to build a large data center and bring in tens of thousands of CPU cores, GPU cores,” Saha noted. “But it is difficult for smaller enterprises to have access to that amount of computing power, which is very important for data science. Cloud makes it easy. It has in many ways democratized the use of data science.”

Google’s approach will get no argument from the enterprise world. In a study conducted by the Harvard Business Review, sponsored by Google Cloud, 97% of company leaders agreed that democratizing access to data and analytics across the organization was important to business success.

The same HBR report also found that 72% of leaders viewed the ability to automate data-driven insight with machine learning built into workflows as “extremely important” to organizational performance. Google has taken that sentiment to heart by releasing BigQuery ML in 2018 and Data QnA, an AI service for business analytical queries, earlier this year.

Whether it involves the use of SQL or natural language processing, Google wants people to feel at home with whatever tools they are most comfortable using for data analytics.

“With BigQuery ML, we have made it possible for our users who like SQL to use the power of machine learning without having to learn anything else or without having to move their data anywhere else,” Saha said. “What QnA allows you to do is to use natural language on top of BigQuery data. If you can do that, it is the nirvana where someone working in a call center talking to a customer can use a simple query to figure out what’s happening with their bill.”

Pockets of strength

Will Kurian’s game plan for Google Cloud pay off? The company has weathered the economic storms of 2020 by landing a number of large customers, with Nokia, Major League Baseball and the Indian technology giant Wipro Ltd. all signing up in recent months. That lends credence to those who believe that Google is finally starting to figure out how to sell to the enterprise.

In addition, customer survey data from the summer revealed pockets of strength for Google Cloud, with improved market position for database and analytics. In a time when even a three-month forecast may seem ambitious, Google is betting that enterprise customers will need to see what lies ahead now more than ever.

“Pretty much every business is leaning heavily on their data infrastructure to gain insight into what’s coming next,” Saha said. “A lot of the models that people are used to are no longer valid, things are changing very rapidly. In order to survive and thrive, people have to lean on data, lean on analytics to figure out what’s coming around the corner.”

Here’s the complete video interview with Saha, one of many Cube Conversations from SiliconANGLE and theCUBE:

Photo: SiliconANGLE