BigQuery Through The Eyes of Looker

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BigQuery Through The Eyes of Looker
BigQuery Through The Eyes of Looker

By now everyone with a stake in big data – which is probably just about every business person you can imagine – has heard of Google's introduction of BigQuery, its online data transfer and management solution, announced at last month's Cloud Next conference.  

But to understand how far reaching the implications of the announcement are, take a look at Looker, a data analytics startup.  The company launched Looker Blocks, a BigQuery plugin in 2015. The transfer service raises the profile for Blocks and its parent company.

Looker just announced $81.5 Million In Series D Funding from CapitalG, an Alphabet (Google) growth equity investment fund.  The announcement is a cherry on top of a banner year in 2016, nearly tripling revenue while adding a variety of almost 400 new customers such as IBM, Hewlett-Packard, The Economist, Nordstrom's, and Indiegogo.  

I had the opportunity to put questions to Daniel Mintz, Looker's Chief Data Evangelist, about the impact of Google's announcement and how Looker is positioning itself among business intelligence solution providers

The big news from Google Cloud Next is how Google has made it easier for companies to transfer data to BigQuery. Where does Looker Blocks fit with Google's announcement?

One of the best things about Google's new BigQuery Transfer Service is that it makes data from all of Google's marketing products — including Google Analytics, DoubleClick, AdWords, and more — available in one place. Looker's Blocks for this data mean customers can start exploring the data immediately, since the Blocks provide plug-and-play data models and best-practice analysis patterns for each of these data sources. The two products combined mean data from any Google marketing product is queryable by business users in a full-fledged analysis tool suite in hours, rather than weeks.

You first launched a series of Looker Blocks for Google BigQuery back in 2015. Fast forward to now: What are some lessons learned from that launch that's helped development for Google's BigQuery news?

One of the things our founder, Lloyd Tabb, recognized early on with BigQuery, is that it's enormously powerful and cost-effective, so tools that bring their own analytic engines don't make a lot of sense with BigQuery. Because Looker executes SQL queries directly against the database (rather than extracting it), we're in a unique position to take full advantage of the raw processing power of BigQuery. Our native integration also means that as BigQuery improves--rolling out features like Standard SQL, user-defined functions, and compatibility with Google Sheets--Looker users immediately get all those benefits.

Martech has blurred the boundaries and responsibilities between marketers, IT, and developer teams, raising discussion on who is responsible for analytics initiatives in an organization.  How has this influenced the kinds of solutions executive teams are seeking, from Looker's viewpoint and experiences?

Marketing technology has come a long, long way. In the most productive organizations, everyone is using data to inform and improve their decisions. Executives recognize the competitive advantages this brings, so they're seeking solutions that allow all their users (including themselves) to find whatever data they need, whenever they need it. They want users to be able to ask and answer questions themselves, without needing to wait for IT or a data team.

How has LookML — your tool for querying SQL databases — performed in attracting interest among marketers compared to other databases?   Has Looker gained developer interest in the same manner that Amazon, Microsoft, and Google have raised for their open source software?  

LookML is a way to translate each business' metrics into language that their database understands — a way to give their data consistent and trustworthy meaning. Looker supports 33 different dialects of SQL, which makes it extremely attractive to marketers and developers alike, since Looker allows them to choose whatever database makes the most sense for their business. And because LookML is a code-based language, developers and analysts who already speak SQL can pick it up in a few hours. LookML makes it simple to get the knowledge about what the data means out of analysts' heads and into software that scales seamlessly. Plus, it brings flexibility, version control, and governance to SQL--all things that SQL has historically lacked. 

Marketers love our approach because their analysts define the things they care about — clickthru rates, channels, attribution rules — once in Looker and then non-technical users can access all that knowledge through a point and click interface. As customers and users have built amazing things with Looker, they've fed that knowledge back to the community and we've codified some of their best practices in the Looker Blocks we've released.

How has the database LookML played a role with Looker Blocks? 

LookML is the language that Looker Blocks are written in. Because the new Looker Blocks are built specifically to work with BigQuery's new Data Transfer Service, we expect that all of the customers who adopt them will be using BigQuery. But most other Blocks, since they're written in LookML, are dialect agnostic and can be used with whatever database the customer is already using.

Have there been surprising discoveries in how clients discuss data and make decisions while using Looker solutions?

Our customers are building true data cultures where everyone gets data from a single source of truth and no one has to wait to get the data they need. What's amazing to see is that data has gone from a nice-to-have to a must-have at a lot of these companies. Everyone is expected to back up their opinions with facts and encouraged to dig in and not only ask what happened, but why and how things happened. What's most surprising is seeing how deep and wide this mentality has spread.

You launched a Lookbot for Slack.  From Looker's experience with clients, what lessons in communication within organizational teams guided the development for the bot? 

We know how critical good data can be for helping people make better decisions. But we also recognize that when data is hard or slow to access, business people will often just skip it. That's why we think it's so important to make data available to them right in the environments where they're working. The Lookerbot for Slack allows users to ask questions of data and get answers without ever leaving Slack. We also make it easy to embed charts and dashboards in other business tools like Salesforce and have comprehensive options for users to get data delivered to them — by email or SMS or FTP — on a schedule. By putting data right in users' existing workflows, Looker makes it much more likely that they'll use it to improve their decisions.

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