You have a long track-record of successfully working in and managing diverse Product development teams. How did you arrive at Looker and what attracted you to the BI ecosystem?

A database class during my sophomore year in college inspired me to pursue a career in Data and Analytics. The idea that information can be organized and used to make rational, fact-based decisions has always appealed to me personally. I’ve been in this industry for 20+ years so long enough to know when a company is moving in the right direction. Joining Looker was a no-brainer. It’s a company with whom I share a few core beliefs: that if we want to help companies become data-driven, we must design software for everyone, not just data experts; that a dashboard is not the solution to every data problem, and that data has the power to transform the way we live and the way we work in positive ways.

How much has the BI Tech industry evolved since you first started? 

I started my career when the term Decision Support System was still popular. Modern BI has really come a long way from those days of just providing querying and reporting. Today, companies are realizing that data doesn’t just have informational value, but also operational and economic value. Companies are infusing data into business workflows, creating valuable data-driven experiences such as automatically presenting a discount offer to a customer that’s likely to churn, automatically adjusting bids for under- or over-performing online ads, or using natural language to ask about inventory levels in Slack and ordering additional units based on the answer. BI is no longer designed primarily for data-savvy experts, modern BI can be accessible by anyone who needs data in their everyday business-oriented environment.

Tell us about the unique features of Looker 7 — how does it tie-in customer data to a unified data analytics platform for all decision-makers? 

Looker 7 helps companies transcend traditional BI and enables them to deliver data-driven experiences across their business. This is accomplished through three key areas: a new development framework for building powerful tailored data solutions and a Marketplace in which to discover and deploy them; reimagined BI experiences out-of-the-box that improve collaboration and data flow across common business tools such as Slack, GSuite and more; and an enterprise-grade architecture designed to support the most demanding workloads securely and at scale.

How do you see BI platforms evolving into a more integrated tech stack for Marketing, Sales and Customer Analytics teams? 

The MarTech landscape continues to grow. There are over 6,500 MarTech tools an organization can choose from, and research shows that the average organization uses 1,100+ SaaS apps. Bringing all these systems together and deriving tangible value from all this data is a challenge all companies face today. BI platforms can empower people to make data-driven decisions that move the business forward, as well as help companies discover new revenue streams through data monetization opportunities. Looker 7 opens valuable new opportunities for companies to obtain a more comprehensive understanding of their business, infuse data into everyday business workflows, and bring new data products to market.

Google acquired Looker recently; while Salesforce acquired Tableau. What roadmap do you foresee for Looker and other BI platforms?

We will continue our focus on Multi-Cloud. This means giving companies flexibility when choosing how and where to deploy their BI environments. This flexibility avoids costly migrations, reworking of business logic, and months of effort. The modern Looker data platform supports a wide range of databases, 50 SQL dialects, and multiple Cloud hosting options. Looker’s Multi-Cloud approach allows customers to meet the unique needs of their business in the ways that work best for them.

Could you tell us one outstanding use case scenario that helped you scale to next-level BI offerings?

An example that immediately comes to mind is with our customer PopSockets. The company needed a data platform to support their global finance team. As a fast-paced and growing company, they needed a solution that would scale with their business. And it needed a tool that could not only deliver data access to everyone in the organization but could also be governed, modeled, and defined to fit its operational needs. After evaluating other tools, PopSockets implemented the Looker data platform.

PopSockets experienced an overall company growth of 70,000 percent over a three-year period. By providing employees with insights that allowed them to do their jobs as efficiently and effectively as possible, PopSockets is able to streamline their global finance process close time nearly in half and drive the success of its charity program.

LookML, Looker’s quick and powerful SQL data modeling language, allowed the PopSockets’ analytics team to define its data model and rapidly iterate on new business needs. Now Marketing, Product, Finance – nearly every team at PopSockets uses Looker to ask the right questions of their data and make informed business decisions.

How does Looker help to bridge this technology gap across geographies and industries that you currently sell to?

Looker prides itself on helping customers choose the data stack that best serves their specific needs across borders and industries. The Looker platform is localized across a variety of languages, and its unique Multi-Cloud architecture lets our customers deploy in the best way for their particular geography.

Now speaking 50 different dialects of SQL, Looker is compatible with the most popular modern database and data warehouse technologies. The latest database integrations from Looker include Actian, Avalanche, BlinkDB, Mongo, and Vector.

Looker also allows customers the choice to have their instances hosted on Amazon Web Services (AWS) or Google Cloud Platform (GCP), with plans to offer Azure hosting in early 2020. And, as always, anyone can self-host. At Looker, we continue to invest in capabilities that enhance our Multi-Cloud approach across borders—as well as improving interoperability with a wide ecosystem of technologies.

What are your predictions for the role of Embedded Analytics, Big Data and BI tools all coming together at Looker BI? How do you see AI capabilities leading to quicker adoption of BI tools?

The data and analytics technology landscape is undergoing a profound transformation. Data is no longer isolated in a single monolithic software suite. It is spread out across multiple applications in the Cloud. Modern databases are more powerful, faster and cheaper. The traditional ETL paradigm is giving way to data transformation on demand. But it’s not just the technologies that have evolved, we’re also seeing a fundamental change in the way companies use data. Forward-looking organizations no longer see data simply as something that’s displayed on a screen to be analyzed. Companies today are integrating data into everyday operational workflows. They are embedding analytics into business applications to deliver richer experiences for their customers. They are also building and bringing new data products to market that create new revenue streams.

When it comes to the future of AI, products such as Looker play a huge role as it gives people the power to unearth the insights needed in their data for AI models. Machines are not going to be able to tell what’s wrong or what’s right, but can be instructed to service the wrong correctly when provided with the factual data. There has to be a clear starting point and expectation.

What Marketing-related challenges do you meet every day? How do technology and collaboration tools help you to overcome these? 

Data and analytics is a very crowded space, which means buyers are confronted by a lot of noise when evaluating solutions. From a Marketing perspective, not only is it important to have crisply differentiated positioning, but also to measure the success of our messaging. Data is at the heart of all we do at Looker, so of course we are very data-driven when it comes to testing certain messages, but we’ve experienced incredible success when creating opportunities for face-to-face connections with current customers and prospects. The human connection we value at Looker is often our X-factor. Our annual conference JOIN is a great example of this and why we’ve made it an international roadshow with JOIN: The Tour. We understand that our customers want to engage with other data-focused peers and we’ve curated these events to allow our team, customers and partners an opportunity to create those human connections.

Which Marketing, Sales, and Customer Support technologies do you leverage at Looker? 

Talking specifically about my function, Product Marketing is really underserved when it comes to technology solutions, relative to other Marketing functions. There are some helpful tools that we’re leveraging, for areas like competitive intelligence, for example. And there are some newer solutions we’re evaluating for areas like messaging and positioning, and for voice of the customer. But what we use the most is our own product, Looker. Our entire Marketing department uses Looker every day to understand our business. We look at campaign analytics, metrics like cost-per-click, click-through rates, ROI. We analyze customer acquisition data, like conversion rates across different channels. Using Looker, our team delivered 66% of the company’s net new revenue through Marketing programs such as organic search, pay-per-click, and events, and our contribution to new business increased by 50% while our budget only increased by a fraction of that.

How do you prepare for an AI-centric world as a Marketing leader? 

The future of AI holds amazing promises for all areas of business, marketing included, but as mentioned before, at the core of AI is data. Data products are at the root of all this as they provide the collection, integration, analysis, and presentation of data – solving incredibly complicated challenges that make it possible for AI to leverage all of that data and insight, applying algorithms and models to do the “cool stuff.” Data is the new currency of business, and AI can’t work without that currency being constantly collected, integrated, analyzed and fed into the AI models – which is why in preparation for an AI-centric world it’s crucial we get our data right first.

The markets are validating this with massive data companies understanding the importance of being able to collect and analyze data anywhere it lives, in real time – to deliver on the promise of both AI and ML down the road.