More data, more ways

As digital commerce and footprints have become foundational for success over the past five years, we’ve experienced exponential growth in clientele. Keeping up with the unique analytics requirements of each client has required a fair amount of IT agility on our part. After outgrowing spreadsheets as our core BI tool, we adopted a well-known data visualization app only to find that it couldn’t scale with our growth and increasingly complex requirements either. We needed a solution that would allow us to pull hundreds of millions of data signals into one centralized system to give our clients as much strategic information as possible, while increasing our efficiency. After outlining our short- and long-term solution goals, we weighed the trade-offs of different designs. It was clear that the data replication required by our existing BI solution design was unsustainable.

Previously, all our customer-facing teams created their own insights. More than 200 consultants were spending hours each week pulling and compiling data for our clients, and then creating their own custom reports and dashboards. As data sets grew larger and larger, our desktop solutions simply didn’t have the processing power required to keep up, and we had to invest significant money in training any new employees in these complex BI processes. Our ability to best serve our customers was being jeopardized because we were having trouble serving basic needs, let alone advanced use cases.

We selected Looker, Google Cloud’s business intelligence solution, as our BI platform. As the direct query leader, Looker gives us the best available capabilities for real-time analytics and time to value. Instead of lifting and shifting, we designed a new, consolidated data analytics foundation with Looker that uses our existing BigQuery platform, which can scale with any amount and type of data. We then identified and tackled quick-win use cases that delivered immediate business value for our team and clients.

Meet users where they are in skills, requirements, and preferences

One of our first Looker projects involved redesigning our BI workflows. We built dashboards in Looker that automatically serve up the data our employees need, along with filters they use to customize insights and set up custom alerts. Users can now explore information on their own to answer new questions, knowing insights are reliable because they’re based on consistent data and definitions. More technical staff create ad hoc insights with governed datasets in BigQuery and use their preferred visualization tools like Looker Studio, Power BI, and Tableau. We’ve also duplicated some of our data lakes to give teams a sandbox that they can experiment in using Looker embedded analytics. This enables them to quickly see more data and uncover new opportunities that provide value to our clients. Our product development team is also able to build and test prototypes more quickly, letting us validate hypotheses for a subsection of clients before making them available across the company. And because Looker is cloud based, all our users can analyze as much data as they want without exceeding the computing power of their laptops.

Seamless security and faster development

We leverage BigQuery’s access and permissioning capabilities. Looker can inherit data permissions directly from BigQuery and multiple third-party CRMs, so we’ve also been able to add granular governance strategies within our Looker user groups. This powerful combination ensures that data is accessed only by users who have the right permissions. And Looker’s unique “in-database” architecture means that we aren’t replicating and storing any data on local devices, which reduces both our time and costs spent on data management while bolstering our security posture.

Better services and hundreds of thousands of dollars in savings

Time spent on repetitive tasks adds up over months and years. With Looker, we automate reports and alerts that people frequently create. Not only does this free up teams to discover insights that they previously wouldn’t have time to pinpoint, but they have fresh reports whenever they are needed. For instance, we automated the creation of multiple internal dashboards and external client analyses that utilize cross-channel data. In the past, before we had automation capabilities, we used to only generate these analyses up to four times a year. With Looker, we can scale and automate refreshed analyses instantly—and we can add alerts that flag trends as they emerge. We also use Looker dashboards and alerts to improve project management by identifying external issues such as teams who are nearing their allocated client budgets too quickly or internal retention concerns like employees who aren’t taking enough vacation time.