Pacific Life—an insurance industry leader for over 150 years—employs many brilliant data scientists. But, before the company’s move to the Snowflake Data Cloud, their skills were underutilized. Instead of spending the bulk of their days building data science models and analyzing the results, they had to hunt down and wrangle data.

“It’s not fun—to a data scientist, it’s busy work,” says Paul Pohoresky, a Slalom director. “It’s moving data and loading data and manually cleaning it up. It’s draining and time-consuming. With new tools and solutions, the data scientists can concentrate on more high-level, valuable tasks.”

Slalom’s work with Pacific Life evolved from helping with reporting and supporting the company’s Salesforce implementation to recommending and then implementing a new data infrastructure solution.

As Slalom worked with the business to roll out Tableau, hindering issues emerged: data was scattered; it wasn’t loading quickly; metrics and business logic were embedded in an old reporting platform; and there were firewall issues. And all the different data sources and formats meant it wasn’t normalized for ingestion.

“The amount of effort and integration required just to get the data ready to feed the Tableau dashboards was a huge risk,” says Pohoresky. “This issue created an opportunity for us to present Pacific Life with new technologies.”

Powerful, user-friendly dashboards require a back-end infrastructure that can support them. “The integration and the transformation of data that’s taking place in the background is so much deeper than people realize,” says Pohoresky. “It’s like an iceberg. You see the tip but underneath it’s so much deeper.”

Slalom provided the Pacific Life data science team with a data and analytics strategy and a roadmap to move to the new data cloud. Slalom also helped the data warehouse team move their on-premises Oracle database to Snowflake—and supported the journey every step of the way.

Initially a small proof of concept, the project was so successful that it blossomed into a pilot. Then, other businesses within Pacific Life took notice, leading to widespread adoption throughout the enterprise.

Part of Snowflake’s value lies in its ability to connect with other tools. Teams are using Tableau and Power BI for data visualization, plus AI-enabled tools, such as Appian, Alteryx, RStudio, and H20 Driverless AI for data science and analytics. The variety of tools allow business users of all abilities to gain value from the solution. Some apps are low code or no code, while others require more technical know-how. All users could employ familiar tools with the new data cloud—which runs on Amazon Web Services—and gain deeper insights faster.

Now, instead of wasting time hunting down siloed data, data scientists meet 80 to 85% of their data needs with Snowflake. In addition, users can trust the data because they know where it originates. They’re not asking themselves if the data is raw, or if it’s been updated or manipulated—that confusion is gone.

With the legacy platform, when people needed to produce month- or quarter-end reports, there could be up to five teams “trying to hit the same data sets,” says Kurpal Sandhu, Pacific Life’s Director of Data and Visualizations. “And whether it’s with queries, reports, or processes, they were all waiting for each other because each process was blocking the other process.”

But now? “Concurrency issues are gone. Latency issues are gone. Resource constraints are gone,” says Slalom data and analytics principal Sharadhra Sandur, who supported Pacific Life on its journey. Now Pacific Life has a data infrastructure with infinite scalability—one that can handle multiple users hitting 65 billion records.

As part of the data transformation, Slalom worked with Pacific Life to consolidate, evaluate, and structure years of siloed data to “create a single data site that can cater to multiple use cases,” says Sandur. “The entire organization is seeing the value in it and all of them are making Snowflake their enterprise database system,” she says.

There isn’t room to report on all the wins and efficiencies this transformation has brought to Pacific Life, but here are some notable examples:

• Traditional database administrator (DBA) resources can get redeployed, because many of the traditional DBA duties—the historical backups, loading, indexing, and tuning—are eliminated.

• With role-based access control and reusable data pipelines, teams can access the data with built-in security and less restriction.

• Data scientists, data analysts, and business users have their own workspaces in which to experiment and innovate—even with data that’s in production. Senior data scientist Olaf Menzer, part of Pacific Life’s Decision Analytics team, says the workspace helps him prototype new data views: “Before, I’d need to involve a data engineer and ask him to stage it.” This unique workspace feature isn’t part of traditional databases, and it’s a playground that has liberated data scientists and business users alike.

• Snowflake gives the actuarial and finance teams the ability to run more—and faster—scenarios. They’re more closely connected to potential changes in the market than they had been because now “they can run queries in a matter of minutes as opposed to hours,” says Sandhu. The teams get a daily Tableau dashboard of the active contracts and assets they manage. Before the upgrade, those views came on a quarterly basis.

• Pacific Life went from four active Tableau users to 450 people actively looking at the dashboards, with 170 developers creating them. Members of the sales team receive a daily dashboard of their sales compared to their goals, and the sales leadership team has views that give them insights into daily, weekly, and monthly sales trends. Before, the data only captured the prior 13 months—but now, they can course-correct much sooner.

The company's motivation for the move to a leading-edge platform wasn’t just to introduce efficiencies. Talent development and retention were also part of the plan. Tech talent is spare, and “Pacific Life is competing with Amazon, Google, and consulting firms,” says Sandhu. “The data science team really had an opportunity to flourish because they’ve been given easy access to data and AI tools like Tableau and Driverless H20.”

Sandhu has deeply appreciated Slalom’s technical talent. “They’re some of the best professionals that we’ve worked with,” he says. “Absolute first-class. And the supporting organization behind those individuals has been excellent. We’ve got a really good partnership with Slalom because they have really good resources and they’ve always been helpful to bring more people in to help educate the team or clarify questions and issues that we run into. It never feels like we’re being sold something. It always feels like there are opportunities for us to be educated.”