Group 1001 Transforms ‘Old School’ Insurance Industry With Data

In an industry notorious for being slow to modernize, Gu Xie is on a mission to innovate, fast. Gu leads a team of four at insurance holding company Group 1001, focusing on the annuities side of the business.

“The insurance industry is old school, on-prem, slow to react,” says Gu. “Because Group 1001 is a technology-first, forward-thinking organization, we’re trying to foster innovation and bring new modernization approaches to the insurance industry to fundamentally transform it.”

That transformation is off to a trailblazing start. By modernizing Group 1001’s data stack with Snowflake, Fivetran, and Coalesce, Gu says his team’s productivity is up 10x, now able to go from idea to insight in just two days, compared to as long as three months with the company’s old legacy solutions.

The original data stack was neither efficient nor reliable. “We had Airflow processes, we would take data using Python to extract them from the APIs, land them to a Postgres data store, and then build our Power BI reports off of that,” Gu recalls. “The problem was that it often took five hours to load those processes, and there was a fundamental lack of version control across each of those steps. As a result, whenever issues occured—and they inevitably would—it took seven hours to repair, rerun, and correct.” This essentially ate up the business user’s entire workday.

Gu and his team work closely on the actual policy management system for the annuities business across all Group 1001 brands. His main initial goal was to create a unified, top-down view showing the total assets under management by the annuities business in the Group 1001 organization, broken out by company, by product line, and by customer. But this proved impossible given that there was no data modeling in place. “All reports were being run by ad hoc views that were being vetted for each of these data sets,” he explains. “There was no modeling to create a standard dimensional effect layer to report all of the assets within the organization.”

Another important aspect of the business is the operations side of provisioning the policies sold to customers, which involves heavily regulated paperwork: collecting documents, getting signatures, conducting the KYC/AML analysis, and more. But according to Gu, the company had no visibility into this process from the start of the sales funnel through to the end. “When we first started a new project to help address this issue, there was an expectation on SLAs for policy issuance,” says Gu. “What we discovered is that it actually took over 3 times longer on average.”

From a business operations standpoint, with no insight into the backlog, the company couldn’t staff properly to accommodate the influx of policies. “You can imagine if I’m a customer finding out that my policy took longer than expected to issue, I’m not going to be very happy,” he says. “Not having the right data to address those concerns was ultimately impacting customer satisfaction and preventing us from scaling to the demand for annuity policies.”

Gu’s team interfaces primarily with two large financial firms under the Group 1001 umbrella: one sells policies to consumers via financial advisors, and the other uses a direct-to-consumer model in which customers create their own annuities policies directly from the website. “We needed to create a unified view of all policies within the organization, all of the transactions, customers, and balances across the board for the products that we serve within the organization,” he says. “That’s why we kicked off the POC with Coalesce, in order to create a model to address that need.”

In his previous organization, Gu had rolled out a code-first data transformation solution, but onboarding and training the developers to use it had been a huge lift. “I simply didn’t want to do that anymore. That’s why we chose Coalesce, for the fact that I didn’t need a large team of data engineers, data modelers, or data analysts to build these data models from scratch—I just needed one person.” In a few weeks, the team had already built the entire data model, creating a holistic picture across all of the organization’s data. “Now we can proceed with operationalizing this end-to-end, and start transitioning and rebuilding our reports on top of this new unified data model.”

Coffee with Coalesce:
Listen to our podcast interview with Gu Xie, who shares the secret to building highly performant data engineering teams.

Today Gu’s team has fully redesigned the data stack, which is built around Snowflake as its core data repository: “We’ve completely rewritten all the fundamental processes and the platform from scratch,” he says. “We have Snowflake as our data store, we have Fivetran replicating our pipelines over to Snowflake, and we’re using Coalesce for data transformation.” A number of other tools are in the mix as well, including Soda for data quality, Atlan for the data catalog, and Dagster for orchestration.

“We chose Coalesce because of the fact that using the automation, using the templates, using the very streamlined way of development makes the iteration cycles much faster,” Gu said. “It’s very easy to troubleshoot, to apply changes and refactor the models as a result of Coalesce’s strong lineage tracking capabilities.”