How Landbay is democratizing data with dbt and Looker
Landbayâs vision is to be the UKâs leading buy-to-let lending platformâthey want to be the go-to lender for brokers and the partner of choice for investors. And by all accounts, they are well on their way to achieving this vision. In 2018, Landbayâs 1500% growth over a four year horizon earned it a spot on Deloitteâs âfast 50â awards recognizing the UKs fastest growing companies. In 2019, the 90-person team earned 10 âbuy-to-let lender of the yearâ awards. This is the story of how Landbay is democratizing data to achieve their vision.
Landbay is a buy-to-let mortgage marketplace lender. Borrowers pay interest, and Landbayâs platform passes that interest along to its institutional investors. The team completed its first mortgage in July 2014, began deploying institutional capital in July 2017, and signed a landmark ÂŁ1billion funding agreement in partnership with an established global financial institution in July 2019. To date, Landbay has completed over ÂŁ400 million of specialist buy-to-let mortgages, and has had zero defaultsâa particular point of pride for a team that pays close attention to underwriting quality.
âThe key difference with Landbay is our marketplace funding model,â said Julian Cork, COO at Landbay. âThe marketplace has two sides, the lending and funding. Our vision is to be the go-to lender in this space and a default option for brokers, as well as the partner of choice for those that want to invest in buy-to-let mortgages.â
Data is central to this vision.
Why dbt?
The most pressing need for access to data arose after Landbay onboarded its first institutional investor. The UK requires a critical report for all mortgage lenders called the âBank of England reportâ. This report contains a mortgage lenderâs entire portfolio of loansâwhere the money was loaned, repayment timelines, and portfolio performance. Itâs a massive collection of data. In order for Landbay to work with a greater number of institutional investors, it needed to automate the production of the Bank of England report.
Landbay is built on an AWS microservice architecture that includes 79 databases. The business also uses a variety of SaaS toolsâNetsuite ERP, Segment, and Google Analytics, just to name a fewâto power core business operations. The infrastructure they adopted for the Bank of England report needed to extend to a variety of future use cases as well.
This is when Landbay began evaluating Looker. The deeper they went in the buying process with Looker, the more they began to hear about dbt. âI had heard about dbt before,â said Chris Burrell, CTO at Landbay. âAnd it was mentioned on nearly every customer reference call we did with Looker customers.â By separating the analytics engineering workflow that happens in dbt from the data analysis environment, data teams can ensure that all data served up in the business intelligence tools is accurate, tested, and modeled in a way that is easy to analyzeâeven if youâre not an analyst.
With this setup, the Landbay team was able to democratize data in three key ways:
1. Deliver accurate, tested data to Looker users
With dbt, Landbay can deliver the Bank of England report quickly and, even more importantly, with absolute confidence that every data point is correct. âBefore we rolled out the new setup, we did a massive quality assurance check on every piece of our data transformation logic,â said Bruno Murino, Data Analyst at Landbay. âWe sat down with brokers, lenders, and product managers and asked: âWhat needs to be true at all times?â And then we wrote dbt tests to catch those errors.â
With data models built in dbt, anyone running any report in Looker can be confident that the data is 100% accurate. âIf an engineer makes a semantic change, dbt tests catch that,â said Chris. âAnd we stop the bad data from flowing into Looker: Looker users might see old data, but theyâll never see wrong data.â
2. Add new data sources quickly
Landbay aims to provide a better-than-great customer experience and achieving this requires data. For example, one customer experience improvement theyâve made recently is to speed up the loan approval process by gathering all the data underwriters needed in one place. âUsing a Glue Crawler we can automatically create tables with external data in Redshift, then we clean the data and join it on the postcode using dbt, and finally serve it up to end users in Looker.â Bruno said. âWe have already incorporated land registry data, police data, fire brigade data and geographical data using that method.â Instead of conducting each of these searches individually, underwriters can access the data in Looker and quickly determine how sustainable and affordable a particular loan is.
Opportunities like this exist in every part of the mortgage process:
3. Build a data-driven culture without a massive data team
With dbt, Landbayâs two-person data team is able to meet the data demands of a fast-growing, 90-person company. This efficiency comes from spending their time providing clean, tested data sets rather than tracking down data quality issues or building dashboard after dashboard in Looker. âWith dbt, we can empower users without technology team involvement, and the outcomes for the business are huge,â Julian said. âAll of our dataâbroker payment automation, sales order value reporting, broker performance, marketing campaignsâis served up by dbt and accessible in Looker. As a result, people feel empowered to get the best outcomes for Landbay.â