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Case Study: Molo

Implementing analytics stack without engineers

Jul 13, 2022

30% reduction in time from application to offer for Molo customers.

£70,000 saved per year on data engineering resources to develop and maintain pipelines and related infrastructure.

Built a functional MVP data warehouse within a scope of weeks, and a rich data warehouse with hourly updates within the scope of months.

About

Molo is the UK’s first digital native mortgage lender. Since launching in 2018 Molo have used technology to deliver simpler and faster online mortgage lending to make homeownership easier for everyone. They have led the change in the UK mortgage industry, delivering online mortgages to customers with both speed and efficiency.

Industry: Financial Services

Employees: 150+

Website: molofinance.com

We needed a solution that offered the core foundations and enabled everyone to answer business questions in the most efficient way, without having to hire data engineers to solve common ETL problems or building a complex infrastructure to do so. Kleene has provided a simple and efficient solution which connects our data and provides every function with greater insight. The time saved through the automation of reports has been phenomenal – before we started automating reports, it was a nightmare. On a service level, the responsiveness and support we have received from the team has been amazing!

Alvaro Zubizarreta Lopez – Head of Data Analytics

The Challenge

Having previously used a production database for reporting and analysis, the Head of Data Analytics required a more analytics friendly infrastructure that would provide reliable insight and accurate data to all teams, in order to answer critical business questions. 

The process of building the solution internally would have been lengthy and complicated, particularly as mortgage products have a complex data infrastructure. With limited resources available, the team sought a simpler route. Molo transitioned to a modern analytics infrastructure with the implementation of Kleene. 

Now, with a modern analytics stack, self-serve dashboards provide insight to all functions and ensure employees across the business are data-driven. Furthermore, the data surfaced can be used to make projections and fuel strategic decision making.

The Solution

Kleene worked with Molo to eliminate their data silos and generated automated reports for performance and KPI measurement. Normalized sets of data have been generated, removing the previous requirement for SQL query to be written in Redash. Self-serve dashboards enable anyone in the business to access required data. 

Kleene built a robust, purpose built data infrastructure the team can use to access granular data, and optimize performance and efficiency. 

The Results

Building a robust data infrastructure without engineers 

Building a data analytics infrastructure from scratch can be a complicated, lengthy and costly process. The Head of Data Analytics required a robust data infrastructure, manageable in house, without a large financial investment. 

“The beauty of Kleene is that you can run a data and analytics function without having to invest enormous amounts into engineering. The quick and efficient setup enabled us to move ahead in a lean manner and answer the business questions in the most efficient way. The other option would have been to hire a data engineer early on, which would immediately incur a bigger cost. Being in the position to show the C suite that we can get the data we need whilst spending half of what we expected, is great. Data engineers can provide great benefits to a company when focused on complex high value projects and bespoke solutions, but we should not be reinventing the wheel when retrieving data from common databases or APIs.”

Modernizing reporting 

Top level reporting was in place, however the process was lengthy and inefficient. Due to the lack of granularity, the team were unable to access key information to answer critical business questions. Modernizing the reporting has provided clear visibility on performance at a weekly, monthly and quarterly scale, without the level of effort that was previously required. Now, performance versus targets has been formalized and can be measured in an automated fashion.

“The pain of manual work was immense, with teams reconciling by hand, which was open to human error. Before, the marketing team had to start early on Mondays so that they could present at 9am. Now, all of the data is pulled overnight and the reports are automatically produced, ready to share in weekly performance meetings. It’s absolutely fundamental to be able to execute performance measurements in a consistent and repeatable way, as frequently as we need. We’ve been able to craft performance monitoring that before was frankly impossible to do.”

Driving critical business insight 

With a robust, purpose built data infrastructure by Kleene, molo can answer more complex questions, beyond the performance of KPIs. Now, they can drill down into a more granular view and use that data to optimize performance and efficiency. Each function can access the data they need for insight and utilize it to understand their performance and make projections. 

“Now we can understand what’s going on in the cogs of the machine. By connecting the dots and bringing all of our data together we can understand the root cause of issues – for instance which products trigger the most customer service contacts. We’re also using data to view the consequences if we were to make a change, running simulations and prediction models to see what the impact could be if different rules were applied.”

Democratizing data 

Prior to the implementation of Kleene, Molo struggled to answer business questions, because to do so, a SQL query had to be written in Redash. Now, with a normalized set of data, tables can be queried to reveal the answers to business questions, without anyone else in the business needing SQL skills. The self-serve dashboards powered by Redshift and kleene enable anyone in the business to access the precise data they need and answer the questions they have. This allows teams to drill down to the specifics, without technical support. 

“Numbers tell the truth of the business and I don’t want people getting different answers. Now, on my end, I can just query a table and get the answer to questions that arise, and on the business side, I don’t expect anybody to write a single line of SQL. It would be a failure if I’m forcing the C suite to write SQL.”

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