August 13, 2021

Implementing the foundations of an analytics stack without engineers

  • Industry Finance
  • Location London, UK
  • Data Stack kleene, Redshift, Tableau
Molo case study | kleene.ai

Molo, the UK’s first digital mortgage lender, transitioned to a modern analytics infrastructure with the implementation of kleene.

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.

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. 

Challenge

Establishing automatic reporting to deliver insight, without high engineering costs.

Solution

Elimination of data silos and generation of automated reports for performance and KPI measurement.

Outcome  

Democratising data to build a data driven business with increased speed to insight.

 

Results 

  • Building the core of a robust data infrastructure without engineers 

Building a data analytics infrastructure from scratch can be a complicated, lengthy and costly process. Given the stage of growth of the business, the Head of Data Analytics required a robust data infrastructure, manageable in house, without spending large sums of money building the basic data warehouse infrastructure.

“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.”

  • Modernising 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. Modernising 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 formalised 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.”

  • Answering business questions

With a robust, purpose built data infrastructure, the team can answer more complex questions, beyond the performance of KPIs. Now, Molo can drill down into a more granular view and use that data to optimise performance and efficiency. Each function can access the data they need for insight and utilise 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.”

  • Democratising data 

Prior to the implementation of kleene, the team struggled to answer business questions as in order to do so, a SQL query had to be written in Redash. Now, with a normalised 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.”

Molo Testimonial

“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!”

kleene.ai