Industry
Retail Insurance
employees
100+
website
https://laka.co/gb/
About
Laka is a London-based insurance company offering crowd-based policies to cyclists in order to rival traditional premiums and has been voted ‘Best Cycle Insurance Provider’ for four years running. Laka offers a customer-focused model of collective cover, based on flexible payments that rise and fall in line with claims volume in order to provide best value by only paying for the true cost of cover.
6 FTE days/week saved
> £70k per annum saved
Curated Self-Serve
Challenges
Laka needed to change the way they worked with data. To move away from working on single, isolated data projects focusing on individual needs within each of the departments and instead implement a cohesive strategy in which data could be well organized, reliable and easy to access for everyone’s needs.
With data siloed across multiple different systems, a claims management system running on PostgreSQL and multiple separate SaaS API data sources such as commercial billing on Xero, payments via Stripe, multi-currency exchange rates and marketing via Facebook, bringing all this data together without Kleene would have meant a multi-month engineering project and a lot of ongoing management.
With the Kleene platform in place, Laka were able to set up automated data pipelines to power both daily reporting and self-serve around products, policies and claims.
Solution
In Ben’s experience, people who are not data specialists will often gravitate towards the latest buzzwords in technology, which lately lean in the direction of high-availability, low-structure types of data resources (e.g. data mesh, datamart, data lake etc.).
Ben feels that for organisations of a similar size to Laka, or even an order of magnitude larger, a consolidated, structured approach to data will net the best results – which is very much the data warehouse paradigm. He notes that the fact that it is well modelled makes it much easier for people who are less data-literate to interact with and that this is especially true around a single source of the truth.
Without one, a simple metric can have an entirely different meaning across departments which are both technically correct, but do not align.
"Having a good warehousing stance means that analysts and data scientists can spend way less time organising data and more on extracting good, actionable insights from it”
We would have had to have figured out another way to answer data questions because that’s what we would have had the capacity for. That means we lose understanding, which is vital for us to make good business decisions. It becomes an amount of work that is not really possible for us to do at all without a service like Kleene to get us across the line
Ben Fields – Head of Data, Laka
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