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Case Study: Laka

Building a data warehouse to drive strategic business decisions: Laka

Nov 25, 2022

6 FTE days/week saved
50% of all reporting requests available from self-serve.

> £70k per annum saved
in data engineering resource and management time.

Curated Self-Serve
Business stakeholders able to make informed decisions

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.

Industry: Retail Insurance

Employees: 100+

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

The Challenge

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 organised, 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.

The 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”

The Results

Ingesting data quickly and easily

Ben finds that one of the trickiest bits when handling data is pulling that data together from different sources. Kleene handles the connection, extraction and unpacking of data seamlessly, when it would be tedious and complicated to code manually. Laka use Xero for all of the invoicing for their commercial clients. Ben feels that while coding extracts for Xero to pull data in JSON format for each different use case is perfectly tractable, that it is not an efficient use of his time. It would probably take a month of full-time software development to build an initial connector which is time and cost prohibitive for them, and they end up having to find other ways to get the information they require.

“Because Kleene have an off-the-shelf connector, it moves the effort for getting data that we can merge with our model to nearly zero.”

Managing and maintaining connectors

Ben also notes that in previous work environments he has found that ongoing maintenance of connectors to data sources can be problematic. When version changes to the data source occur, they are often not coordinated with the consumers of that data source and that if you do not immediately make corresponding changes – that could add up to a lot of unplanned development effort – that your source of data stops outright.

“By shifting to using one of Kleene’s connectors, we remove that burden from our engineering team, which is great for us”

Transforming data

When looking at transforming the data, Ben finds that being able to do it all in one place is helpful. Without Kleene, he would be performing a similar job, but a lot more manually and across several tools. Whiteboarding the transforms, writing the SQL and then importing that work into a DAG (Directed Acyclic Graph – used for managing dependencies) tool to verify it is all as needed.

“I find that to be able to mint transforms directly from the SQL console is a really useful way to pragmatically work through modelling decisions”

Self-service reporting – saving time

One of the main goals for Laka’s data warehouse project was to reduce the volume of inbound report requests into the data team by enabling self-service reporting for their users. It has been running for just under a quarter now and early signs point to fantastic adoption by the wider business, freeing up time for the data team to focus on the more complex queries that will drive greater business value.

6 FTE
days/week

“I find that to be able to mint transforms directly from the SQL console is a really useful way to pragmatically work through modelling decisions”

Operating at a higher level

Ben finds that there is a tremendous amount of utility in everyone being able to access reliable measures of business metrics and their change over time. These are some fairly simple calculations but were difficult to get an answer to prior to the project with Kleene. With this data available to users throughout the organisation, decisions can be made without having to wait for the data team to compile new reports. Having the Kleene platform in place saves time and allows the business to function at a higher level.

Curated
self-serve

“With the ad-hoc reporting in place, we can see the early shoots of where we can put together interventions that will save us significant money.”

Running a lean data team

With the Kleene platform, all of the manual work around data extraction and building a data warehouse can be fully automated, freeing up time to drive a data strategy that can be transformative for the business.

6 FTE
days/week

“Kleene easily saves us from having to hire a whole data engineer and at least a day out of every week of my own life that I can now spend in other places.”

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