A Property Marketplace
Data Platform Case Study

Industry: Real Estate
How we helped: Data Platform

Key Takeaways

Single source of truth for data analytics

Self-service analytics through access controlled user profiles

Flexibility in managing storage and compute resources

About The Company

Founded in the early 2000, this property marketplace has been regarded as the leading prosperity marketplace in Switzerland, with more than nine million visits per month and over 110,000 properties currently on offer.

At its headquarters in Zurich, as well as its additional offices in Switzerland, the company’s experts work in the areas of digital real estate marketing and financing. In addition to product development, website development, database management, and sales and marketing initiatives.

 
Together with the Zürcher Kantonalbank, this marketplace offers an online mortgage available everywhere in Switzerland.

“Automation led to all semi-structured data flowing through the platform to become accessible to all, with some core data sets having near real-time latency. This provided decision-makers with the right data at the right time. Improving business intelligence capabilities to drive forward business value,”

Mike Dixon

Director, 101 Ways 

The Challenge

Whilst the online real estate specialist’s data warehouse had successfully served all the company’s analytics and business reporting for nearly two decades, over time it had become very specialised and denormalised in structure. This created challenges when trying to add on additional reporting, leading to questions within the business over its accuracy.


“The data team responsible for the data warehouse’s maintenance and new features was small and often distracted by ad hoc requests. This resulted in all data requirements for product experiments and analysis becoming blocked by the data team,” explained Mike Dixon, Director, 101 Ways.

The Solution

101 Ways designed and built a scalable data platform for this company, using modern data warehouse technologies such as a cloud native approach, Matillion and Snowflake. This gave users of the data platform access to all trusted data sets whether published by the data team, collected by the feature teams, or curated by analysts. This facilitated better collaboration and transparency of data between business functions and the technology teams.

“Automation led to all semi-structured data flowing through the platform to become accessible to all, with some core data sets having near real-time latency. This provided decision-makers with the right data at the right time. Improving business intelligence capabilities to drive forward business value,” added Dixon.

Further, the core data sets were remodelled as star schemas, enabling the data to become more generalised and flexible for future reporting requirements.

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