Harmoney is Australasia’s largest personal loan marketplace, facilitating over $1.7 billion in loans to nearly 50,000 customers in both Australia and New Zealand.
Since its launch in 2014, Harmoney has worked tirelessly to transform the way people borrow and lend money. Their success can be linked back to their innovative way of using data to streamline the application process for their borrowers while also improving the accuracy of credit risk assessments.
To achieve this, however, they needed to ensure they had the right data infrastructure in place.
To compete against major banks and lending institutions, Harmoney needed to innovate and use their data with near real-time analytics and reporting.
To achieve this, they needed several robust data systems including cloud-based data storage, machine learning, and data visualisation tools to better understand their data and use it to improve their processes. And, they needed the systems to work together seamlessly for faster and more accurate results.
They had already implemented Snowflake as their data warehouse solution, and DataRobot to develop and deploy machine-learning models. The piece that was missing was a platform for data visualisation for deeper analysis and to uncover further insights in real-time.
“The best use case is the sales funnel and how making this data available to all allows people who are responsible for various parts of the funnel to focus on improving their part and also understand what’s happening with the wider funnel.”
Miles Davis, BI Manager
One of the key requirements for the data visualisation and analytics tool was that it would integrate seamlessly with both Snowflake and DataRobot. Harmoney had already experienced the complications that arise from siloed data and systems that don’t natively talk to each other – so they wanted to avoid this.
They also needed a system that was future-proof, and able to scale with their business as it grew. Finally, they needed a system that worked for everyone in the organisation and allowed them to easily share reports and insights between teams and departments.
Tableau ticked all the boxes. It has native integrations with Snowflake and DataRobot, as well as supporting and augmenting the work that Harmoney is doing on these platforms.
Combining Tableau and DataRobot means Harmoney can experiment and simulate the machine learning models they’ve created in DataRobot in real-time to identify areas of improvement or determine the next course of action.
Similarly, linking their data warehouse to Tableau means the wider business can dig deeper into Harmoney’s wealth of data to identify opportunities where they could improve or streamline processes. And, as everyone within the organisation has access to Tableau, they’re able to run reports and deliver insights relevant to their department – from Sales, Credit, Collections, Marketing, Finance, Operations, and external reporting.
“The site license is incredibly useful as it allows people within the company to create reports and share it with a wide audience to allow decisions to be made with the right data.”
Miles Davis, BI Manager
Harmoney now has the incredible wealth of information their data provides right at their fingertips. Every department in the organisation can make decisions based on almost real-time data. Providing everyone with access to Tableau means each team can continuously improve their processes – one of the most significant being their sales funnel. Everyone within the sales process can focus on improving the part of the sales funnel they’re responsible for. While also understanding the wider funnel and business context, to ensure that every decision or tweak made has benefits across the business.
The three systems combined, Snowflake, DataRobot, and Tableau, provide Harmoney control and access over their data that they would otherwise never have had. These solutions have supported the company’s growth over the years. The combined systems mean they’re able to develop and deploy models that improve the customer experience. Harmoney staff can analyse data in near real-time to see the effects a model or process change is having on the sales process. Enabling them to pivot and adjust if needed.