MTF Finance
Faster data for better business decisions

Fujitsu’s Azure Databricks Standard Lakehouse Accelerator revitalised MTF Finance’s data landscape, empowering the MTF team with near real time analytics, secure user information handling, and accessible insights for data-driven decision making in the competitive financing market.

Challenges

Legacy data systems hindered efficient analytics and reporting, with data latency, inefficient PII handling, and limited real time capabilities, impacting its ability to make data-driven decisions.

Solutions

Fujitsu implemented the Azure Databricks Lakehouse Accelerator, delivering multi source integration, a secure and governed data platform, and extensive knowledge transfer to upskill MTF Finance’s data team.

Outcomes

Data reporting latency reduced from 24 hours to two hours
MTF Finance’s data team upskilled to independently enhance the platform
Secure user information handling and mobile data access via PowerBI

"Fujitsu really went the extra mile for us, and transferred critical knowledge so we could work with and enhance the new platform ourselves in three months."

Steve Wood, Head of Data & Analytics, MTF Finance

2 hours for data loading, reduced from 24 hours

3 months for the data team to get upskilled to independently manage the platform

Industry:Financial Services
Location:New Zealand
Customer's website

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About the customer

Founded in 1970 by 12 vehicle dealers, MTF Finance has been helping New Zealanders finance vehicle loans, business loans, and other wide-ranging needs for over 50 years. With vehicle dealer partners and franchises across New Zealand, MTF Finance plays a crucial role in supporting the country’s automotive industry.

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Navigating potholes in legacy data platforms

Motor Trade Finance Ltd (MTF Finance), a financing provider in New Zealand, has been helping customers with vehicle loans, business loans, and other financing needs since 1970. Founded by 12 vehicle dealers, MTF Finance now has over 50 franchisees and 150 dealers across the country.

As the company grew, so did its data challenges. Years of data were stored in various cloud-based and on-premise legacy platforms, complicating its workflows. Non incremental data loads extended into business hours, hindering timely reporting.

Steve Wood, Head of Data & Analytics at MTF Finance, explained: “Our systems constantly receive data from the 200 franchisees and dealers who engage with the end customers to provide them with different types of loans. That incoming data was not being loaded incrementally into our system but was reloaded completely every night.

“As the volume of data increased, the data took longer to process in our traditional data warehouse architecture. This meant that when our staff accessed the system in the morning, they were not getting up-to-date data to analyse and to make decisions on. The data was up to a day behind and it was becoming more frequent for that to impinge on our business operations and efficiency.”

At the same time, MTF’s legacy systems did not support real-time analytics well and with no privacy by design for secure personally identifiable information (PII) handling, the MTF Finance data team had to take ad hoc and inefficient approaches to solve what should be standard challenges.

Wood said, “Our existing on-premise data platform was becoming close to unfit for purpose. We needed a platform that allows us to process and analyse data more efficiently, be more agile, and with privacy and discoverability built in.”

With the mounting need for a modern data platform to harness large volumes of sales data and to enable future growth, MTF Finance approached Fujitsu Data & AI team as well as Microsoft, with support from the team at Databricks, to transform its data landscape.

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An integrated and flexible data platform for simplified data management

After considering various options, MTF Finance chose Fujitsu’s Azure Databricks Standard Lakehouse Accelerator to address its data challenges. The Fujitsu solution provided a proven framework including multi-source integration, a medallion architecture, PowerBI setup with reporting dashboards, and deployment across multiple environments.

Wood said Fujitsu’s track record and the ability to upskill MTF Finance’s data team were instrumental in convincing MTF Finance to adopt Fujitsu’s solution.

“It’s not unusual to have bad experiences with vendors who are learning on the go as they implement new technology. However, with Fujitsu Data & AI, we felt we were getting a tried-and-true methodology that it had successfully carried out multiple times with customers.

“The big thing on my mind was Fujitsu’s ability to work alongside and provide critical knowledge transfer to my team. Our team did not have experience with Databricks so it was important we could maintain the platform ourselves after the project was completed,” said Wood.

Over six months, Fujitsu experts collaborated closely with MTF Finance to design and implement the solution, as well as upskill MTF Finance’s data team. Knowledge transfer and co-development took place, which maximised the learning opportunities for the MTF Finance team. “Our technical lead had hands-on involvement all the way throughout the project with the Fujitsu team,” said Wood. “He would either tag-team with one of the Fujitsu developers, or he would be doing the work under the direction of one of the engineers. Fujitsu also ran several knowledge transfer sessions for our wider data team throughout the project.”

During the project deployment, some challenges related to underlying cloud and network infrastructure cropped up and Fujitsu brought in additional technical expertise and resources to overcome the challenges. “Fujitsu really went the extra mile for us,” said Mr Wood. “And thanks to Fujitsu, Microsoft and Databricks, we were able to unlock additional funding to support this project.”

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Speedier data availability improves decision-making

With the Azure Databricks Standard Lakehouse Accelerator now in place, data can be incrementally loaded every two hours instead of just overnight, so the maximum data latency can be one hour rather than 24 hours. This means that MTF Finance stakeholders will have access to much more up-to-date information when making key business decisions.

Another key benefit has been the upskilling of MTF Finance’s data team. “Our internal capabilities improved significantly, so all of the team members have been able to work with and deliver enhancements to the new platform within three months of the Fujitsu engagement,” said Mr Wood.

PII data is now securely handled by design, making it easier for MTF Finance to fulfil its privacy commitments to customers. PowerBI is enabling company stakeholders to access critical data insights on the go with a mobile app. PowerBI is also tightly integrated with other Microsoft 365 apps that MTF Finance already deploys.

Looking ahead, MTF Finance plans to leverage the lakehouse platform’s analytics and machine learning capabilities to better understand customer needs, offer differentiated services, and exceed its growth targets.

For example, customer segmentation analysis could help identify demographics where there may be opportunities to better serve customers with additional finance products. “It’s about working out where we’re serving customers’ needs well and where we could uplift our game,” explained Mr Wood.

By working with Fujitsu to implement the Azure Databricks Standard Lakehouse Accelerator, MTF Finance has modernised its data landscape, enabling near real-time analytics and paving the way for continued growth in serving New Zealanders’ financing needs.