LeasePlan is a Dutch automobile leasing and financial services provider that specializes in fleet management services. Since its establishment in 1963, LeasePlan has evolved into the world’s largest fleet management service company, currently operating in 32 countries across the globe. LeasePlan has over 6,800 employees and operates around 1.8 million vehicles. It operates in two large and growing market segments: Car-as-a-Service for new cars, through its LeasePlan business, and the high-quality used car market, through its CarNext business.
Data as a Top Priority at LeasePlan for Expanding Digital Services
Data became one of the top priorities at LeasePlan as it embarked on a journey to become a fully digital car-as-a-service company by expanding its service portfolio. As is the case with other major companies, LeasePlan was also struggling initially to figure out how to leverage its vast amount of data assets for enabling revenue-generating activities and innovating new products and services. To do so, however, LeasePlan needed to achieve its vision of making data available anytime, anywhere, regardless of its form (structured, semi-structured, and unstructured). By declaring data as a strategic asset of the company, LeasePlan got its top leadership on board to further its data strategy. It built a blueprint of its data ecosystem that included the data sources owned by the company as well as the external data sources that had to be integrated, in order for certain processes to enable new business models. The blueprint made it possible to visualize what data assets can be leveraged for specific purposes and identify missing data assets for spearheading its data-driven initiatives.
Logical Data Fabric as a Single Point of Data Integration and Governance for Hybrid Environments
But the challenge remained: how to efficiently knit together all of this data to exploit it for its intended purposes? LeasePlan’s data assets were heterogeneous and siloed in nature. It consisted of internal legacy systems as well as quite a few external data sources. Internal data sources included SAP, Salesforce, Avaya, IBM DB2, and a few others. The heterogeneity of the data sources made the data integration cumbersome and threatened to leave LeasePlan’s data initiatives in limbo. It became apparent to LeasePlan that the company needed modern data storage that was scalable, reliable, efficient, and less expensive. The result was the move to the AWS Cloud. Additionally, LeasePlan used the Denodo Platform to build a logical data fabric architecture that sits at the heart of its global data hub. The logical data fabric aggregates data from various source systems and serves as a single, integrated source of all the data needed by reporting applications.
So far, the logical data fabric has served three major objectives at LeasePlan:
- Removed data silos
The aggregation of data through a centralized secure data layer of the logical data fabric has helped in removing the data silos at LeasePlan, and has expedited its data-driven business initiatives.
- Enabled precise car-damage detection
The data gathered from internal and external systems enabled LeasePlan to more accurately detect car damage and calculate residual value, maintenance charges, and other related costs.
- Enabled predictive maintenance
LeasePlan can now predict far more accurately when a car may need service and steer its customers to preferred garages in time, and even schedule appointments for them. Also, by merging information from one car with the historical information from other cars, LeasePlan can predict when a car could break down so that it can take preventive action.
To learn more about how LeasePlan built a logical data fabric with data virtualization, watch the video below, taken at a recent Denodo DataFest 2020.
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Hey Sutender, great article.
Every component of a truck’s mechanical and electrical system is on the line. Also, those that have an impact on the driver’s and passengers’ safety, as well as the cargo’s integrity, so that it arrives in excellent shape. The volume of data, on the other hand, is immense. There is a significant amount of data and paper that must be written by hand and later digitized, with a high danger of losing vital information. As a result, it’s critical to consider ways to strengthen this management.