Vesteda is a Dutch residential investor in mid-market rental housing in major urban regions. In doing so, the company invests funds on behalf of institutional investors, such as pension funds and insurers. Vesteda was looking for a way to make better use of the large amount of data at its disposal to improve its services. "Thanks to the switch to TimeXtender, we were able to automate the manual work we were still doing across five different modules," says Jelle Vegter, team lead BI & Data Science at Vesteda.
Vesteda chooses TimeXtender®
By the end of 2020, Vesteda had invested a total of almost 8.2 billion euros in Dutch residential real estate. The rental portfolio comprises more than 27,400 homes. These homes are located mainly in economically strong areas and major urban regions. A portfolio of this size naturally comes with a lot of data. By combining data from the financial systems with data from all kinds of other sources, the residential investor can draw meaningful conclusions. Until a year ago, it did this through a manual process that was time-consuming, error-prone, and spread across five modules. Jelle Vegter had therefore been searching for some time for a solution to replace the old data warehouse.
The Result
Getting started themselves
Vesteda's employees received TimeXtender training from E-mergo, allowing them to work with TimeXtender on their own. This means they are no longer dependent on E-mergo consultants for changes.
Automated
TimeXtender was able to automate a number of processes that used to be manual and time-consuming. In doing so, TimeXtender acts as an 'orchestrator' that now controls five of Vesteda's existing modules. This significantly shortens the time from request to go-live.
Data-driven
Vesteda now has better insights and can make better decisions by working in a data-driven way. The data warehouse plays a crucial role in this, offering a lot of insight into the data while meeting strict privacy and security requirements.
“My first impression was that TimeXtender could automate a number of processes that were time-consuming and error-prone.”
Jelle Vegter
Team Lead BI & Data Science
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