NIBC retail bank wants to become a truly data-driven bank in order to maintain growth in a competitive market with shrinking margins and increasing cost pressure; therefore, Amsterdam Data Collective was asked to kickstart their data-driven transformation.
Amsterdam Data Collective’s data-driven approach for NIBC consists of 3 main parts:
- Create excitement for the data-driven transformation within the bank by creating multiple prototypes that show short-term concrete results.
- Develop a strategy for lasting enterprise value using our signature organisation scan.
- Transfer knowledge from the Amsterdam Data Collective team to the internal team in order to aid in building best-in-class standards.
“It’s really impressive what the team from Amsterdam Data Collective has been able to achieve in terms of impact in only a matter of months. Thanks to their combination of senior marketing experience with state-of-the art technical knowledge they are the perfect partner to translate our commercial data into value for the bank”Richard Leijnse – Managing Director NIBC Retail Banking
The project consisted of the following workstreams that ran simultaneously:
- Workstream 1: Identifying and prototyping the no-regret use cases. These are use cases in the marketing domain that require relatively limited effort and have a high impact on the commercial KPIs.
- Workstream 2: Using our proprietary organisation scan to identify the core processes and data-driven transformation opportunities in these processes that, as a result, led to a prioritised list of over twenty use cases, including impact assessment. During the first phase of the project, a transformation roadmap was made for creating maximum and sustainable value with data.
- Workstream 3: Conducting a data scan to analyse the current data situation (e.g. data availability, data quality, and current infrastructure). Subsequently, the gaps were identified and a future-proof data architecture was developed.
Impact for the Client
In only three months (phase 1 of the project), three prototypes were ready and validated with internal users. In addition, an internal data science team was set-up and started working alongside the ADC team for knowledge transfer, and the future-proof data architecture was designed. Above all, the first interventions have already been executed based on the actionable insights from the prototype models.