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.
We use technologies like cookies to store and/or access device information. We do this to improve browsing experience and to show (non-) personalized ads. Consenting to these technologies will allow us to process data such as browsing behavior or unique IDs on this site. Not consenting or withdrawing consent, may adversely affect certain features and functions.
The technical storage or access is strictly necessary for the legitimate purpose of enabling the use of a specific service explicitly requested by the subscriber or user, or for the sole purpose of carrying out the transmission of a communication over an electronic communications network.
The technical storage or access is necessary for the legitimate purpose of storing preferences that are not requested by the subscriber or user.
The technical storage or access that is used exclusively for statistical purposes.The technical storage or access that is used exclusively for anonymous statistical purposes. Without a subpoena, voluntary compliance on the part of your Internet Service Provider, or additional records from a third party, information stored or retrieved for this purpose alone cannot usually be used to identify you.
The technical storage or access is required to create user profiles to send advertising, or to track the user on a website or across several websites for similar marketing purposes.