Data-as-a-Product: Shaping the future of the financial industry

When:

On-Demand

Where:

Online

Data-as-a-Product: Shaping the future of the financial industry

Data is critical to every financial institution; it is recognised as a core asset to drive customer growth and innovation.

Through the Data-as-a-Product (DaaP) paradigm, product thinking is applied to datasets to enable strong decision-making and promote a more efficient approach to data. DaaP makes company data more scalable and accessible, promoting development and innovation.

The same objectives that are driving DaaP have been driving the transition from monoliths to microservices. A central characteristic of microservices architecture is that functionality (service) and state (data) are managed and owned by the same team. When data is transferred and managed by data warehouses the benefit of a microservices architecture is lost. With DaaP, the methods of decentralization applied are the same as with microservices. Making data a product means that whoever “sells” or “serves” the data is holistically responsible for all aspects of the data, non-functionals as well as semantics.

As the need to leverage data efficiently increases, the technological frameworks surrounding it need to be altered. A shift towards DaaP can improve data analytics and the way banks approach data by providing them with new technical capabilities. With the development of DaaP, mainstream financial institutions can be more proactive in dealing with data and more specific around the information they collect.

Sign up for this Finextra webinar, hosted in association with MongoDB, to join our panel of industry experts as they discuss the following areas:

  • What are the business problems that are triggering financial institutions rethinking their approach to data?
  • What is Data-as-a-Product? What issues can DaaP solve?
  • How can companies use DaaP constructively?
  • How will financial institutions navigate this transition?
  • What is expected with DaaP in terms of data platforms, data architecture, and data governance?

 

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