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Agile Data Management Strategies for Fintech AI Applications

EasyChair Preprint no. 13264

16 pagesDate: May 13, 2024


Agile data management strategies play a crucial role in the success of Fintech AI applications. Fintech, the intersection of finance and technology, relies heavily on accurate, timely, and secure data to power its AI algorithms and deliver innovative financial services. This abstract explores the key principles and strategies of agile data management in the context of Fintech AI applications.


The agile approach to data management emphasizes iterative and incremental processes, cross-functional collaboration, and flexibility. By breaking down data management tasks into smaller iterations, Fintech companies can continuously incorporate feedback and improve their data management practices. Collaboration among stakeholders, including data scientists, engineers, and business analysts, fosters effective communication and knowledge sharing, facilitating the seamless data flow throughout the organization. Furthermore, the flexibility and adaptability of agile data management enable Fintech companies to respond to changing data requirements and evolving needs.

Keyphrases: Agile Data Management, Data Analytics, Data integration tools, data preparation tools, Data Virtualization

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Edwin Frank},
  title = {Agile Data Management Strategies for Fintech AI Applications},
  howpublished = {EasyChair Preprint no. 13264},

  year = {EasyChair, 2024}}
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