Equipping FinTechs with Advanced Data Analytics and Data Science Skills

This experiment took place in either 2019 or 2020, if I recall correctly. As the guidance suggested was "Dream Big, Start Small and Move Fast", some people naively made considerable investments in the expectation of creating a self-sufficient industry utility for the ecosystem. Although these goals are admirable, the sheer difficulty of getting people to use an online platform became obvious soon after. Keeping the platform running and continuing to allocate resources to it became an arduous task, and the platform eventually closed down. This experiment seeks to discover ways to make data engineering more efficient in data analytics and data science projects, with a focus on the most time-consuming and labor-intensive tasks. Surprisingly, the most difficult part of data analytics and data science projects for FinTechs and innovative elements within Financial Institutions is not the statistical, quantitative, and qualitative analysis conducted at the end, but the tedious data engineering required to create a data pipeline that uploads unified data to the data lake at the beginning. The initial mile data engineering has 3 central elements. The innovation lab aspires to transform the murky quagmire of publicly available datasets into a more easily navigable data lake, draining away the mud in the process. The innovation lab is looking to make additional investments to create a cloud-based data engineering platform. This platform is going to bring together public datasets from various sources in the cloud. In order to foster data engineering, the innovation lab will provide some of its own data engineering libraries and processes. Utilizing these, platform users will be able to turn the public datasets into a harmonized format. The transformed datasets will be shared on the cloud-based data engineering platform as a way of giving back to the community. In order to promote the platform, the innovation lab has allocated data science and data engineering resources to transform publicly available data sets and make some of their own data sets available. This is expected to help spark more interest in the cloud-based data engineering platform and lead to increased use. Should the experiment prove successful, a data engineering platform with best practices from an innovative lab could be made available to the public, along with a library of transformed datasets. This could lead to a continuous improvement cycle and benefit the local community. The platform would enable FinTechs to access datasets easily and use the platform to develop data analytics and data science projects.

Source: https://medium.com/@notherArmchairEconomist/enabling-fintechs-with-greater-data-analytics-and-science-capabilities-1e6be72c45f6?source=rss------fintech-5