In today's era, new social networks and applications are emerging that deliver more and more data. These data, if they are used well, serve to know patterns, preferences and develop superior quality services in Fintech projects, as well as to make better business decisions.
The question is, how is this enormous amount of data handled and through what medium or tools is it processed and stored? Here you will see the basic concepts related to the data analysis and interpretation and how this affects Fintech.
Fintech, Data Analytics and Data Lakes
Briefly, Fintech (financial technology or financial technology) is a term used to describe new technology that seeks to improve and automate the provision and use of financial services. In Mexico there are more than 440 fintech startups and a financial technology adoption rate of 72%.
He Data Lake (data lake), meanwhile, is a flat repository that allows any amount of data to be captured in its original format, both structured and unstructured, and from various sources.
Then there is the Data Analytics or data analysis, which consists of processing and transforming the data into relevant information for the company.
How does Data Analytics promote the growth of fintech companies?
Financial services have embraced data analytics to make better investment decisions with consistent returns. Through the data analysis and interpretation In a massive way, with complex mathematical models, financial companies have managed to maximize the performance of their investment portfolios.
However, everything revolves around Big Data and the full use of its characteristics: volume, variety, accuracy, speed and value. This means that the data must:
- Be massive.
- Be reliable, since the success of the decisions depends on this.
- Come from various sources.
- Process quickly, and even in real time.
- Become valuable information.
How do you do this? With the Data Lake You get all the data that your Fintech needs to make better decisions. The Data Lake has the ability to track, catalog, and index big data effectively, which allows you to use the most relevant data for your sector.
He Data Analytics It takes advantage of the large amount of data existing in the lake, and from there it converts it into relevant and timely information. Some of the main advantages of data analysis in the financial sector are:
- It allows you to actively manage the data of the stock markets.
- Helps to make the right investment decisions.
- It enables insurance and retirement companies to actively manage risks.
- Automate the entire data collection and transformation process, making the organization more efficient.
Some usage examples in fintech projects They are algorithmic trading for the purchase or sale of financial instruments, risk analysis (something crucial in the financial sector) or obtaining consumer analytics to observe their behavior.
Codster: experts in Data Science and Data Analytics
Through Codster, you can implement the technologies that your company needs in the fintech sector, and make better decisions with the help of data analysis.
Codster use Data Lake to integrate data sources, leverage information, and help you improve business performance in meeting goals. It also allows you to segment customers and increase information security. To begin the process of modernizing your data system, you can request a free advice.