For technology platforms, data is the lifeblood. Now it’s clear that the future of financial institutions is impossible without digitalization and data integration. When some companies started to go the way to build a cohesive ecosystem where data can flow easily from one platform to the other, there are still many gaps to fulfill when it comes to integrations. For those who see the growth point in integrating data flows, it should be very interesting to learn how to make the process as predictable as possible.
If I say that establishing integration with ETL (which stands for extract, transform, and load) is a pretty clear and straightforward process, wouldn’t you read this article to the bottom line? I guess you’ll doubt the statement and be meticulously right – data processing, though plain, in theory, has plenty of pitfalls when it comes to practice. Let me show you what challenges you might encounter when integrating data streams from financial institutions and taking control of the process.
Fixing bugs before coding?
This scheme can upside down the way you always thought of software development. The first thing you should do when you extract data from the source storage is to fix the bugs – in the dataset you’ve just downloaded. The trick is that financial institutions collect a vast amount of data about their customers, their assets, trades, the flow of money, some supporting information, and so on. As a result, the datasets which financial technology systems need to process are enormous, which causes challenges in its download and processing.
The data that a financial system uses should be accurate as the cost of a mistake is very high. Data damage in Fintech can bring significant business money losses. That’s why fixing all the mistakes that can happen with the copied dataset before it will be processed is rather an idea. What can happen to the data when it’s extracted from the source?
– Connectivity disruption causes data losses and/or damage. To combat the case, it’s necessary to monitor internet connectivity and download progress all the way through.
– Wrong data input can also affect businesses. To prevent data inconsistencies in your system, check the source data carefully, and avoid exposing your clients.
– Unexpected format changes are undesirable, but unfortunately common in this area. Each company wants to grow and provide its customers with new opportunities, so your data providers can change the format they are sending their data to you. In case their communication channels aren’t dialed in, the format change can appear a surprise to you, which will bring hours of overhead fixing the process to enable work with the new reality.
Along with finding mistakes before they can harm your customers, companies encounter the challenge of processing huge amounts of data in a timely manner. The businesses try to beat the market by implementing efficient ETL processes so that to save time for data transformation. They also need to get access to the newest data prior to their competitors. All these factors make the parallel processing of data sets from different sources necessary. The biggest challenge there is partitioning data sets into small bunches that can be processed simultaneously and then be brought back into one.