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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.

Parallel processing

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.

How to Establish an Efficient ETL Process in a Fintech Product

 

Data Warehouse vs Data Lake: which one to choose

When you download all the data you need for your systems’ work from data providers, you may be embarrassed by the variety of data formats and fields they provide. A great deal of data architecting is necessary to cut everything redundant and extract the information you really need to establish your systems’ work. This process is called transformation in ETL; still, there are different cases and ways how to establish it properly.

The data types you can utilize in your financial system vary. There are three of them: real-time, end-of-day, and historical data. If we talk about the latter two types, the process involves the transformation of data structures into one unified structure according to business rules that are dictated by businesses. The common pitfall here is business people rarely have enough time and data structures understanding to formulate business rules properly. In that case, you should be ready to analyze the business needs and come up with a solution inside a technology team. The data structure used in this case is called Data Warehouse and it’s applicable for wealth management systems, especially ones for passive investment, lending, etc. The data load and transformation once per day is allowed there.

When it comes to daily trading or consumer banking, it’s crucial to have a simultaneous data exchange based on events. The execution of trades and transactions runs multiple times a day and immediately affects the account of a customer. This type of system is called real-time and requires a Data Lake storage structure. Data Lake doesn’t require transforming source data structure in any way but envisages a more complex process of extracting the data from storage.

If a mistake is caught, undo

Traceability of changes in a target database is a necessary condition of control over the ETL process. The pitfall here is to make the process simple enough to have every change monitored. With the scaling of integrations number, it becomes a real challenge to pull everything together.

There are various ways to solve the problem of monitoring the data integration process. First and the most common is introducing automated testing of data insert into the target base. It saves QA effort while creating a foundation for tracing different parts of the process. One may also use Redash panels to gather information about certain firms, for example, the number of firms today and yesterday; such information prevents data loss or duplication. In addition to this, the processes of data transformation are usually monitored via a table that displays whether a specific process failed or succeeded.

Takeaways

Integrations between financial institutions often require data transformation. Here’s where an efficient ETL process can help your business to avoid challenges and bring more value faster. With our new guide to ETL processing, one can find all the necessary basics to allocate problems and find a way to fix them.

About INSART

INSART is a Fintech engineering partner with outstanding knowledge and experience in developing Fintech solutions, assembling dedicated teams for Fintech projects, and managing them in a long-term perspective. We teach our developers financial concepts and foster a culture of financial literacy and well-being across the company. Our expertise saves you at least 25% time comparing to teams inexperienced in the business domain area. INSART is a leading partner of WealthTech Club. Contact Vasyl Soloshchuk to learn details. Visit site.

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