We have partnered with Vijayan Jayaraman, who is a Data & Analytics – Solutions Architect at Daimlinc. He is a senior technology enthusiast with over 13 years of experience in designing, configuring, and maintaining the systems for the Bigdata ecosystem, along with experience in handling orthodox infrastructure components. Read on to learn from his valuable experience in the industry.

Your data team might be pushing themselves so hard to build financial forecasts and formulate strategies. Millions of your ETL finance data are being processed and analyzed. A pat on the back for your team’s efforts!

But, are they able to provide financial reports to other teams as and when required? Are they facing difficulty in centralizing data for budget allocation? A fast and reliable ETL in Finance can help you tackle all these problems along with many others. 

An efficient ETL process brings data from all your finance data sources to a central location for efficient data analysis and reporting. Using this end output, you can combine departmental budgets together to create a plan and get a complete understanding of each person’s and department’s performance.

Sounds great. But, you will come across many challenges while benefiting from ETL. By the end of reading this blog, you will learn all of them and the solutions to tackle them. 

Let’s start with why ETL is important in finance industry. 

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Why ETL Matters in Finance Industry? 

In this section, I will walk you through how all ETL can help your data teams to provide best insights out of your financial data. 

  • Financial analysis and reporting: Tools for ETL in finance automate replicating the data from multiple sources. Using this, you can find your company’s financial health, analyze quarterly reports, and multiple years’ balance sheets, and decide on ROI.
  • Industry research: You will have access to the most updated data on market demand, competitor profiles, segments, and financial status. You can make more informed financial decisions on projects and invest in profitable stocks.
  • Incorporate innovation in business applications: Automating and streamlining data integration will make it easier to extend business applications for new innovations driven by AI and ML. It will also enable financial services businesses to more easily improve existing integration codes to reduce their total cost of ownership for the long term.
  • Provide customized experience to customers: A banking analytics-oriented shift will help you provide better customized experience for customers of banks and credit unions.

You have seen all of these extensive use cases of ETL in finance industry. It’s time to understand what challenges you will face during the process and how to face them. 

Common Challenges and Solutions During ETL Process 

Being a finance-related organization, most of your data will be transaction oriented and you would need real-time updates to have the most up-to-date and accurate records. A few challenges you will face during the process are, 

  1. Ensuring data security throughout the data replication process
  2. Latency of the data pipeline which delays the data for analysis
  3. Ensuring data sanity or no data mismatch in the destination during data integration  

In the next section, let’s dig deeper into how each of these challenges is significant and how to tackle them. 

Challenge 1: Data Security in ETL Finance 

As you know, finance industry deals with highly sensitive and confidential information, including financial transactions, customer data, personal identification details, etc. Also, financial institutions are prime targets for cybercriminals aiming to gain unauthorized access to customer accounts, commit identity theft, or conduct fraudulent transactions. Therefore, implementing robust data security measures is important during data integration. 

What are the data security measures you can implement?

  • Make sure that data is stored, processed, or maintained by your organization or service provider in a specific geographic location without crossing borders (also called data residency). This reduces potential security gaps associated with such transfers. 
  • Prevent unauthorized access to your data integration process by adding encryption mechanism.
  • Establish proper access control mechanisms (RBAC) for your data pipelines
  • Ensure compliance with GDPR, CCPA, and financial industry-specific regulations like Sarbanes-Oxley Act (SOX). They ensure data anonymization, consent management, and secure data transfer.
  • Technical issues, system failures, or human errors also result in data loss. To avoid these, have proper backup and recovery mechanisms in place to minimize the impact of data loss.

Challenge 2: Data Latency of Pipeline

Data latency refers to the delay between the generation of data from source and its availability for analysis, or the end use. Financial institutions require real-time or near real-time access to data for activities such as trading, risk management, and monitoring market conditions. Data latency would be a roadblock there.

Let’s get into the scenarios when data latency arises and how to deal with it:-

  • Data transformation and cleansing can cause latency. To tackle this, you can optimize data transformation processes by using efficient algorithms, parallel processing techniques, and distributed computing frameworks. 
  • Network latency can impact data transfer speeds and affect time-sensitive financial processes. To deal with this, evaluate network infrastructure and upgrade network equipment, leveraging content delivery networks (CDNs), or using dedicated network connections for critical data transfers.
  • Batch processing can also impact latency. Because, here data is collected and processed in batches rather than in real-time or as individual events occur. As a result, there is a delay between the time data is generated or received and when it is processed. This accumulation of data over time can introduce latency, especially if there is a large volume of data to be processed.  You can implement data caching mechanisms and leverage in-memory processing technologies to store frequently accessed or time-sensitive data. This approach can help reduce latency by providing faster access to pre-processed or frequently used data.

A few more solutions to reduce the latency that arises due to the above situations are,

  • Instead of performing full data refreshes, use automated tools that have change data capture feature only to synchronize changes between systems. This approach can minimize latency by updating specific data elements that have changed, rather than processing and integrating the entire dataset.
  • Use monitoring tools and performance analytics to identify areas of improvement and fine-tune the integration pipeline for better efficiency and reduced latency.

Challenge 3: Data Sanity in ETL in Finance 

Data sanity is the process of verifying and ensuring the quality, correctness, and consistency of data before using it for analysis. It involves conducting checks and validations to identify any anomalies, errors, or inconsistencies in the data. 

Remember that whether it’s investment decisions, pricing strategies, or forecasting, data sanity provides the foundation for informed decision-making. Therefore, you need to pay attention to it, if you haven’t already.

So, how can you deal with data sanity issues?

  • Apply data validation rules to ensure the accuracy and integrity of the cleaned data. 
  • You can also use statistical methods, such as z-scores or box plots, to identify outliers. Depending on the context, you may choose to remove outliers, replace them with more representative values, or segregate them for further analysis.
  • Perform data integrity checks, such as referential integrity checks, range checks, and data type validation. Flag or remove data entries that violate predefined validation criteria.
  • Compare financial statements, transactions, or balances against independent records, such as bank statements or external data sources. 
  • When encountering missing values in finance data, apply appropriate data imputation techniques. Depending on the nature of the data, you can use methods like mean, median, or regression imputation to estimate missing values. Be cautious when imputing values and consider the potential impact on analysis and decision-making.
  • Apply data transformation and normalization techniques to address inconsistencies in data formats, units, or scales. 

You have seen the challenges and solutions while performing ETL for the finance industry. In the next section, Vijayan Jayaraman shares the challenges and solutions of ETL in the banking sector based on his extensive experience in the field.

Main Challenges and Solutions in the Banking Sector

Banks, handling sensitive customer data and navigating strict regulations like GDPR, often hesitate to embrace cloud-based PaaS services or market available tools for integration. Their reservations stem from concerns about data security, limited customization, and adherence to data sovereignty regulations. Banks prioritize direct control over data management and require tailored solutions to align with their unique operational needs.

Additionally, the need to mitigate risks and ensure uninterrupted operations further underscores their caution when considering external PaaS services. In the banking industry, real-time data integration is essential for making timely decisions and ensuring regulatory compliance. However, it comes with specific considerations.

Banking institutions must grapple with the complexity of integrating vast and sensitive financial data in real-time, a process that demands substantial resources and meticulous data quality control. Achieving low latency is critical for real-time transaction processing, but challenges may arise in ensuring consistent, high-speed data flow. Robust error-handling mechanisms are necessary to maintain data accuracy, especially when integrating real-time data with legacy banking systems.

Data security is non-negotiable, with encryption and access controls crucial. Banks also need to manage operational overhead and be cautious of vendor lock-in when adopting real-time data integration solutions. Kafka Streaming and Spark Streaming are the preferred streaming technologies commonly used across the industry.

While powerful for real-time data processing, they share several limitations. Both may introduce complexity and steep learning curves, with Kafka Streaming often requiring expertise in distributed systems. Achieving ultra-low latency can be challenging due to Kafka’s distributed nature and Spark’s micro-batching model.

Maintaining stateful processing in both frameworks can be complex. Additionally, resource overhead and integration complexities can pose challenges, and monitoring and debugging real-time applications in these systems can be intricate. These factors are always considered while selecting a streaming solution for a specific use case.

For banks that are not yet prepared to embrace automated platforms, an alternative approach is to develop in-house integration platforms coupled with data lakes. While this solution may not encompass comprehensive data observability, it allows for the establishment of internal standards to address data quality issues. This approach works effectively when the ultimate objective of data integration is trend analysis and report generation in the finance sector.

By quantifying deviations from 100% data quality, organizations can incorporate this information into their analytical processes, facilitating more informed decision-making. But on the other hand building and maintaining in-house integration platforms coupled with data lakes comes with disadvantages. It can be cost-prohibitive, requiring a skilled workforce and time-consuming implementation.

Scalability, data quality, and governance challenges may arise, limiting the organization’s ability to handle evolving data needs. Additionally, it may lack specialized features and vendor support available in commercial solutions.

A platform like Databricks effectively addresses real-time streaming challenges through its seamless integration of Structured Streaming, simplifying complex stream processing tasks. However, to leverage this platform, onboarding and adopting the platform as a holistic solution is essential, which is not possible for every organization.

Here is where automated platforms like Hevo Data can help.

How an Automated Data Integration Tool Can Help You?

Using an automated data pipeline platform for ETL has the following benefits:-

  • Simple schemas:- The finance data source schema of such tools will be simplified. So, your team can focus on analyzing the data, accessing it wouldn’t be a concern.
  • Customized cadence:– With their configuration options, you can choose which finance data tables are on top priority to sync. Also, how often those tables need to be refreshed in your warehouse. This enables lesser time for your analysis.
  • Efficient finance analytics:– Third-party data pipelines can bring in insights from your important financial data sources. So, your finance team can build more predictable customer models or enhance revenue tracking. 

Next, let’s take a look at a case study of Hevo Data to show you an example of how an automated data pipeline can help your data teams. 

Case Study on How Hevo Data Helped a Financial Business for Efficient Analysis and Reporting

Lendingkart helps customers with risk identification, fraud detection and disburse loans within 72 hours by using its in-house big data analytics tools and ML algorithms.

The team used to try out multiple pipeline combinations with varying transformations as their data pipelines were not fixed. This consumed most of their engineering team’s bandwidth and critically hampered focus on other projects on priority and resulted in delays and unreliable data output. 

Lendingkart found Hevo a good fit for an automated data pipeline solution that’s fast, reliable, provides pre-built integration with sources, and supports advanced transformations.

Hevo helped them build multiple pipelines and they used our features like auto-mapping, CDC, JSON based API parsing, and Python interface extremely helpful which saved their time and efforts in extracting, transforming, and storing their data in the data warehouse.

Let’s wrap it up!

Conclusion

ETL in finance helps you analyze real-time data from all finance data sources and centralize data for use cases like budget allocation. You can provide analytics-oriented customer experience, analyze industries for investing in the most profitable stock, and take decisions on ROI through financial analysis and reporting. 

The top challenges in ETL for finance are data security, latency, and data sanity. You can find the nuances of practical solutions for each of these in the blog. For all your use cases, an automated no-code ETL tool like Hevo Data can help you with its customized cadence, and simplified schema and features for efficient financial reporting.

With Hevo, you can integrate data from 150+ sources into a destination of your choice. ELT your data without any worries in just a few minutes.

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It has pre-built integrations with 150+ sources. You can connect your SaaS platforms, databases, etc. to any data warehouse of your choice, without writing any code or worrying about maintenance. If you are interested, you can try Hevo by signing up for the 14-day free trial.

Vijayan Jayaraman
Data & Analytics - Solutions Architect, DAIMLINC

Vijayan Jayaraman, a Data & Analytics - Solutions Architect at Daimlinc, brings over 13 years of expertise in designing, configuring, and maintaining Bigdata ecosystems and orthodox infrastructure components. As a senior technology enthusiast, his experience offers invaluable insights into navigating the complexities of modern data landscapes.

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Content Marketing Specialist, Hevo Data

Anaswara is an engineer-turned writer having experience writing about ML, AI, and Data Science. She is also an active Guest Author in various communities of Analytics and Data Science professionals including Analytics Vidhya.

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