How to Reduce the Price of Poor Financial Data Management

This post was first published by our partner, MANTA, on their blog and is republished with permission

Image sourced from QuoteInspector

Data management in the financial sector can easily get out of hand.

The volume of data that is growing every moment, the complexity of data environments, ever-changing regulatory compliance requirements, and system modernization challenges prevent financial data end-users from using data as it’s intended. Despite today’s state of data and the pace of digital transformation in financial services, you can win on this front without hiring an army of data engineers. Learn how automating data lineage collection helps clear financial data management hurdles and:

  • Build better data governance and compliance programs
  • Improve financial reporting
  • Assess and mitigate financial risk
  • Make migrating to the cloud possible

Regulatory Compliance Programs

Navigating data regulations is particularly challenging for banking and financial institutions. 

Every organization handling the personal data of customers from the European Union is subject to the General Data Protection Regulation. In the United States, financial institutions are obliged to protect customers’ personal data under the Gramm-Leach-Bliley Act. And there are several other regulations worldwide that oblige all kinds of institutions to secure their customers’ data. In fact, 128 countries have legislation in place to secure the protection of data and privacy. And data privacy is just the tip of the regulatory compliance iceberg. 

Knowing the complete data journey, its sources, and its transformations is crucial for a successful regulatory compliance program.

Financial regulation compliance requires institutions to maintain a complete and accurate record of financial reports (SOX), generate and aggregate up-to-date risk data (BCBS 239), and ensure pre- and post-trade transparency (MiFID/MiFIR). Achieving these goals requires a solid and detailed understanding of all relevant data that can’t be achieved without automating data lineage collection in the initial phase of a data governance program. Why is that? Manual efforts of mapping the sources and all data flows are tedious and error-prone. They will most likely result in inaccurate results that won’t be delivered in a timely manner, resulting in a snowball effect and jeopardizing your future compliance efforts. 

How to overcome this issue. Automate data lineage collection efforts and leverage the power of automated scanning of every nook and cranny of the data environment. No matter where the data you need resides and how it’s transformed along the way, you can easily understand it with a clear and understandable map of all data flows. Automating lineage collection across multiple sources eliminates the risk of human error and ensures a complete overview of the processed data. 

Proactive Analysis for Risk Management

Back when times were simpler, with less data at hand, financial institutions could only afford to rely on people when developing credit scoring models and anti-money laundering assessments. 

With more customer data, these models and assessments can be more solid and reliable than ever before. But only if technology comes into play to help analysts process and understand this abundance of data.

Why is that? Data is simply too distributed and too dynamic for the manual approach to harness it. Lengthy, tedious processes can’t give an organization an overview of what data they have at their disposal. Tracking the data back to its source, untangling all its connections across siloed systems, and figuring out various formats—without automated processes focused on these tasks, it’s nearly impossible to assign context to the data that is crucial for analysts in order to monitor their data properly and perform their actual tasks. 

Automation to the Rescue

Automating data management processes in a smart way allows financial analysts to focus on building better data-driven risk models and fighting financial crime. A clear and understandable map of the complete data journeys with their sources, dependencies, and transformations provides users with crucial contextual information. It also empowers them to use data with more confidence (in less time!). Running regular automated scans of the data environment will help you detect any changes and abnormalities so you can recognize risks immediately and act accordingly to mitigate them.

Has the scoring algorithm changed over the years? A proper data lineage solution allows you to travel in time to compare lineage from different time slices. MANTA’s historical lineage is a feature that a customer of ours used in a similar scenario. Their customer had attempted to sue them for not approving his loan the first time he applied, only to be approved a year later. Comparing lineage ensured that their credibility was left unharmed.

Moving to the Cloud

Current business imperatives, the development of new capabilities, and technical innovation are just a few reasons why financial institutions are looking to move to the cloud. However, the nature of many banks’ legacy infrastructures and strict privacy and security policies make it challenging to design a perfect migration plan and stick to it. At MANTA, we know this all too well—data migrations fail due to data quality issues, an incomplete image of what data is being stored in complex environments, and how the assets are interconnected. 

Smooth and Painless Financial Data Migration 

The key to a successful migration in such an environment is a complete and accurate map of the migrated systems. Once the map is generated, the system can be broken down into smaller parts. It allows the team responsible for the migration to decide what should be migrated when and also assess the impact of the planned changes before they have been implemented. It’s the way to go to avoid data downtime and data corruption. 

Data Lineage for Smooth, Controlled, Effective Migration to the Cloud
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Moving to the cloud has numerous advantages, but we also work with financial customers whose internal policies don’t let them move their data to the cloud. Some of our customers combine mature, established technologies with cloud technologies. We have a solution that helps you make all kinds of technologies coexist—Open MANTA. Read more about it to find out how our extensions allow you to illustrate any kind of relationship, even for technologies that we don’t have a formal scanner for, so you can benefit from MANTA’s automated lineage no matter how mature or innovative your environment is.

We know how challenging data management is in the financial industry. Let us show you how automating the efforts of tracking data, its sources, and how it moves across the pipeline will improve your data management.