Financial Institutions and Regulations Part 2: Getting Trust with Data Lineage.
In the aftermath of the 2008 financial crisis and the introduction of strict regulations, financial institutions came to rely on metadata management solutions the hard way. They found that data governance brings more to the table than simply remaining compliant. Part one of this article explains how the need for trusted data and transparency was solved through implementing data governance. One of the most important data governance solutions for financial institutions is data lineage.
There’s more to Data Governance than Regulatory Compliance
Financial organizations found that they needed data governance to acclimatize to increasing data demands. The once overwhelming volume of unusable data is managed with metadata into valid and trusted data, inventoried to be accessible by users. Sensitive data can be detected automatically and policy controls can be enacted to ensure compliance.
For financial institutions, data governance was a solution to organize an enterprise’s data landscape and make sure they avoid fines. As the financial sector became elevated with metadata, the best companies asked the obvious question: ‘Why not use metadata to bolster workflows and get value too?’
Data Lineage is Data Trust
Trust is a key point in the best practices of financial institutions. With financial organizations’ data landscape now usable with metadata management tools, having data lineage takes the use of this data to the next level.
Data lineage tracks the entire lifecycle of data assets, revealing its transformations and relationships from its source to destination. It equips users with deeper knowledge and context about data assets. Having more information about data assets gives you two things: (1) more trust in the data, and (2) better use of the information it provides in your tasks.
For asset managers, it allows them to display how they use funds for auditors and investors. For lenders, it provides a means for them to validate information with their own eyes so they can better make decisions about loans. For insurers, they are provided with further context about scenarios and how that affects their customer’s circumstances. For data analysts, insights on a data asset’s history and interactions facilitates business strategy.