Automating data mapping is crucial for small financial service organizations to ensure regulatory compliance and track sensitive data across hybrid clouds. By utilizing data governance tools, the IT team can mitigate the risk of data breaches and maintain transparency in data management.
Automate Data Mapping for Compliance
For small financial service organizations, implementing automated data mapping can help track sensitive data across hybrid clouds. By integrating data governance tools, the IT team ensures regulatory compliance and mitigates the risk of data breaches.
Data mapping is about understanding the data lineage and cataloging. It answers the questions, what is the origin of this data, where is it stored and how does it move across systems?
“Data mapping is indispensable for effective data governance and regulatory compliance,” Schreiner says. “It provides the transparency, structure and documentation needed to manage data responsibly, respond to regulatory demands and build trust with stakeholders and regulators.”
Having a unified data management platform is key for automating data mapping. Before you can truly map your data, it helps to have a unified view of it.
RELATED: Unify data protection across your hybrid infrastructure.
Real-World Example: A Small Healthcare Practice
Healthcare companies implement data mapping as part of their unified data platforms. This allows them to “trace patient data from intake to reporting, ensuring HIPAA compliance,” Schreiner says.
Data mapping would also identify and secure personally identifiable information, understand its lineage and, crucially, use this information to identify and address compliance risks such as unauthorized data transfers.
Predictive Analytics for Operational Efficiency
“Comprehensive data integration and transformation supports seamless merging and joining of structured and unstructured data from multiple sources,” Schreiner says. It subsequently becomes easier to use services such as AWS Glue to “cleanse, standardize and prepare data for enrichment and analytics.”
For example, advanced metadata extraction makes it possible to enrich data catalogs with business context to “improve discoverability and enable richer data insights,” Schreiner says.
Simply put, data becomes much more valuable and traceable. For SMBs eyeing AI workflows, this prevents the “garbage-in, garbage-out” scenario. It also enhances efficiency by reducing risk and maximizing data’s utility to employees, regardless of its location in a hybrid cloud environment.
READ MORE: Small businesses can, and should, capitalize on AI opportunities.
Real-World Example: Supply Chain Analytics
Small logistics firms like Shipwell and Flowspace are adopting AI-driven predictive analytics to analyze fragmented data streams from multiple cloud providers. The solutions identify potential delivery delays, optimize route planning and enhance supply chain visibility.
These types of predictive systems would be impossible without a clear way to track, contextualize, govern and ultimately utilize their data.
