As the data collection methods have extreme influence over the validity of the research outcomes, it is considered as the crucial aspect of the studies
May 2025 | Source: News-Medical
In an increasingly data-oriented world, compliance with regulatory activities is more than just a checkmark, particularly in industries like finance and healthcare. Entities in these spaces must comply with strict regulations, including HIPAA, GDPR, SOX, and other statutes that characters the undertakings of sensitive data including the storage, usage, and transfer of data. One significant, yet heavily underutilized approach to supplement compliance efforts, is data dictionary mapping.
Data dictionary mapping is the mapping of data elements to data in various entities by metadata definitions conformable in respective systems’ data dictionaries. Data dictionary mapping does not solely provide consistency and quality to data, it also enhances visibility, traceability, auditability and other key tenants of compliance frameworks. We’ll review a few ways organizations can utilize data dictionary mapping to meet regulatory criteria effectively, with emphasis on finance and healthcare.[1]
In an increasingly data-oriented world, compliance with regulatory activities is more than just a checkmark, particularly in industries like finance and healthcare. Entities in these spaces must comply with strict regulations, including HIPAA, GDPR, SOX, and other statutes that characters the undertakings of sensitive data including the storage, usage, and transfer of data. One significant, yet heavily underutilized approach to supplement compliance efforts, is data dictionary mapping.
Data dictionary mapping is the mapping of data elements to data in various entities by metadata definitions conformable in respective systems’ data dictionaries. Data dictionary mapping does not solely provide consistency and quality to data, it also enhances visibility, traceability, auditability and other key tenants of compliance frameworks. We’ll review a few ways organizations can utilize data dictionary mapping to meet regulatory criteria effectively, with emphasis on finance and healthcare.[1]
The finance and healthcare sectors are among the most regulated worldwide. Each industry must develop strict data controls to ensure the confidentiality of personal information, the legitimacy of financial transactions, and the reliability of medical records.
As mentioned previously, here’s how data dictionary mapping supports adherence to compliance requirements:
Without standardized mapping there are usually discrepancies in nomenclatures, data types, or even interpretations that could create enormous issues regarding compliance-related destruction including data breaches, poorly developed compliance reports, and failed audits.
Follow through with data dictionary mapping to comply within both a finance and healthcare environment. Below is a revised action-based plan to assist your organization:
To begin, you will need to provide clarity around the purpose and regulatory situation with data dictionary mapping:
Determine which regulations relevant in your organization (e.g. HIPAA, SOX, GDPR).[3]
Now you have scope to map towards relevant compliance goals. Effectively implementing data dictionary mapping is necessary for compliance in the finance and healthcare environments. Here is a revised action plan to assist your organization in this way:
First, understand what data dictionary mapping is and the legislative context around it:
Now you have scope to map towards relevant compliance goals.
To the best of your ability develop a consolidated inventory of metadata on the systems of interest:
Recognizing information such as:
If you do not have data dictionaries to inventory, maybe data profiling tools can help, or contact the systems owners.
Before mapping begins, consider normalizing your metadata across systems:
Normalizing operations includes:
This step lessens the opportunity for misinterpretation and makes it easier to map the data.
Once you have normalized your metadata, the next step is to create mapping logic to relate your fields across a system:
Types of mappings:
Make sure to document each mapping which should include the source, target, transformation rules, and any other notes you may need for audit or troubleshooting.
Limit the headaches from manual work. Utilize dedicated mapping platforms that incorporate automation and compliance assistance.
Recommended features:
Common tools: Informatica, Collibra, Talend, Erwin
Don’t forget! Validating mappings is essential to make sure they’re accurate and not risk state compliance.
What to consider when testing:
Who should be included?
Proper documentation allows your mapping to be transparent, repeatable, and ready for audit.
Important documentation categories:
There will be an immense value to auditors, IT teams, and future project teams.
Data landscapes and policies change: mappings must change too.
Governance best practices:
A well governed mapping will make sure that mapping is updated, accurate, relevant, and compliant over time. [6]
In regulated sectors (finance, healthcare), the context of data management isn’t simply operations related; it’s about trust, accountability, and compliance. Data dictionary mapping represents an efficient, auditable way to ensure that data is accurate, consistent, and compliant across systems in a structured and scalable way.
With the right tools, processes, and governance models in place, organizations can improve their data quality, productivity, and decision-making, while also steering clear of potential regulatory actions. As data becomes less of an asset and more of a liability, mastering data dictionary mapping can be a potentially valuable way to be in control of your sustainable success.
