Big Data Initiatives in Document Compliance Management
Businesses and government organizations of all sizes, and across all industries continue to be challenged by rapidly growing repositories of unstructured content. Regulated industries like financial services, healthcare, legal, and government may be subject to more regulatory compliance standards and audits.
Yet all businesses can be taken to court, be acquisition targets, or receive unexpected requests for data about files like:
- HR Records
- Customer Invoices
- Product Engineering Files
- Accounting Statements
Since many businesses are now storing terabytes and petabytes of unstructured content, manual tracking and analysis of document metadata and usage simply isn’t feasible.
Big data analytics platforms are an executive’s best ally for understanding vital information about corporate content. For example, how long files declared as records have been kept in storage (or if they need to be deleted or destroyed for compliance reasons).
Document management APIs and SDKs allow for integration with a wide variety of analytics tools. Instead of building document-related compliance functionality, developers can package the API code into their application. This will ensure that the solutions their internal or external stakeholders use will meet compliance regulations and requirements.
Here are four ways big data initiatives help enterprises address regulatory compliance requirements from a document management perspective.
1. Legal eDiscovery
Technology companies in the legal research space like Lexis Nexis and Cornerstone Discovery add millions of documents to their repositories on a daily basis. Big data is used in these systems for purposes like sourcing expert testimony or gathering evidentiary documents to defend against class action suits.
For inside corporate legal teams, the ability to identify all legal matters related to a certain product, client or event, and lock those files down is a step in litigation readiness. It’s also a common practice for legal librarians to generate reports on document metadata such as creation dates, authorship, and permissions. According to Law Technology Today, data-driven decision making is increasingly significant in areas of law such as patent and trademark law, intellectual property, antitrust, and securities.
2. Human Resources
Corporate HR departments often have large libraries of documents, including timesheets, expense reports, policy documents, and benefits forms. Though HR personnel may not notice that an expense form has been submitted twice over an extended period of time, analytics engines can identify duplicate documents based on similar inputs.
Analytics can also identify whether electronic or digital signatures have been applied to documents or related workflows, such as to demonstrate a company obtained reference consent from employment candidates. Another example is to ensure existing employees understand the results of their performance evaluations. HR business applications for payroll, applicant tracking, and performance monitoring often are integrated with libraries of records, and they are subject to retention schedules which need to be followed.
3. Privacy Compliance
Data privacy standards and regulatory requirements such as the European General Data Protection Regulation (GDPR), the US Privacy Act, and the Health Information Portability and Accessibility Act (HIPAA) are growing in scope and complexity. All industries need to demonstrate GDPR compliance and make sure their website visitors understand how their data is stored and used so they can knowledgeably consent to allow it.
If case files or other documents need to be redacted for privacy reasons, a big data engine can identify all documents with a specific attribute such as author, patient name, or account number. Information workers can then easily redact sensitive content out of documents within the query instead of hunting them down individually. Or annotate documents while preserving the original document for compliance purposes.
Standards-based API tools offer “buy instead of build” options to meet document privacy regulations. They take a lot of the complexity out of identifying what documents need to be marked up, redacted, or deleted for privacy reasons.
4. Compliance Requirements for Biomedical Image Files
The vast number of medical imaging files, crime scene forensic photographs, and security camera images are beyond the scope of manual processing. Artificial intelligence and big data play a significant role in parsing huge repositories of these files, such as for facial recognition, or identifying medical images for similar patient diagnoses.
Compressing biomedical imaging files before storage reduces storage and CPU utilization. Anonymizing these files through redaction is required for compliance HIPAA and other federal and local Personal Health Information laws. Analytics queries can be made on a patient’s prognosis instead while stripping out identity data. Sharing information and images can advance medicine and treatments for diseases like cancer, yet progress doesn’t have to mean sacrificing patient privacy.
Are you looking to extend your structured data application with document and/or image file management functionality? Need to address regulatory compliance, while helping your customers to get better insights from their documents?
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