This Pathfinder paper navigates decision-makers through the issues surrounding a specific technology or business case, explores the business value of adoption, and recommends the range of considerations and concrete next steps in the decision-making process.
How Sinequa Helps Organizations Meet Compliance Regulations.
Data privacy and data protection regulations continue to evolve. Against this backdrop, it is not uncommon for risk-averse organizations to mistakenly believe that compliance requirements conflict with their initiatives to use data to derive business insights. It is true that certain subsets of highly sensitive personal data are restricted in terms of processing or access. However, the data management underpinnings that are necessary to meet ‘reactive’ compliance requirements are also the capabilities needed to support more ‘proactive’ and flexible uses, such as exploratory analytics.
- The ability to accurately navigate, search and identify data is the foundation of proactive content governance and management, including data privacy compliance
- Businesses today largely perceive information governance to be an enabler of business value rather than a cost center
- Data privacy concerns are ranked as a top barrier to being more data-driven as an organization. Data privacy capabilities can alleviate these concerns
- Many organizations struggle to even know their compliance-readiness stance. This is in part due to the inability to understand existing data. The inability to quickly and efficiently identify personally identifiable information (PII) is a persistent risk
- The number and complexity of data silos within organizations presents a challenge in the consistent management and leverage of data. Velocity of content creation within the organization is another factor that complicates management
- Alignment of business objectives is needed to maximize value. A comprehensive approach to data management and search can accelerate proactive initiatives just as much as compliance.
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Finding relevant knowledge in the complex and diverse data of Biopharma companies requires cognitive systems using Natural Language Processing (NLP) capable of "understanding" what unstructured data from texts and videos is about. This whitepaper highlights how Cognitive Search and Analytics are key elements for driving innovation, improving the efficiency of research, clinical trials, and regulatory processes.