According to a study by Tufts CSDD, Phase III clinical trials alone generate an average of 3.6 million data points.
An intricate web and massive amount of personal data weaves through clinical trials, patient insights, and genomics—each strand holding the potential for groundbreaking discoveries.
However, the pursuit of novel drugs and treatments unfolds against the backdrop of privacy laws and data protection regulations. Properly handling, safeguarding, and using this data in compliance with laws and ethical guidelines pose significant challenges.
By properly discovering and managing this data, Pharmaceutical companies can improve data usability, accuracy, and efficiency of their research and development efforts, ultimately leading to better patient healthcare outcomes and compliance with privacy laws.
- An enormous amount of data is collected and processed daily from different sources and channels. It is essential that this data is used in accordance with laws and ethical guidelines.
- Personal health information, genetic information, and financial information are among the sensitive data that must be secured and appropriately handled to avoid both financial and reputational loss.
- The amount of data that is collected and generated creates a non-trivial problem of keeping track of the data you have, where it comes from, who has access to it, and how it is being used.
Using Data Discovery and Classification for Transformational Results
- Streamlines your daily operations and ensures that daily tasks, such as correspondence with customers and partners, are secure and compliant.
- Restricts access to personal data you collect and manage to the authorized personnel.
- Enables you to make data-driven decisions and proactively react to any regulatory change
- Going beyond the business-critical or operational data. Having a powerful discovery solution can add another layer of intelligence to processes, such as data loss prevention, data archiving, and data removal.
- The unobtrusive nature of the Data Discovery software, with its scheduled runs outside operational hours, aids in understanding and managing the data that is stored across the systems – both in files and relational databases, ultimately ensuring that data is used for appropriate purposes and that appropriate data governance controls are in place.
DPM Data Discovery Solution
DPM Data Discovery is our personal data classification software, developed from over ten years of experience in the privacy sector.
DPM Data Discovery uses advanced technologies such as machine learning and natural language processing (NLP) to discover, classify, and identify personal data efficiently and accurately.
Ensuring Compliance and Privacy-driven Performance
- DPM Data Discovery classification engine identifies different types of data – from personal data in its strict sense (names or addresses) to medical data (drugs and dosages) to financial data (salaries or credit card numbers).
- By applying distinct visual labels to each data category, the DPM Data Discovery allows you to see personal data in your systems at a glance.
- Since our classification engine relies on machine learning, it is not dependent on column or document names. It bases its decision on the actual data snippet, thus being more accurate and allowing companies to discover personal data where they might not expect it.
- The secure Data Inventory sub-module contains all labels and dashboards to overview all found data domains without data leaving the company systems.
DPM Data Discovery results are an efficient, streamlined business process with data privacy and security at its core, allowing companies to respond promptly and confidently to regulatory changes.
State-of-the-Art Data Discovery
Looking at the amount of data Pharmaceutical companies generate on the one hand and the different regulatory changes. There are several reasons to use state-of-the-art Data Discovery.
Pharmaceutical companies operating in the modern privacy-driven ecosystem are subject to various regulations requiring them to handle personal data responsibly and dynamically.
DPM Data Discovery is designed to help companies comply with regulations such as HIPAA and GDPR by providing tools for identifying, classifying, and protecting personal data and allowing them to know the locations of personal data in their systems.
Furthermore, DPM Data Discovery includes features for conducting risk assessments, data protection impact assessments, and incident management.
2. DATA GOVERNANCE
Across the sector, Pharmaceutical companies often have complex data governance processes, with multiple departments, systems, and locations involved in handling personal data.
Ensuring consistent and compliant data management across the organization can be challenging. Data Discovery allows you to automate personal data classification, enabling you to establish and maintain control over data.
Having these domains readily available can help ensure that personal data is handled consistently and competently across the organization.
3. DATA PROTECTION
Knowing where personal data resides is a prerequisite to protect it from unauthorized access, disclosure, or loss.
Data Discovery allows you to dynamically set up access controls and points to highly sensitive data that should be encrypted.
4. AUDITING AND REPORTING
Allows easy auditing and reporting on controls and measures for systems where personal data is located. It can also ensure that appropriate additional controls are implemented, i.e., unstructured files on servers.
Data Discovery as a Necessity in Pharmaceutical Industry
In response to escalating costs and strict regulations, the Pharmaceutical industry must embrace digital transformation as a strategic path to revitalize its operations.
Recognizing the pivotal role of digital transformation in every area, including privacy, is key to ensuring the secure handling of sensitive data and compliance in an increasingly digitized pharmaceutical landscape.
In this sense, data is a foundation. Without effective tools for data discovery, classification, and management, even the most valuable data loses its significance.