Speed time to market, ensure regulatorily
compliance and improve decision making.
Data Governance is necessary for compliance with current regulatory expectations for data integrity in pharmaceutical R&D and manufacturing enterprises.
A company should consider whether it has a data governance policy and, if so, whether it is comprehensive and effective. Data governance policies have become a regulatory expectation as one of the core quality system policies. It has been stated by the Medicines and Healthcare products Regulatory Agency (MHRA), European Medicines Agency (EMA), World Health Organization (WHO), Pharmaceutical Inspection Cooperation Scheme (PIC/S), and the Australian government’s Therapeutic Goods Administration (TGA) that a data governance system should be an integral part of the pharmaceutical quality system.
Issuance of a quality system policy, ensuring the integrity of data unless the policy addresses all relevant aspects of the company’s operations, including personnel behaviors and actions. Data integrity breaches can result from poor practices, or inadequate systems/procedures.
Effective Data Quality
Stakeholders have the ultimate responsibility to ensure an effective pharmaceutical quality system is in place to achieve the quality objectives, and that roles, responsibilities, and authorities are defined, communicated, and implemented throughout the company.
Data Risk Management
Decision-makers are responsible for the implementation of systems and procedures to minimize the potential risk to data integrity, and for identifying the residual risk, using risk management techniques. In addition to the legal and ethical responsibilities for ensuring patient safety, the financial risks of poor data integrity justify significant engagement by senior management.
How does the Data Insights Platform
support pharmaceutical manufacturing?
Data Insights Platform delivers a range of Data Governance capabilities to improve your pharmaceutical manufacturing performance so you can deliver safe, effective drugs and other therapies to patients with greater data efficiency and confidence. Robust data governance and quality systems in pharma enterprises not just pave seamless clinical care, but also makes it more reliable for the patients. These are the key drivers of explicable patient outcomes and experiences as well as the productivity of the enterprise.
Clear Definition of Data
Robust Data Quality
Regulatory Policy Management
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