Pharmaceutical companies are often among the largest and most complicated organizations in the private sector, which are constantly under close scrutiny and subject to complex regulation. Their business success depends on the timely and successful completion of clinical trials. Clinical trials generate incredibly complex data and unless there are efficient processes and modern technology in place, numerous problems can occur. Without the right processes and supporting technology, pharmaceutical companies can pursue a flawed trial or flawed compound for too long and fail to cut their losses. Even if the trial design is good and the compound is promising, the inability to collect, manage, analyze, and package the clinical data can produce long delays that cause costs to soar and keep the drug from hitting the market.
A deeper look at each step of the drug development process reveals a recurring theme of missing, incomplete, or erroneous data that wastes time and resources and adds months or even years to the drug introduction process.
It is imperative for pharma companies to master the key entities in clinical trial data for real time feedback on trial progress and patient compliance, reducing the overall elapsed time and cost of clinical trials.
Current data issues in the pharma industry
Lack of consistency in creating unique identifiers for products, projects, studies, sites, investigators, and other key data across the company hampers project management and cross-functional communication and coordination
Insufficient enforcement of clinical study data standards significantly increases processing cost at multiple stages and compromises study quality – potentially placing patient safety and entire development projects at risk
Difficulty in locating documents in a timely manner, due to inaccurate or incomplete document metadata tags, poses (potentially critical) risks to regulatory submissions and inspections
When is data considered to be of high quality?
When the data:
- Is accurate, current, consistent, complete, and relevant
- Possesses integrity (e.g., unique identifiers, no duplicates, and parent-child records are properly linked)
- Can be easily accessed across systems
- Can be easily processed and analyzed for multiple uses
- Retains all of the above, even as data volumes grow and new data sources and integrations are introduced
How can this be achieved?
A robust Master Data Management (MDM) Solution can help achieve all of the above by offering a customized solution in the near term, mid term, and long term, aligning to the strategic needs of the customer.
Master data management has helped customers achieve great successes in other industries. It’s now time for the pharma industry to use this solution to achieve the below benefits:
- Simplified business operations, standardized processes and better cross functional collaboration for study, site, product, project, investigator, and subject enterprise assets management
- Single source of truth to publish these key entities to all the required stakeholders/systems across the enterprise
- Facilitate connecting the dots to identify new business opportunities and facilitate strategic decision making by providing consistent, consolidated, standardized, enriched, accurate, and inter-related master data to reporting and analytical systems
The global digital disruption affects the pharmaceutical industry as much as any other industry. The companies that succeed will be the ones that best manage, use, and share data.
Transformation, however, is not necessarily an immediate change. Especially in large companies, new processes and technologies will be implemented over time and potentially a large number of individual projects. Each project will need its own justification, delivering a measurable return on investment. Using MDM to deliver the master data needed for each project’s success accelerates RoI on each project by making it easy to rapidly find and reuse the data required for an individual project.
MDM helps us by providing processes for how we collect, summarize, and cleanse our data to ensure consistency, and appropriate governance in the ongoing maintenance and use of this data.