Breaking down barriers: Data quality as a driver for pharma transformation

The life sciences industry faces many challenges, driving a critical need for rapid digital transformation with data-driven decision-making as the backbone for change. Data enables pharma companies to meet challenges head-on and leverage the power of digital technologies to improve outcomes for patients and drive business success.

But leaning on data and moving it to the cloud is only one part of a bigger story. The industry faces several data quality challenges, including data governance, data silos, and data integration. To overcome these, pharma companies are turning to modern master data management (MDM) platforms that provide a data quality layer that ensures accurate and reliable data. These platforms help manage data quality challenges in real-time, ensuring that business users have the accurate, up-to-date information they need for sound decision making.

In this piece, we explore the significance of data quality in the pharmaceutical industry and explain how a modern MDM platform serves to address challenges.

Digital challenges are everywhere for pharmaceutical companies

There are several reasons why pharmaceutical companies need to rapidly transform and get more from their data.

Growing competition and pressures for cost reduction are forcing pharma to operate more efficiently, while demands for an omnichannel, patient-centric experience require them to rethink their approach to engagement. At the same time, intelligent operations and the adoption of "digital everything" are becoming increasingly important, as companies seek to improve their performance and speed up processes. This is particularly relevant in the context of virtual clinical trials, which are becoming more common and require rapid R&D.

Another key driver of digital transformation in the pharmaceutical industry is the rise of software as a medical device and the adoption of wearables. These technologies are creating new opportunities for companies to gather and analyse data, while also improving patient outcomes and experiences.

As the industry embraces these new technologies, however, it must also contend with stricter new regulations designed to protect patient privacy and ensure the safety and efficacy of new treatments. In all of these scenarios, data plays the central role for transforming pharmaceutical companies. Regulations vary across markets, such as GDPR in the European Union, and are likely to continue changing over time, becoming even more stringent. Standardisation and alignment across functions are critical for establishing a solid foundation in consent. It’s essential to have the right technology in place that will ensure customer-facing teams understand the customers’ most up-to-date consent preferences.

Incomplete and fragmented healthcare professionals (HCP) data also increases compliance risk, as consent and preferences are not captured, maintained, and shared. Inconsistent targeting across multiple channels can result in HCP frustration and loss of trust. Generic, spam-like marketing messages and campaigns can also damage the relationship with HCPs.

Importance of data quality management in clinical trials

The pharmaceutical industry relies on data from multiple sources, including electronic health records (EHRs), patient-generated data, and third-party data. Therefore, it is essential to ensure that the data is accurate, reliable, and accessible. High-quality data enables pharma companies to make better-informed decisions, improve patient outcomes, and comply with industry regulations.

Let's consider the example of a pharmaceutical company that is conducting a clinical trial for a new medication. Clinical trials involve collecting data from multiple sources, including patients, healthcare providers, and laboratories. If the data collected is inaccurate or incomplete, it can have serious consequences for the safety and efficacy of the medication being tested.

For example, if a patient's medical history is not accurately recorded, it could lead to the patient being enrolled in the wrong trial, receiving the wrong dosage of medication, or experiencing adverse effects that could have been prevented with better data quality management.

Similarly, if laboratory test results are inaccurate or incomplete, it could lead to incorrect conclusions about the effectiveness of the medication being tested, potentially delaying or preventing its approval by regulatory agencies.

Data quality is critical at every stage of the drug development process, from pre-clinical research to clinical trials, to post-market surveillance. Ensuring high-quality data not only improves patient safety and outcomes, but also helps companies make better-informed decisions, drive innovation, and comply with regulatory requirements.

Modern MDM’s leverage AI and ML to enhance data quality

Modern MDM Solutions help solve the data quality challenge by unifying data from multiple sources and enabling companies to manage and maintain high-quality data. A modern MDM platform includes a data quality layer that uses machine learning (ML) and artificial intelligence (AI) capabilities to ensure accurate and reliable data.

With a modern MDM platform, pharmaceutical companies can identify and correct data quality issues before they cause downstream problems. For instance, a data quality layer can detect duplicate records and merge them into a single master record, which helps eliminate inconsistencies and inaccuracies in the data. Additionally, a modern MDM platform allows users to create rules and workflows to manage data quality and ensure compliance with industry regulations.

Managing billions of records requires the right tools

Managing data quality is challenging, especially for pharmaceutical companies that work with billions of records. To manage data quality effectively, transparency is crucial. It is essential to be aware of issues and run scans to identify similar records using a modern MDM system. Implementing safeguards to prevent data loss or overwriting is critical to maintaining data accuracy. When issues occur, it is vital to communicate transparently with customers and provide solutions to any problems they may have experienced.

A modern MDM platform provides a data quality layer that helps to manage data quality challenges, including data governance, data silos, and data integration. Implementing data quality standards and maintaining data quality through data profiling and monitoring is crucial to ensure data accuracy, reliability, and accessibility. Managing data quality in MDM applications requires transparency, awareness, and implementing safeguards to prevent data loss or overwriting.

Overall, modern MDM solutions help pharma companies improve their data quality management practices and ensure that they have accurate and reliable data for making better-informed decisions and complying with industry regulations. Accurate and reliable data enables pharma companies to make better-informed decisions, drive better patient outcomes, and comply with industry regulations.