Why is Data Integrity Critical in the Pharma Industry?

This perspective will clarify the concept of DI, identify its implications for business owners, and the link between data governance and industry best practices. Finally, it outlines actionable steps for integrating big data and DI into existing operations and will provide an understanding why the ability to generate value by identifying useful information from digital detritus will be a key indicator of business longevity in the pharmaceutical industry.

Figure 1. Large data sets and advanced analytics can stimulate adoption of innovative technologies, integration of automation systems, and boost decision making process assuring production of high-quality drug products.2

To ensure the success of drug manufacturers, there is a need to adopt a holistic view of the business operations that encompasses processes, culture, and technology.3 In this relation, DI helps to ensure that data in an organization is complete, trustworthy, consistent, and accurate throughout the lifecycle of the product.3 ^,4 DI requires an environment in which solutions and ideas can evolve and be developed to manage, control, and compliantly use of data. It is a fundamental pillar in the pharmaceutical industry, ensuring that medicines are of the required quality and safe to the patients. You can build up this pillar by following DI controls that allow making a step forward towards the integration of new technologies, digital innovation, and ultimately automated production systems (Fig. 1). The integration of these production systems is crucial to upgrade and replace the outdated systems with improved technologies that will better support its operations.

Among strategic directions in implementing new technologies, for example, a special place is occupied by cloud technologies, analytics of large amounts of data, and integration of mobile devices and technologies of social networks into the corporate environment. Combining these technologies and processes brings together the collective term "Third Platform," which will lead to the transformation of business models in most industries in the next few years. Consequently, ingraining DI into your processes now will improve the efficiency and productivity of your organization in the future while striving towards high-quality products.

Companies can only prove the quality of their products by showing data produced during their production processes. In other words, the data quality determines the possibility for companies to guarantee the quality of their products. Data records are the only proof that your production process is performed according to the quality standards. The formal management of records and data throughout the regulated company is ensured by data governance.3 ^, 4 Data governance encompasses the people, processes, and technology required for effective data handling that eventually results in high-quality products (Fig. 2). Therefore, regulators and health agencies, during inspections nowadays, focus more on how companies manage their data, enforcing data integrity-related guidelines that help set the controls in the organization over the data.

Figure 2. Elements of the Data Governance Framework.3

If you can demonstrate what controls you have in place to prove trust in the data in your organization, this means that every data lifecycle step, the GxP (IT and non-IT) systems, and all related processes are in control. Here, understanding the fundamentals of DI and defining the maturity level of DI in your organization is the first step towards a tailor-made DI program that can facilitate the security and quality of the collected data during product manufacturing.3 This will help demonstrate DI controls, and your organization may consider implementing a corporate DI maturity program.

A DI maturity program uses standard rules and procedures that will take the organization through all aspects of DI. It will support your organization towards a safe environment and a strong culture by properly managing data, ensuring high-quality standards, and improving efficiency. On top of ensuring a high-quality product, your business’s core processes’ costs will be significantly reduced. A DI program will also help you to identify, remediate, and manage potential risks to DI.

Actionable steps for integrating DI controls into existing operations:

• Assess DI maturity level as early as possible
• Draft a goal-oriented plan
• Control collection, management and data storage
• Focus on company culture
• Demonstrate by examples and educate staff

Realizing the potential of big data is a challenge for business owners, but it also creates an opportunity. Large data sets and advanced analytics can lead to new products, boost existing services, substantially improve decision making, mitigate and minimize risks, and produce valuable insights about operations and consumer sentiment. Therefore, DI is essential for reshaping the pharmaceutical industry and triggering significant innovation for ensuring the reliability and trustworthiness of the information. In the future, the importance of implementing measures and practices that frame the integrity of the collected data through the whole life cycle of a product will increase. Those regulated industries, which implement first the DI practices and a DI program, are likely to gain significant advantages over their competitors.

• 2Image credit: nicoelnino © 123RF.com
• 3 a b c d e
• 4 a b