5 top tips to drive data quality

Life science companies are increasingly focused on the need for digital transformation in the pharmaceutical industry. The reality is that basic issues such as getting up-to-date and consistent data to talk to each other across functions and systems are stymieing ambitions.

Regulatory systems contain data on products and their licences. There is also procedural data, recording interactions with the authorities about a licence, from the initial application through to post-authorisation changes to the licence. Elsewhere, expert functions from manufacturing to clinical teams generate the basic data that feed the regulatory dossier that supports the licence. Typically, there is no direct communication between regulatory systems and expert functions systems.

A data-first starting point is key. If companies store clean and consistent data, rather than documents, they will be a in a much better position to automate processes and share this data efficiently with regulatory bodies. Yet, companies continue to struggle with basic data quality issues.

First, there is the compliance issue, where licences must accurately reflect activity relating to clinical trials or manufacturing. In a regulated environment, compliance failure could lead to product recall, licence suspension or fines. Secondly, there are issues tracking changes in data over time. Drugs that are produced over many years will experience changes in for example composition or manufacture. These must be reflected both in regulatory systems and in the company’s operational systems.

Ideally the synching process should be integrated with the regulatory process. That way, when the company introduces improvements to the product, testing data can be shared with the regulator much more quickly.

Here are five practical action points to help get companies started on their data quality journey:

1 Communicate with all the stakeholders involved in the process. Together, identify the use cases for data flow continuity and agree on how best to measure the benefits of automating data integration. Getting everyone’s buy-in and developing solutions collaboratively drives transparency and improves trust among functions.

2 Develop a shared vocabulary to talk about data held in common across functions. Presenting product data across the organisation in a way that everybody understands, with commonality of language, also builds trust as well as driving operational excellence and innovation.

3 Standardise data descriptions. Once use cases have been identified and a common vocabulary agreed, consider how best to standardise data relating to complex products. The IDMP model is a valiant effort to find a common way to describe product data. The quality and consistency of individual data is also key to data standardisation initiatives, such as the US FDA’s drive to standardise Pharmaceutical Quality CMC (PQ-CMC) data elements for electronic submission. The more widely accepted a product model is, the easier it is to share with external parties.

4 Ensure processes are properly aligned. There needs to be a robust process for capturing and sharing changes over time – and making sure that systems keep in sync and that there is as little time lag as possible. Focus on bottlenecks. There may be one process in an operational setting and another in the regulatory section. Where does the data gets exchanged and how could that be improved?

5 Identify suitable technological solutions. The initial focus should not be on finding the right software, but on the system architecture and how and where to connect systems. It is important not to take a static approach – how do I solve the problem now – but also consider maintaining the solution and innovating over time.

Pharma companies are under pressure to accelerate innovation. Technology can provide a platform for efficient and effective innovation based on automation – when it is built on a solid structure of standardised and interoperable data.

About the author

Duncan van Rijsbergen is Associate Director Regulatory Affairs at Iperion, a globally-operating life sciences consultancy firm which is paving the way to digital healthcare, by supporting standardisation and ensuring the right technology, systems and processes are in place to enable insightful business decision-making and innovation.