OR WAIT 15 SECS
Building in the right processes and support for data quality, standards, and governance
Imagine this hypothetical scenario, which is not farfetched in the life sciences industry. Let us assume there is a safety event in a market due to a contaminated drug product and the health authority in that market issues a recall. If the drug sponsor would like to proactively withdraw it from all markets and if the company does not have good data about which markets receive the product from a specific manufacturing site, then the sponsor is likely to be impacted by a number of challenges including loss of revenues, compliance and/or reputation risk, and potential penalties.
The information about market approvals and product manufacturing sites related to approvals is usually managed by regulatory in what is called a Regulatory Information Management (RIM) system.
A recent article in Forbes stated that “While industries such as financial services have had a long track record of managing data, and applying analytics to optimizing customer relationships and developing new services, life sciences companies have only in recent years begun to fully embrace and seize upon the opportunities to organize and apply their data in a systematic way to a range of drug development and patient care challenges.” While the life sciences industry has begun to see the advantages of treating data as a strategic asset, pockets of the industry have always played a key role around data as a strategic asset. For example, the clinical function, due to its very data-driven nature, has always focused significantly on data as a strategic asset given that the safety and efficacy profile of a drug largely depend on good data management practices.
Impact of bad data quality
Data can be a strategic asset only if there are enough processes and support to govern and manage data quality. There are several areas of impact due to bad data quality.
In a recent World Class RIM survey of 65 companies, 62% of them reported that their data standards and data governance processes are inefficient (see figure).
Good data quality could be achieved through people, process and technology considerations (see figure below).
Data governance framework
Strong data quality begins with strong data governance (see figure below). Based on our experience with large data quality initiatives in multiple industry sectors, we recommend the following:
Here, we present a short case study of how a life sciences organization established a strong data quality program. Key objectives of the program were:
We supported this client with the following:
The outcomes that very visible to the organization included:
As a company, we support various aspects of the customer data journey, including current state assessment, defining and implementing data strategy and data governance approaches, designing and implementing master data management, applying visualization and analytics to generate insights and supporting data quality operations.
In summary, a number of the challenges outlined in the introduction about a hypothetical scenario of a contaminated drug being withdrawn can be managed effectively through good data quality and integrated end-to-end visibility into data.
Good data quality starts with the realization that data is a strategic asset with the following key principles to be observed:
About the author
V. “Bala” Balasubramanian, PhD, is Senior Vice President at Orion Innovation and heads the Life Sciences IT division