Why data governance should be a top-of-mind priority for life sciences leaders today

From clinical readings and medical prescriptions to patient treatment journeys, surgical records, laboratory tests, insurance claims, and more – an exponential amount of healthcare data is generated every day, trickling in from every activity across the life sciences ecosystem. Each of these data sets contributes to an endless stream of valuable information that benefits life sciences organizations in more ways than one, including helping them better understand diseases, accelerate the development of drugs, create winning commercial strategies, and a lot more.

Crucial to this success is an effective data governance system - one that is necessary to tame and convert the unstructured and siloed healthcare data into a consistent, secure, reliable, and accurate format. The lack of this can potentially complicate data integration efforts and create data integrity issues that affect the accuracy of healthcare reporting and analytics applications. This eventually holds organizations back from extracting relevant, business-critical insights and driving promising healthcare and commercial outcomes.

In this article, we delve deeper into what makes data governance so important for life sciences organizations and why it should evolve from being a mere afterthought to a core component of an organization’s data management strategy.

The life sciences industry is highly regulated and governed by strict health data protection laws (such as the Health Insurance Portability and Accountability Act), making it crucial for organizations to protect customer privacy. This is done by anonymizing data related to Personally Identifiable Information (PII) and Protected Health Information (PHI) - keeping them secure and out of the hands of anyone who doesn't need it.

Customers whose PII/PHI is breached or lost may be susceptible to having their confidential health records disclosed and may also suffer from financial or medical identity theft. In fact, it has been estimated that lost or stolen PHI may cost the US healthcare industry up to $7 billion annually¹

Good data governance helps organizations prevent this. With robust metadata management, data governance can recommend standards and procedures to safeguard customer data, prevent sensitive data from being added to applications, monitor and align with regulatory compliances as they evolve, control data ownership and access, reconcile privacy and security issues, etc.

Besides protecting patient privacy and maintaining regulatory compliance, organizations must also ensure that they are proactively protecting customer data from internal and external breaches. The 2021 cost of healthcare data breaches soared to an average of $9.3 million per occurrence, a 29.5% increase over 2020’s average of $7.13 million².

Data governance can help life sciences organizations prevent such breaches to a large extent. Policies under this framework are typically designed to help organizations identify and segregate sensitive customer data from the rest – all through an effective data management and filing system. Here’s what essentially happens:

This way, with highly-sensitive files separated and stored behind a firewall, users won’t risk accidentally sharing that data.

Data scientists help life sciences organizations make better use of healthcare data by extracting critical insights through advanced data exploration techniques and predictive analysis. However, activities such as data identification, cleansing, wrangling, understanding the definitions and inter-relationships of each data set, etc. take up most of their daily bandwidth (roughly 80%³). Here's where data governance can help. With uniform data architectures and standardized data assets, a governance strategy goes beyond seamlessly capturing, curating, and storing data to providing a means for searching, sharing, transferring, analyzing, and visualizing data effortlessly. This naturally gives data scientists more time to maximize the value of data, run predictive analysis at scale, extract relevant insights, and help organizations drive better business decisions when they matter the most.

A well-crafted data governance strategy enables the timely application of business analytics. Life sciences organizations can have a great product strategy in mind, but if the strategy isn’t impeccably data-driven and timely, organizations likely won’t reach their full potential. Having an effective data governance tactic in place can help them improve all aspects of their business workflow and operational processes, allowing them to improve data gathering, storing, and processing. This way, they can perform deep, predictive data analysis across different use cases including:

Developing an effective data governance program is no small task and is a continuing process. It should not suddenly appear as a fully formed, mature system but should be allowed to evolve incrementally, being adjusted and modified with each phase. Building a successful data system relies on first identifying present data challenges, prioritizing key business goals, and then establishing data-gathering processes and structures around those goals – all while ensuring the integrity of your data.

Stay tuned for our next blog, where we will lay down the foundational blueprint of what it takes to build a high-performing and effective data governance model.