In the pharma and medical technology industries, the term “data governance” can conjure up different meanings depending on the operational and experiential lens through which the term is being perceived:

• Executives are likely wondering if the ROI of implementing modern data governance technology and processes justifies the investment of time, money, and resources.
• Compliance and privacy officers managing organizational risk and assuring compliance with global regulations and privacy laws are likely aggressively advocating for immediate adoption of leading-edge data governance tech.
• Sales and marketing are likely exclusively interested in the ways their organization’s data can be leveraged to support their acquisition, sales, and revenue goals – and hoping data governance doesn’t equate to limitations.

Perceptions aside, the realities of compliance and the current shift towards data-driven decisions will soon force IT leadership to implement enterprise-wide governance platforms.

However, the spreadsheets and manual processes once associated with data governance only a short time ago are being rendered obsolete rapidly, in consideration of:

• Speed, volume, and varieties of available data
• Rapid demand for data-driven business solutions and productization of services
• Inevitable integration of data technology with industry-specific generative AI and Large Language Model (LLM) innovations

All constituents would benefit from a fresh look at the advanced capabilities of modern data governance when effectively infused with the latest technologies – and the quantum leap forward on the horizon as the currently unknown potential of AI in this area is unveiled. LLMs can turn raw data into a steady flow of predictions, insights, and business recommendations across an entire global organization with unprecedented speed. However, advanced data governance will be needed to protect life sciences organizations from unintentional non-compliance; while providing sought-after capabilities to empower every department to multiply productivity and performance with access to the precise information they need to make well-informed decisions.

Data quality always comes first

Before exploring the benefits and capabilities of data governance technology, it is crucial to understand the importance of using clean, reliable data to fuel governance solutions. Inaccurate or incomplete (meta)data has a negative impact on the performance and adoption of any data-driven solutions, but that impact is magnified in the case of healthcare data and the complex rules and requirements that regulate its access and use.

Life sciences early adopters of a technology-first business philosophy are enthusiastic about adopting new innovations and exploring the expanded functional possibilities. However, the potential downstream negative impacts of multi-layered, enterprise-wide decision-making based on unverified and inaccurate data are practically immeasurable. While an organization embarks on the implementation of a full-scale data governance solution, prioritizing the mastery of a high-performance master data management (MDM) is highly recommended to ensure data and metadata are always current, accurate and as complete as possible. In short, the potential negative outcomes of “garbage in – garbage out” are multiplied exponentially when unreliable data is fueling every aspect of business operations, from growth initiatives in sales and marketing to risk management and data compliance.

Quality data builds trust and adoption

All life sciences companies would agree with the principle of fully integrating data into their operational DNA. However, if those being asked to depend on data don't trust the data provided, they will quickly regress to legacy data management methods. Users need assurance on questions like these:

• Am I leveraging quality data to drive my business decisions?
• Is it going to give biased results?
• Is the data clean enough to train generative AI models?

What users of data inherently trust most is their own experience and observed results. If technologies, tools, and processes are built upon a foundation of quality data, the results will almost certainly be favorable, and users will find value in learning to leverage new data solutions effectively.

Data governance for compliance

Many perceive data governance to be the policies, documents, and contracts – but the key is to include people as a main ingredient in the process. While most life sciences companies have defined policies and procedures around the management, accessibility and use of data, the actual governing of those policies and procedures can be difficult without the right data management structure in place. Changes like staff turnover, international expansion into new regions, regulatory updates, etc., can turn inflexible static policies into unsecured liabilities overnight. An intelligent, flexible, and responsive data governance platform provides the necessary structure to manage and adapt to the constant changes that can threaten compliance adherence. Once a quality data governance platform has been properly configured, organizations gain:

• Built-in guardrails to ensure the appropriate use of the data
• Checks and balances to eliminate unintentional errors
• Dynamic control to ensure confident legal compliance
• Making data governance more a habit than a task

If risk managers and compliance officers are having difficulty convincing upper management to invest in a quality data governance solution, a review of the potential negative consequences of failure to comply with various regulations can be convincing, as reputational damage with clients, prospects and partners can significantly impact a brand long-term.

Enhancing business performance with data governance

Because most people associate data governance with following rules, they underestimate its ability to produce new business insights, inform business decisions and inspire creative ways to leverage data for enhanced operational performance. However, the IT department is traditionally (and often reluctantly) the gatekeeper of everything related to data. Need a report? Ask IT. Need a segmented prospect list? Ask IT. That is about to change as modern data governance and catalog technologies drive data democratization and data literacy. Sophisticated, user-friendly data cataloging tools can elevate novice data consumers with no IT expertise into highly effective, self-governing data citizens with the ability to segment, analyze and understand the many types of data that are available to them. Analogous to a simple Google search, users can enter their own data queries based on logical, common-sense questions and gain access to the precise data needed for specific business objectives.

Modern catalogs already turbo-charged with AI technologies enable users to simply enter the desired business objective and instantly receive the data, reports or other outputs needed to achieve it. In the near future, individual data consumers will be limited only by their own ability to ask smart questions and imagine creative new use cases.

Today’s data governance solutions are flexible, reliable, and extremely user-friendly. They provide secure, structured access to data to the right users, and instill confidence that rules are being followed and requirements are being met, which results in the freedom to explore the boundaries of data. A tech-enabled data governance provides this freedom and data literacy to the enterprise data citizens to perform at their best with improved knowledge, access, and control of the data.

Compliance and the prevention of noncompliance are reasons enough for life sciences companies to commit to implementing capable data governance technology. The expanded capacities, efficiencies and capabilities being constantly unlocked through creative AI integration are making the investment that much more worthwhile.

IQVIA supports global pharma, medical device and healthcare companies in their data governance and stewardship needs. You can learn more about IQVIA’s Data Governance and Stewardship capabilities at our website.