#FutureReadyHealthcare
Building and Scaling Robust and Effective Enterprise Data Governance in Life Sciences
Insights from Practitioners' Experience
Life sciences companies must consistently assess and strategically invest in their enterprise data and analytics (D&A) capabilities to remain competitive and drive value. However, these capabilities can only be fueled by reliable, high-quality, and readily available data, and robust data governance is the key to ensure your data meet these pre-requisites.
As companies embark on their digital transformation journey, governing enterprise data assets has emerged as one of the core competencies of modern life sciences organizations. Despite this recognition, companies still encounter challenges related to ineffective and unscalable data governance.
In this whitepaper, we delve into:
Strategic importance of data governance as an enterprise capability
Data governance in data and analytics maturity continuum
Key design principles, framework, and best practices for comprehensive and scalable data governance
Strategic approach to implementing and evolving maturity of data governance
Key success measures for data governance linked with business value to help you achieve excellence in data governance.
Leading with customer centricity
Customers have taken center stage in the commercial model across industries, and life sciences is clearly making strides toward customer-centricity by emphasizing digitization and automation. However, some companies are still grappling with the challenge of achieving customer-centricity and personalization, which requires a more sophisticated, data-driven approach. According to a recent study by DT Consulting (an Indegene company), pharma companies are falling short of providing excellent experiences to healthcare professionals (HCPs) with a CXQ® score of only 591.
Life sciences organizations recognize that customer-centricity becomes more attainable when decision-making is informed by data. Consequently, they are increasingly embracing strategies that leverage data to enhance customer experience and drive business outcomes.
Why data-driven decision-making is the new standard
Typically, a life sciences organization collects, processes, and consumes data from various internal and external sources. This data may come from
Research and development
Genomics and registries
Clinical trials
Medical affairs
Market and customer research
Payer and provider networks
Claims
Customer profiling
Promotional activity
Customer engagement initiatives from field sales and omnichannel marketing
Patient support services
and others
Undoubtedly, the data volume, variety, velocity, and veracity are expected to increase year on year. As organizations build new use cases and integrate multi-modal or generative AI and analytics-enabled business processes across the life sciences value chain, the pace and complexity of operations will further amplify.
It is the ability to govern such diverse data assets in a systematic and compliant manner that is the key to smarter and informed decision-making.
Key business drivers behind the shift toward data-driven decision-making
New product launches
The need for timely and actionable information at several stages in launch lifecycle, specially between T-12 to T+12 timeframe
Evolving market and competitive landscape
An increased focus on crafting and delivering customer-centric, omnichannel engagements
Proliferation of healthcare ecosystem, partners, and data sources
New business opportunities derived from vast amount of data—connected devices (personal mobile health apps, IoT/wearables), dense digital profiles and digital footprint of HCPs and patients, and anonymous patient level data (APLD).
Data governance: The glue that holds your data together and
drives value
Data governance can be defined as a proactive and systematic function for managing data assets and ecosystem through well-designed policies, standards, processes, roles and accountabilities. It also handles change to drive business value and ensure compliance.
Data governance is not a one-time project or exercise, rather an ongoing business and strategic activity.
Why do you need robust and scalable data governance
Ineffective data governance can lead to multiple challenges.
Slow decision-making due to unclear data and processes ownership
Legal implications following regulatory/ complaince risk
Data silos, redundancies, and high cost of acquisition
Issues related to data quality, integrity, and trustworthiness
Subpar understanding of data and its limitaions due to low data literacy
A robust and scalable data governance can help organizations achieve the following.
Comply with industry-specific compliance regulations such as HIPAA, HITECH, and GDPR, and avoid operational, financial, or legal risks
Proactively ensure data security and protection of PII to elevate customer trust and eliminate regulatory or legal challenges.
Improve trust in data, reduce data redundancies and optimize cost.
Enable systems of insights such as dashboards and analytics applications to deliver real-time and actionable insights based on trustworthy and reliable data.
Integrate predictive and prescriptive analytics into day-to-day business processes and functional tasks.
Shift toward data-driven organizational culture, data expertise and advisory support, and data democratization, elevating organizational Analytics Quotient (AQ)
Data governance from the lens of data and analytics maturity
Thoughtful investment in data governance capabilities promotes analytics maturity and drives significant business impact. Top 30 pharma companies are revisiting or planning to assess the current state of data governance and prioritizing initiatives to address the gaps and map pathways to elevate their capabilities2.
As data and analytics maturity improves, data governance capabilities need to align with it at a similar pace and scale.
Building a solid data governance framework for business excellence
Based on our experience working with data and analytics leaders across top biopharma, biotech and medical devices companies, we have outlined key guiding principles for robust data governance.
Guiding principles
Comprehensive and scalable:
Encompassing business functions, business units, external partners, data domains and end-to-end data lifecycle.
Flexible and extendable:
Adaptable to new data sources, ever-evolving compliance requirements, and geography or market-specific nuances.
Value creation:
Value for every stakeholder group to maintain momentum toward a data-driven organization.
Collaborative:
Shared accountability between business unit/global market stewards, data consumers, and data governance council comprising of data stewards, analysts, business stakeholders, and executives. Maintain balance in data governance structure as well as roles and responsibilities across stakeholders.
Measurable:
Well-defined outcomes and metrics for measuring success, aligning with overall business objectives and value.
Bringing it all together: Data governance 2.0 with GenAI
Here is a high-level, foundational Data Governance Framework built on the basis of above guiding principles, embracing GenAI.
Tailoring the framework for better organizational integration
Align each building block and process group with your organizational context, business needs and priorities
Pair detailed policies, standards, roles-responsibilities, and processes paired with supporting templates
Leverage right tools and GenAI to
Build a comprehensive data catalog with rich metadata and contextual information, and intuitive UI layer for easy search and discovery, profiling, and data insights
Streamline operations for scale and flexibility
GenAI has the potential to enhance various aspects and activities of data governance. Here’s how it can make a significant difference.
Creating or updating metadata labels, especially for unstructured data
Extracting and summarizing relevant information from vendor data contracts to build first-cut data profile, licensing information, permissible use and other contractual information.
Automatically updating data lineage and maintaining traceability
Detecting data quality issues, enabling data cleansing
Data filtering to weed out incomplete or unusable parts of data
Compliance checks and data de-identification
Data Governance teams should explore opportunities and run proof of concepts or pilot projects in key areas to stay one step ahead in the ever-evolving data landscape. This way they can not only adapt but also serve as effective enablers of innovation and progress.
Implementing and scaling data governance across your organization
Crafting the best-fit data governance framework for an enterprise is no small feat. It’s not just a project but a strategic initiative that needs support and sponsorship from senior leadership.
Here’s a simple guide to get you started.
Navigating common pitfalls and leveraging best practices
The following best practices can help you tackle potential roadblocks proactively.
Active involvement and support of key stakeholders
Have your key stakeholders onboard from the beginning. Their buy-in can help you align the initiative with broader organizational goals and secure the resources needed for successful implementation.
Progressive adaptation and automation
Rollout your data governance framework in a phased manner. It is advisable to introduce relevant data catalog tools and interactive self-service capabilities in the same way. This allows for incremental improvements and adaptation based on feedback and outcomes, while avoiding overwhelming the organization with a sudden, large-scale change.
Pragmatic frameworks and SOPs
Develop data governance framework that is clear and adaptable. This approach prevents your framework and SOPs from becoming overly rigid while ensuring flexibility and scalability.
Rewards and recognition
Link the data governance KPIs with strategic business goals, track KPIs and communicate value to the leadership at every step. Recognize and reward the efforts of those who drive the success of your data governance program including the core team, champions, and early adopters.
Setting KPIs: Assessing impact and success
To gauge the success of your data governance program, consider using objective key performance indicators (KPIs) across categories such as adoption, compliance, and efficiency. These measures provide a comprehensive view of how well your data governance strategies are working and where you can improve.
Conclusion
Data Governance is a critical lever in elevating and enabling data and analytics maturity of your organization. Without it, organizations face risks related to data quality, inefficient resource use, and impaired decision-making. Data governance requires a thoughtful approach with the support and sponsorship of leadership. Partnering with subject matter experts in data, analytics, and life sciences can help you objectively assess where you stand and benchmark the current state of data governance. This helps build a pragmatic roadmap and design a comprehensive framework and SOPs with right data management tools, ensuring successful adoption. By tracking success with clear KPIs and adapting as you go, you can build a strong data governance program that drives lasting business success.
References
1.
Tyer, D., Tongeren, T. van, Price, H., & Lee, E. (2023, October 12). The state of customer experience in the Global Pharmaceutical Industry, 2022: HCP Interactions. DT Consulting.
2.
Bisson, P., Hall, B., McCarthy, B., & Rifai, K. (2018, May 22). Breaking away: The secrets to scaling analytics. McKinsey & Company.
Authors
Prasad Adavadkar
AVP – Strategic Business Solutions, Indegene
K Rudra Shakar Reddy
Sr Director – Data Management, Indegene
Medha Rawat
Marketing Manager, Indegene