Staying Ahead of the Evolving Data Paradigm

The healthcare data landscape continues to evolve, driven by cloud computing and the increasing technical expertise of business users. While these exciting changes empower users with greater access to data, they also necessitate a fresh data strategy and governance perspective to support key data-driven decision-making.?

Modern data governance is not merely about control and access. It fosters collaboration and promotes a data-driven culture. There is a need for:?

• Breaking Down Silos: Encouraging open communication and collaboration between IT and business teams.?

• Democratizing Data Assets: Providing analysts with the necessary tools and access while maintaining security and compliance.?

• Cultivating Data Literacy: Equipping stakeholders with the skills to understand and interpret data effectively.?

Resolving Discrepancies?

The weakest link in data-related processes is always the human factor. Every story has three sides: your side, my side, and the truth. Data can also be interpreted in different ways, leading to conflicting conclusions.??

For example, different users may use different approaches to evaluate a metric, leading to discrepancies in results. Let's say User A, new to the team, needs to evaluate the 'Total revenue generated by the sales of Brand A.' He uses the Xponent dataset to calculate revenue as TRx (Total Prescriptions) multiplied by the average price per unit. However, this approach may not be ideal.?

On the other hand, User B, who is more familiar with the business, relies on Specialty Pharmacies (SP) dispense datasets to evaluate any sales-related metric for reporting. These datasets typically contain more accurate information on actual sales transactions.?

The disparate data sources cause a mismatch in results obtained by both the users, leading to confusion and potential misinterpretations of 'Total revenue generated by the sales of Brand A.' While it is well known that Xponent data is projected owing to its limited coverage, it is recommended to use SP dispense data for evaluating sales metrics. User A might not be aware of this nuance or the availability of SP datasets in the first place; the absence of a single source of truth makes these reporting errors prevalent.??

Similar challenges are very common across teams. To address these challenges, there is a need to prioritize:?

• Clear and Concise Documentation: Documenting data sources, definitions, and calculations is paramount.?

• Building Consensus: Establishing agreed-upon definitions and metrics across the organization is crucial.?

• Data Ownership: Clearly defining roles and responsibilities for tech and business teams for data maintenance and updates.?

• Actionable Recommendations: Guiding interpreting and utilizing data to drive business outcomes.?

Conclusion?

In the constantly evolving data landscape, effective data governance is not a luxury—it's a necessity. By embracing collaboration, empowering analysts, and prioritizing clear documentation, organizations can unlock the full potential of their data and drive meaningful business results.?

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I’d love to discuss how we can implement effective data governance strategies for your team. Let's connect and see how we can make your data work smarter and more efficiently!?

Feel free to drop me a note or connect with me on LinkedIn.?

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