Pharma Data Management

Pharma Data Management: Pre-built vs Custom Solution?

Most pharma organizations consider ‘data’ as a key focus area to forge emerging business models by harnessing its immense potential. Data is crucial to fulfilling critical decision making across the Pharma value chain. Today, IT functions in most organizations focus on areas such as data governance, data quality, access management, model configuration, etc. along with data maintenance, processing, and storage. A robust data management solution is a critical IT requirement across pharma organizations. With the advent of cloud infrastructure and new-age technologies, most of the pharma organizations are now preferring advanced next-generation data management solutions for cleaned, integrated and streamlined data on-demand, along with intelligent insights, leading to better decision-making driving long-term profitability.

While working with some of the top pharma companies across the globe, we observed specific trends. On one hand, IT teams are inclined towards ready-made or pre-built data management solutions, and on the other, business teams are looking for custom-built data management solutions with self-serve data management capabilities to reduce the dependency on IT. This difference in priorities further makes it complicated for organizations to decide between pre-built and custom solutions.

To make this decision more collaborative and constructive for pharma companies, we recommend considering the following critical factors while deciding between a pre-built and a custom data management solution.

IT Readiness and Maturity

Pharma organizations’ internal competencies and expertise in data management are critical factors to consider while deciding between pre-built and custom data management solutions. While finding custom data management solutions, pharma organizations need to evaluate whether:

  1. Their long-term IT strategy is aligned to build their own data management solution.
  2. They are mature enough to drive, implement, and support data management solutions successfully
  3. Their IT team has in-house data management consultants or experts to oversee support services
  4. They are ready to invest in the time to build a custom solution and lost out on the time-to-value for intelligent decision-making.

If the answer to even three of the four questions is a ‘no,’ most pharma companies would prefer to focus on core business activities and not on an extensive IT set-up.

Business Scale and Size

It is essential to consider the specific business challenges and requirements while deciding between the two types of data management solutions. Problems of large pharma differ significantly from those of small/ medium business (SMB) pharma companies, so they must be evaluated critically as per organization needs. Along with scale and size, many other factors differentiate an SMB pharma from large pharma, such as top-level strategies, research, and clinical set-up, selling models, business functions, organizational hierarchies, etc.

  1. While large pharma organizations prefer an enterprise-wide big data revolution, SMB pharma organizations are likely to focus on easy fixes for rapid data integration and swift commercial operations.
  2. For large pharma organizations, the data management solution needs to cater to a broad set of business use cases across user personas, such as:
    1. Maintaining a historical view of integrated data in a typical Enterprise Data Warehouse.
    2. Self-Serve Data Management capabilities to empower a large pool of Data Scientists and Data Stewards community, as against an SMB.
  3. For large pharma organizations, a pre-built data management solution may not able to cover the entire spectrum of the business requirements across user personas, and organizations would prefer specific customizing on top of commercially available pre-built solutions.

Given below, we have highlighted some situations in which the practical approach for SMBs and large companies differ significantly and demands different solutions.

Key Drivers
Small Pharma
Mid/Large Pharma
Business Unit
  • Smaller business units
  • Limited business functional units such as finance, sales, brand, and reimbursement
  • Business units expanded all across therapeutic classes, geographies, brands, and business functions
Technology
  • Leaner technology footprint with must to have IT infrastructure
  • Limited IT team to assist IT setup or data management build/operations
  • Limited knowledge of IT/technology best practices
  • Significant IT infra setup to support high data management solution
  • Larger and dedicated IT experts
  • Standard setup processes to execute data management projects
Business Process
  • Rely on solution provider for SME expertise for business processed such as alignment, sales crediting, and data processing requirements
  • Inhouse SMEs for consulting on business processes
  • Robust organization competency to guide data management build and operations
Operation Support
  • Limited governance model setup
  • Evolving change management
  •  
  • Minimal tracking mechanism of change requests, IT incidents, and service requests
  • Limited governance model setup
  • Evolving change management
  •  
  • Minimal tracking mechanism of change requests, IT incidents, and service requests

 

Data Governance

Data strategy and governance are integral parts of any data management solution, essential for its successful implementation and operation.

  1. For pre-built solutions, pharma organizations need to assess the robustness and sustainability of the data governance module.
  2. With a custom data management infrastructure and governance setup, the ease of structured and unstructured data access across data consumers such as data scientists, data stewards, business users, and reporting users needs to be evaluated.
  3. Pharma organizations need to validate whether a solution provides central governance processes/ frameworks for data ingestion, storage, registration, and usage, or does the solution mostly rely on a manual setup for governance needs.

Whichever type of a data solution a company may prefer, robust data governance strategy for managing and controlling data will always be an integral component.

Total Cost of Ownership

While considering the total cost of ownership between the two options, it is equally important to chalk out the most essential opportunity costs of business growth.

  1. Set Up Cost:
    1. From the one-time cost of setting up a custom data management solution to recurring cost of operational maintenance, organizations need to budget the additional spend on technical infrastructure, tools, and the consulting/ IT workforce.
    2. Pre-built solutions are more cost-effective when it comes to set up costs.

  2. Change Management Cost:
    1. Organizations still need to consider, estimate, and pre-validate the budget allocation and change request costs with the pre-built solution vendor beforehand.
    2. The change management budget for custom solutions needs to be predetermined to avoid hidden costs or premium fees due to lock-in clauses.

Conclusion

Despite their suitability to varying scenarios, it is beneficial to have multiple types of data management solutions for pharma companies. Both options have pros and cons and meet different expertise requirements. For pre-built solutions, one vendor is responsible for delivering the dream data management solution and making sure that all technical and business puzzle pieces fit together. This enables the move to a new solution on time, within budget, and with very few surprises.

For a custom solution, the IT teams, consultants, and business SMEs play a vital role, as they know the business processes inside and out. Moreover, the business knowledge empowers home-grown solutions, which caters to specific business needs across use cases of organization. It becomes critical to closely monitor the time to implement, and the overall spent, including organizations internal resources in a custom solution scenario.

Axtria provides data management solutions across all engagement spectrums for its clients. Feel free to explore Axtria’s Cloud Platforms for data management capabilities, pre-built and customizable for organizations to reap benefits of industry-leading and proven solutions.

 

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Tags: Information Management, Data Governance, Cloud Data Management, Pharma, Commercial Data Management, Commercial Data Warehouse, Pharma Data Management, Data Strategy

Abhishek Srivastava

Abhishek has 13 years of experience in IT Consulting, Implementations, Operations, Project Management and Customer Engagement in Life Science and Health Care domain. He has extensively worked on Data Warehousing, Data Lake, Master Data Management (MDM), Business Intelligence (BI) and Consulting projects. He holds MBA degree from IIM Lucknow.

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