Part 4: Data Migration in Pharma

In a series of four, Senior Project Manager and 7N IT specialist, Lars Søndergaard, focuses attention on what challenges and possibilities he sees with data migration in the pharma industry. Besides management,

Lars possesses data warehouse and business intelligence as core competencies and has more than 20 years of experience within his field. He has gained a lot of his experience within the pharma industry, the latest from an assignment at LEO Pharma as a Project Manager and Data Governance Consultant.

Driving a hard bargain

Suppose the data migration effort is outsourced to an external Vendor. In that case, it is critical to understand how big a task you are outsourcing.

Before signing a contract, the size of the product backlog should be understood and, if possible, mentioned in the agreement. E.g., the number of data objects grouped by object types (Key, Join) and complexity.

Evidence of the costs for one data migration can be used to budget the next data migration and to have a fact-based discussion of costs with a data migration vendor.

Most of the things described in this article are focusing on risk mitigations related to data migrations. However, even though many suggestions are implemented, the probability of increased cost is still very real. Many things cannot be controlled.

It could be considered to make a contract where some commercial risks are shared with the vendor.

In that case, it would be my advice to be fully transparent with the vendor on the following:

• The data migration scope
  
  
• Uncertainties and risks
  
  
• Known data quality issues
  
  
• Etc.

And then explore with the vendor if a fixed-price contract is possible.
There is a risk that the vendor will add a significant amount of costs on top. Hence, a realistic understanding of the costs is a prerequisite for pursuing a fixed-price contract.

Conclusions

1. Explore the opportunity

• Have a strong focus on data quality, not only short term when migrating but also long term, by preventing bad data from being entered, e.g., by smart configurations and taking advantage of reporting capabilities to monitor data quality.

2. Work agile

• Deliver the data migration in sprints, data object by data object, and respect that data is a shared resource across user stories and features.

3. Collaborate

• Ensure that close collaboration between migration, LoB SME's, and Veeva configuration leads is embedded directly in the Agile framework.

4. Do the planning

• Respect the complexity of the data migration and create a data migration strategy describing the options and rationale for decisions and make sure that key stakeholders agree and sign off.
• Make sure that the overall sprint planning is done as co-planning with all stakeholders

5. Don't outsource something that you don't understand

• Work diligently with the business to understand the target data scope before outsourcing to a data migration vendor.
• If a fixed price contract is pursued, have a realistic view of the costs.

6. Collect evidence

• This will not be the last data migration. Collect evidence of costs, impediments, etc., for the next data migration.