Big Pharma = Big Data: Challenges and Solutions

Large pharmaceutical companies face numerous data management obstacles. This is, in part, due to the large scope of data that requires proper data entry, storage, access, curation, and reporting. Laboratory Information Management Systems (LIMS) are a common and appropriate solution. However, many Big Pharma companies (and other large life science entities) solve the data management challenges with a series of non-connected software solutions that differ by departments. The result is a confusing network of data that is not necessarily interlinked to allow robust record keeping and data mining. If your organization faces these challenges, keep reading for helpful solutions.

The scope of Big Pharma data management is huge

Data related to trial participants, clinical patients, clinical visits, sample/specimen collection kits, samples, are only a portion of the data requiring management. Consider also:

  •  Study Management: registering Patients, tracking consent, scheduling visits and tests, and recording results
  • Biobank: preparing collection kits, accessioning samples, linking samples to patients/studies/results, protecting PII and PHI data, and processing requests
  • Shipments: managing incoming and outgoing shipments
  • Derivatives: managing aliquoting, pooling, DNA/RNA extractions, and other derivative types in a way that links the derivative to relevant upstream data
  • Testing: test results, quality results, and reporting to physicians or regulators

LIMS is the solution, but…

LIMS solutions have been available in since the 1980s. As computing powers increase, so does their sophistication, incorporating relational databases, client/server architecture for data exchange, web-access, several modern functions like electronic laboratory notebooks (ELN), and integration with instruments and other software, among others. [1]

…multiple, disconnected LIMS across departments are problematic

Unfortunately, many Big Pharma entities now have multiple software solutions that are not linked and differ among departments. The causes can be simple – new functionality needs arise over time, new managers bring their own favorite LIMS, and different departments have different data management needs. However, a single solution is preferable to truly manage and connect the large scope of data within an organization. For many, this means a custom solution.

Finding a custom LIMS provider and avoiding common pitfalls

  • It takes time and effort from your team to pull together requirements for your custom solution, so make sure you look for a LIMS provider with an expert team to facilitate this.
  • Your needs may change slightly over the course of the project, so you will want to choose an experienced LIMS provider that knows how to manage the scope of the project to allow for flexibility, while protecting an on-time delivery.
  • Most custom LIMS projects never go live, so choose a provider with a proven track record for completing custom LIMS solutions that go live.

Conclusion

Managing big pharma data is challenging. Your organization will benefit by finding a provider who is willing to work with you and discuss options for finding a solution that works for your needs, timeline, and budget.
 
 
[1] Gibbon, G.A. (1996). “A brief history of LIMS” (PDF). Laboratory Automation and Information Management32 (1): 1–5. doi:10.1016/1381-141X(95)00024-K. Retrieved 7 November 2012.