How good is your pharmaceutical data management?
A consideration of the causes and consequences of poor practice.
Every lab has data management systems in place, particularly if they work in the pharmaceutical industry, but the demands on those systems are growing. Perhaps a better question to ask is how good is your data management when it comes to the multi-stakeholder projects that now prevail? Can you track back who did what, when, and the results, for every contributor? And can everyone efficiently access the information they need, without a lengthy search?
Via a couple of common scenarios, we look at some examples of sub-optimal data management practice and the very real impact they can have on project timelines and budgets.
The simple spreadsheet
Personal or shared Excel spreadsheets enjoy widespread use throughout research. Small teams routinely use file-based mechanisms for managing critical reagents and entities putting in place standard protocols to ensure a uniform approach. Common applications include freezer or reagent management and as a “database” of results such as IC50s and Hematology. However, record retention and change audit can be challenging. It can be difficult to determine whether a change made to a historical data point was intentional and if so, who made it; in the absence of an audit trail identifying the source and/or reason for change can be almost impossible.
The classic paper/Excel combination that many labs still rely on exacerbates such issues since it translates into no complete, centralized overview. No central information repository to search to gain a full understanding of what has been done and the results generated. This presents far more than a daily irritation, particularly when working with partners, for example, when using three or four contract research organizations (CROs) to work on different molecules for a specific project. In this scenario, spreadsheets can become part of an information flow that is impossible to robustly manage, audit and interrogate, undermining project efficiency, slowing progress, and detrimentally impacting the bottom line.
The essential email
When it comes to CRO management, issues also arise from the use of emails for workflow management. Outbound requests to synthesize or produce compounds, guide sequences, proteins, or cell lines, are initiated via emails sent with multiple attachments. In return the CRO sends a data package and the physical materials, once the development work is complete. On receipt this data is quality checked and imported into internal systems.
Using email in this way, as a prioritization or per-experiment communication medium detaches critical data from recordkeeping systems and can leave important contextual information uncaptured. Furthermore, it results in a linear approach that is slow, inefficient, and, ultimately, an impediment to progress. An email-driven approach means investing time and money in data import and export; that quality checks are done after the completion of the project, rather than in real-time; and that data is received in bulk when all of the work is completed rather than on an item-by-item basis. These issues hamper collaboration limiting it to troubleshooting over email and video/conference calls with teams who, given the global nature of the industry, may be working on a 12-hour difference.
Switching to centralized laboratory data management
A centralized, cloud-based, data management solution, with an appropriate permission model, is the modern answer to these and many other examples of poor data management practice. These solutions provide a single view or interface for all research information from multiple contributors, to improve collaboration, productivity and decision-making. Extending from conception to filing, from sample identification, location, and container management to experiment and results management these auditable, electronic informatics hubs allow researchers and project managers to “follow the data”, from the first experiment, to understand how and why decisions were made. No data are lost, and the context of the data is maintained.
Find out more about how a centralized solution could de-glitch you data management, release the full productivity of your team and help to boost the return on every research dollar spent.