Manual digitization processes are costly in both time and human capital
The mandate for biopharmaceutical companies to digitize their manufacturing data is clear, but the digitization process is extremely resource-intensive for those without a data automation solution. It commonly involves manual data entry into a spreadsheet or database, tying up scientists’ valuable time. To minimize errors in transcription, data is often entered “double blind”, where two different people transcribe each data set and compare results, correcting for discrepancies. This more than doubles the resources required. And if the process is outsourced or delegated to people unfamiliar with the manufacturing process, the likelihood of errors increases – particularly when dealing with the volume of numeric data, technical content, and internal codewords commonly found in biopharma manufacturing records. Each mistake requires further time and effort to assess and correct, a process that quickly becomes overwhelming at scale.
The ROI of data automation
From this perspective alone, a data automation solution that replaces manual, double-blind data entry presents a compelling ROI. Data automation eliminates the resources required for the laborious and error-prone data entry and verification tasks – or frees them to focus on their core, high-value responsibilities. And it greatly reduces the lag time between data generation and digitization, which in turn can accelerate batch releases and regulatory approvals.
Yet this is only the tip of the ROI iceberg for biopharma data automation. Less obvious but more transformative benefits arise when a company can optimize business practices based on timely, accurate, useful access to their manufacturing data – something that is difficult to imagine, let alone achieve, with most manufacturers’ current digitization processes.
Yet this is only the tip of the ROI iceberg for biopharma data automation. Less obvious but more transformative benefits arise when a company can optimize business practices based on timely, accurate, useful access to their manufacturing data – something that is difficult to imagine, let alone achieve, with most manufacturers’ current digitization processes.
Manual digitization results in “trapped” data
Typical data digitization processes, such as manual entry into spreadsheets, result in data that is still “trapped” – meaning it is not very useful, despite being in digital form. It is not easily analyzed, organized, or searched. It is also often incomplete, since biopharma manufacturing data – such as batch records or equipment output – generally doesn’t fit perfectly into a spreadsheet. Thus, in the process of being digitized, fields may be omitted, tables may be flattened, and structural relationships between data may be lost. This means that when analysis of the data is desired, companies often find themselves having to painstakingly re-enter the same set of records into a new spreadsheet format or a software program specifically set up for that type of analysis – and then repeating the process yet again when a different type of analysis is required. The substantial time and resources required make timely analysis practically impossible.
Spreadsheets are also not a very scalable solution, as they quickly become unwieldy when dealing with large data sets. Distribution, access control, and change management are also challenging. Organizations can easily end up with multiple spreadsheets containing slightly different versions of the same data set – none of which is easily traced back to the origin source data, creating a data integrity nightmare.
Using a database rather than a spreadsheet can offer more flexibility if implemented correctly, but this requires significant data modeling and data science expertise and resources – and still relies on laborious, error-prone data entry methods. As a result, many companies relying on manual digitization simply don’t perform analysis on their batch records and other manufacturing data – missing out on the highly useful insights contained within – because the process is simply too lengthy and resource-intensive.
Spreadsheets are also not a very scalable solution, as they quickly become unwieldy when dealing with large data sets. Distribution, access control, and change management are also challenging. Organizations can easily end up with multiple spreadsheets containing slightly different versions of the same data set – none of which is easily traced back to the origin source data, creating a data integrity nightmare.
Using a database rather than a spreadsheet can offer more flexibility if implemented correctly, but this requires significant data modeling and data science expertise and resources – and still relies on laborious, error-prone data entry methods. As a result, many companies relying on manual digitization simply don’t perform analysis on their batch records and other manufacturing data – missing out on the highly useful insights contained within – because the process is simply too lengthy and resource-intensive.
The unexpected business benefits of automated access to data
An effective data automation system solves the problem of “trapped” data. It automates the digitization of all types of manufacturing data, quickly capturing complete information from source records, including structural relationships and context. It then cleans the data, ensuring it is readily usable for all different types of analysis, in a matter of minutes or hours, rather than weeks or months.
Consider the ROI – and the business transformation – that becomes possible if each of the following were not only possible, but fast and easy:
Consider the ROI – and the business transformation – that becomes possible if each of the following were not only possible, but fast and easy:
- Understanding how different process control parameters and environment variables affect yields – and being able to trend parameters in real time to ensure the process is in control and intervene proactively when needed
- Evaluating the impact of process changes, new technologies, or different raw material providers on manufacturing outcomes and cost
- Developing an accurate accounting of the cost of materials, energy, equipment, and human capital at each stage of the manufacturing process
- Identifying all batch records relating to a specific room, operator or piece of equipment to increase the speed of investigations and minimize time required for corrective actions
- Implementing truly automated Continued Process Verification to improve regulatory compliance and quality assurance
The above capabilities are just the beginning of what data automation can help biopharma manufacturers achieve. With AI and advanced analytics, a company’s manufacturing data becomes a goldmine of information essential to its competitive advantage, and data automation unlocks that goldmine – that is its true ROI.
