The need to digitize data
The manufacturing process generates tremendous volumes of data, much of it manually recorded on paper. But paper records take up a lot of physical space, are vulnerable to loss or damage, and are not well-suited for most use cases. They are difficult to share, hard to organize, cumbersome to search – and nearly impossible to analyze in a timely fashion. As a result, most biologics companies have made it a priority to digitize their records.
Not all digital forms are created equal, however, so it is important to think about the end goals before embarking on this process. For example, simply scanning paper records is one way of digitizing them, creating a more durable backup, and – with the right content management system – making it easier to organize and share electronic representations of the original records. But scanning records into JPG or PDF images does not make it any easier to search or analyze them; it still requires a human to view each record and extract the information needed.
Not all digital forms are created equal, however, so it is important to think about the end goals before embarking on this process. For example, simply scanning paper records is one way of digitizing them, creating a more durable backup, and – with the right content management system – making it easier to organize and share electronic representations of the original records. But scanning records into JPG or PDF images does not make it any easier to search or analyze them; it still requires a human to view each record and extract the information needed.
The limitations of spreadsheets
Another method of digitizing records is to manually transcribe the data into an Excel spreadsheet, structuring it for potential future analysis. This process is laborious, time-consuming, and error-prone. Spreadsheets also do not scale well, quickly becoming cumbersome as the data set grows. Additionally, maintaining an audit trail of changes or corrections is difficult, and controlling who has what version of the spreadsheet is challenging, leading to potential discrepancies.
More importantly, while spreadsheets are useful for capturing and analyzing datasets with a well-defined, two-dimensional structure – such as a single table of data with rows and columns, or a set of records that each have the same name/value form fields – manufacturing records are generally much “messier” than this. They contain many different types of data with a variety of structures and relationships that cannot be captured in a single spreadsheet. This means that important information and data relationships may be lost when trying to transcribe the data. Companies may find that after creating a spreadsheet for a specific type of analysis, they must then recreate or add to it (by manually going through the same records again) in order to answer a slightly different question. Moreover, if the analysis requires additional software, the data must often be manually exported from Excel, creating a separate copy of the data that is not linked to the original – again, making version control difficult.
More importantly, while spreadsheets are useful for capturing and analyzing datasets with a well-defined, two-dimensional structure – such as a single table of data with rows and columns, or a set of records that each have the same name/value form fields – manufacturing records are generally much “messier” than this. They contain many different types of data with a variety of structures and relationships that cannot be captured in a single spreadsheet. This means that important information and data relationships may be lost when trying to transcribe the data. Companies may find that after creating a spreadsheet for a specific type of analysis, they must then recreate or add to it (by manually going through the same records again) in order to answer a slightly different question. Moreover, if the analysis requires additional software, the data must often be manually exported from Excel, creating a separate copy of the data that is not linked to the original – again, making version control difficult.
Defining digitization requirements
The limitations of various digital forms of data underscores the importance of understanding the goals and requirements of the digitization process from the outset. Who needs access to the data? How will the data be verified? What is the audit trail? What types of questions can be answered? Does this include complex analysis combining data across different sources? How quickly and how easily can the analysis be done?
Fortunately, biologics manufacturers now have access to data automation solutions that not only enable rapid, accurate, and complete digitization of records, but also extract information from those records in a way that facilitates timely analysis. This can have tremendous business impacts, whether resulting from a better understanding of cell growth factors, real-time visibility into critical production capacities, or faster generation of compliant regulatory data. Thus, setting ambitious but tangible data goals – and investing in the right data solutions to achieve them – can help pharmaceutical companies transform their businesses.
Fortunately, biologics manufacturers now have access to data automation solutions that not only enable rapid, accurate, and complete digitization of records, but also extract information from those records in a way that facilitates timely analysis. This can have tremendous business impacts, whether resulting from a better understanding of cell growth factors, real-time visibility into critical production capacities, or faster generation of compliant regulatory data. Thus, setting ambitious but tangible data goals – and investing in the right data solutions to achieve them – can help pharmaceutical companies transform their businesses.
