What is automated data processing?

Modern companies in regulated industries, like manufacturing and pharmaceuticals, need to make data-driven decisions quickly and securely. To gain a competitive edge, enterprises need efficient systems to store and process data, particularly as they adopt AI. These systems not only handle the vast influx of information but also make sure that it stays accessible for analysis to inform timely decisions.

But before they can make the leap to modern digital environments, companies need to first build the foundations for process and data automation.

Automation, here, refers to handling data tasks without human intervention. It involves using technology or software to streamline all internal tasks and workflows in capturing, handling, and presenting data in compliance with standard.

Automated data processing, therefore, simply describes the use of this technology to automatically manage and manipulate this data. This process typically includes:

1. Data collection Automatically gathering data from different sources like sensors, user inputs, or lab instruments.
2. Transformation Modifying the data's format or structure.
3. Cleaning Removing errors, duplicates or irrelevant information.
4. Data processing Organising data into a structured format by sorting and categorising it.
5. Analysis Using statistical tools and algorithms to analyse the data and pull-out insights around desired data points.
6. Visualisation Visual representations of data using charts, graphs, and maps.
7. Presentation Showing data findings in formats like dashboards or widgets.

In essence, the process of data collection on a manufacturing line to a dashboard on a screen, from A to Z, can all be streamlined. Raw imputed data can be processed until it transforms into printed and approved conclusions.

Automated systems can analyse product data from assembly lines in real time to find defects or deviations from quality standards, making sure that organisations are compliant with ISO standards.

Automated processing of large-scale biological data can help pinpoint potential drug candidates faster by simulating and then analysing chemical interactions at a scale that just isn't feasible manually.

Another use case is making sure that all processes and documentation comply with regulatory requirements. This includes data integrity & traceability from drug development across the product life cycle, all the way to market.

Data from clinical trials can be used to track patient recruitment, monitor results, and ensure data integrity and compliance with regulatory standards like the FDA guidelines.

The automated collection and analysis of data related to drug safety enable companies to rapidly identify and report adverse drug reactions, ensuring patient safety and compliance with health authority regulations.

Automated data processing can have a huge impact on operations and compliance. Recent examples, like Sanofi's roll-out of its AI-powered application plai, illustrate the extremes of what is possible when companies really invest in upgrading their digital operations. In its R&D processes, Sanofi was able to "improve potential target identification in therapeutic areas like immunology, oncology or neurology by 20 to 30%." Whilst in manufacturing, they were able to predict 80% of low inventory positions enabling teams to take mitigating actions to secure supply much faster.

Similarly, Merck introduced modular automation in their laboratories, which was a first for the chemical industry. The enterprise was able to accelerate the launch of new products by up to 50%.

More generally speaking, there are some common benefits that automated data processing can bring to regulate industries:

• Improved compliance & traceability

• Increased efficiency & productivity

• Better data integrity

• Cost reduction

Precision and regulatory compliance are crucial in industries like pharmaceuticals & biotechnology. Software solutions can streamline the complex data workflows from initial inputs all the way to analysis & reporting.
Here are some common solutions that automat data processing to improve operational efficiency & compliance:

1. Quality Management Systems (QMS or eQMS)
2. Electronic lab notebook (ELN)
3. Electronic Batch Records (EBRs) for manufacturing

For deeper dives on these different solutions, feel free to click on the links above where we've explored these in more detail.

1. Data for Quality management systems

Quality management systems form the backbone of a company's internal operations, ensuring consistent quality, meeting regulatory requirements and driving continuous improvement aligned with international standards such as ISO 9001.

QMS systems make use of documents like Standard Operating Procedures (SOPs), Corrective and preventative actions (CAPAs), to make sure companies are complying with external audit requirements. The data within these documents is usually in textual (qualitative) format, with standard procedures or corrective actions taken being described in great detail, and signatures placed where appropriate. Traditionally, tracking changes in these documents and storing them using paper or spreadsheets is tedious and error-prone.

Instead, automated systems handles this process by automatically extracting of pre-existing documentation into a single database. This makes sure that documents are stored quickly and securely accessed by authorised personnel at an appropriate time.

Handling data with an eQMS ensures an error-free and easy way for companies to be compliant and prepare for audits. For example, when a new SOP is approved, the eQMS would automatically update the central database and notify relevant personnel so that all team members have access to the latest procedure.

2. Data in Electronic lab notebooks

Electronic lab notebooks have been a game-changer for R&D teams. They improve how data is recorded and managed in a lab, making work traceable and easy to share & review.
ELNs simplify how experimental data is handled. Lab data usually includes measurements, observations, and various file attachments. These need to be easily accessible for lookups and data migration.

Being able to flexibly create databases, manage documents, and create visualisations has is made much easier. Manual tasks like data entry, calculations and report generations can also be fully automated, ensuring maximal workflow control and efficiency. ELNs can also be used for data modelling for quick data analysis. An example of automated data processing with an ELN, might be as a researcher records temperature data. The system would automatically capture readings from a connected thermometer.

3. Data in manufacturing

For manufacturing, managing data is crucial for tracking the complete lifecycle of a product, from sourcing of materials to production, approval and packaging. This data is typically managed through Batch Manufacturing Records (BMRs).

BMRs are detailed records that document every stage of the manufacturing process. These usually exist as templates that technicians fill out and managers approve. They contain a mix of visual data like barcodes, numerical data like batch ID or LOT numbers.

To speed up this process, electronic bath batch records can be used to automate data management tasks. By gathering all the data into a single, controlled database, EBRs automate tasks like data capture and validation. For example, as tablets pass along a production line, sensors automatically measure their weight and thickness, comparing that data to quality standards to make sure each tablet meets specifications.

EBRs provide users with a way to quickly identify bottlenecks and ensure a risk-free product development in real-time.