Digitalization
Definition
Digitalisation describes the process of transferring analogue, manual or paper-based processes, data and workflows into digital systems. The goal is to capture, connect and utilise information in a structured way in order to sustainably improve efficiency, transparency and quality within organisations.
Extended Explanation
Digitalisation goes far beyond simply converting paper documents into digital formats. It includes the introduction of digital systems, automated workflows and connected data structures that help map and optimise processes in a holistic way.
In laboratory and industrial environments, digitalisation affects areas such as data capture, documentation, analysis, quality management, system integration and decision-making processes.
In many companies, material and quality data are still scattered across Excel files, PDFs, local folder structures or isolated systems. This leads to data silos, duplicated work, long search times and limited transparency.
Through digital platforms, cloud technologies and laboratory data integration, information can be used across systems, processes can be standardised and new opportunities such as AI in the laboratory or predictive models can be unlocked.
Challenges Without Digitalisation
Companies in R&D and quality assurance frequently face challenges such as:
- Scattered data sources
- Manual transfer of results
- Lack of traceability
- High documentation effort
- Inefficient approval processes
- Media discontinuities between laboratory and enterprise systems
- Loss of knowledge when employees leave the company
Especially in complex material environments and with growing volumes of data, a structured digital data foundation is increasingly becoming a prerequisite for efficient development and quality processes.
Key Aspects of Digitalisation
Digitalisation not only involves the digital capture of information, but also the structured connection of data, processes and systems. It creates the foundation for making information more accessible across systems and for improving workflows across different departments and functions.
Particularly in laboratory and industrial environments, several aspects play a central role. These include the digital capture of testing, analytical and material data, the integration of laboratory and enterprise systems, and the automation of manual processes. At the same time, transparency and traceability are becoming increasingly important in order to consistently document and track development stages, tests and quality-related information.
Another key factor is the availability of structured data. Only when information is consolidated and consistently accessible across systems does it become possible to enable more efficient collaboration, data-driven decision-making and the use of AI-supported analytics or approaches such as Material Intelligence.
Digitalisation as a Foundation for AI and Material Intelligence
Artificial intelligence and data-driven analytics require structured, connected and high-quality data. Without digitalisation, many data sources remain isolated or can only be used to a limited extent.
Only a centralised data foundation makes it possible to:
- Identify relationships between materials and results
- Track developments more efficiently
- Detect quality deviations earlier
- Use AI-supported analytics effectively
- Preserve knowledge within the organisation over the long term
Relevance for LabV
For LabV, digitalisation forms the foundation for making material data, laboratory processes and quality information usable across systems. By digitalising laboratory workflows, material data and quality-related information, LabV creates a central and structured data foundation that enables AI-supported analytics, automated workflows and more informed decision-making.
Rather than relying on isolated tools, LabV connects data, systems and processes within a continuous digital laboratory environment for R&D and quality assurance.
Practical Example from the Coatings Industry
Many companies in the paints and coatings industry still work with scattered data sources, manual documentation and disconnected systems. Information related to formulations, tests and development stages is often difficult to access or not available across systems. Practical experience shows that digitalising laboratory and development processes can reduce search and coordination effort, accelerate development cycles and help preserve knowledge over the long term. At the same time, it demonstrates that centralised data structures and AI-supported analytics can be implemented in existing laboratory environments in a practical and achievable way.
Further details are described in the whitepaper “From Data Chaos to Efficient Formulation Development” — covering everything from Excel-based starting points and practical implementation through to the resulting improvements in day-to-day development processes.
FAQ
Why is digitalisation important in laboratories and industry?
It reduces manual work, minimises errors, improves data quality and creates transparency across processes and results — an essential prerequisite for efficiency, quality assurance and competitiveness.
What is the difference between digitalisation and automation?
Digitalisation refers to transferring processes and data into digital systems, while automation builds on this by enabling recurring tasks to be carried out automatically.
Which areas are typically digitalised?
Typical areas include data capture, documentation, analysis, quality management, system integration as well as decision-making and approval workflows.
Why is digitalisation a prerequisite for AI?
AI requires structured and connected data. Without a digital data foundation, information often cannot be analysed efficiently or used consistently
Synonyms & Related Terms
Digital transformation, digital process design, process digitalization, digital workflows
Internal Links
Laboratory Informatics, Laboratory Data Integration, Data Synchronization, AI in the Laboratory, Cloud Computing, Material Intelligence