Introduction Research labs:
Research labs assort and process a wide range of data that becomes central to research and innovation. But poor data management is the pitfall to lab efficiency. Using traditional data management systems is one of the biggest obstructions for research teams in reaching their maximum output potential.
Limitations of Traditional Data Collection and Management
Putting this data up in the spreadsheets only serves the purpose until the end of the experiment. However, it is not an efficient way to manage data for future uses. This results in low productivity, high R&D costs, and sometimes even loss of important data.
Cloud technology allows sharing files and data using a secure off-site server or ‘cloud’ storage. It enables easy access to the team members from anywhere and anytime.
Advantages of Using Cloud Technology for Data Storage and Management:
- It unifies data management features and AI algorithms to provide insights and recommendations.
- Allows team members to work on projects together efficiently with off-premise file storage that provides flexible access to the data.
- Cloud templates eliminate manual data modification for analysis and save tons of time.
- Paired with AI/ML it breaks the traditional long cycles of trial and error-based development and eases Lab automation.
- It saves the cost of running onsite server rooms for data storage to reduce your budget.
- It saves physical space, energy, and additional data management personnel.
- It will be working in the same format of data input, and minimizes the chances of human error in manual lab techniques, further minimizing the company’s R&D budget.
Digitalization in Research Labs
An efficient cloud-data management system makes these processes easier, and with analytical abilities, it can predict the experiments for desirable outcome requirements.
Together with Machine Learning and Artificial Intelligence, cloud computing can enable research labs to optimize productivity and save valuable money on operational costs.