How to Manage Your Data Effectively as a Researcher
Here's how researchers can manage their experiment and research data effectively with data management best practices
Data is the foundational intelligence for important business decisions. Erroneous decisions made from poor quality data are more than just inconvenient – they are costly.
According to research by Gartner poor data quality costs organizations more than $15 million per year. Data management in the chemical industry is even more complex because research data requires meticulous classification, precise labeling, and a collaborative workspace to foster innovation. Using traditional practices of research and experiment data management hinders progress and innovation because of isolated and inflexible software systems.
To identify opportunities in innovation, efficient experiment data management and thorough organization are crucial. Unfiltered data that is poorly stored cannot be used for analysis or making business decisions. According to Anaconda’s 2020 State of Data Science, as much as 45% of a data scientist's time is spent on preparatory tasks including loading and cleaning data.
Yet, as researchers, you don’t always have time to carve out for organising and managing your data. Here are some ways to improve data management that will help you manage your data more optimally:
Protocols for Research Documentation of Research Data
To ensure that the data remains consistent across different researchers, companies need to have a standard format for data storage. A pre-defined template ensures impartial analysis and efficient research reproducibility. It reduces the time spent structuring documents; templates provide blanks for the researcher to fill in quickly.
Data labeling using unique IDs and metadata also allows for easier discoverability in the future. While data storage and documentation protocols take time and commitment to establish, doing this early on helps save precious time that could be used for groundbreaking discoveries.
Investing in Unified Platform for Data Management in Research
Siloed data storage not only limits knowledge transfer but also hinders progress and innovation within an organization. There are two common practices of siloed data storage that need to be changed because they prevent data optimization:
- Non-centralized Data Sources: Organizations have scattered sources for data storage. Some data is stored on the cloud, on-premise servers, computers, and even personal hard drives. That means researchers have to spend time and effort to identify the source of data.
- Siloed Software Solutions: A common misconception concerning data storage is that storing it on the cloud would improve data usage and accessibility. However, siloed software solutions that independently store data slow down the knowledge transfer and data integration because they do little apart from labeling and storing data in an organized manner. To welcome groundbreaking ideas, a platform that allows collaboration is a crucial parameter for an efficient experiment data management solution.
Hence, a unified platform would solve the aforementioned problems. By unifying data sources and eliminating siloes, research teams are now empowered: they are knowledgeable of where data comes from and how they relate to one another.
Efficient data management practices are essential to accelerate R&D efforts and enhance research productivity. To enable this, organizations need to establish a standard system of consistent data storage and have a unified workspace to foster collaboration.
Through its suite of interconnected features Polymerize combines both these aspects in a seamless data management platform that is designed to improve data storage, enhance productivity, and encourage collaboration.
