Transforming Research Data Management for Greater Innovation

Discovery depends on data. It’s what fuels research, tests our ideas, and drives breakthroughs in science and engineering. One well-crafted dataset can unlock a new drug, reveal hidden climate patterns, or expose insights into human behavior that reshape public policy. Data can be highly sensitive or openly accessible, timeless or ephemeral, irreproducible or disposable, or structured or chaotic.

Research institutions face both opportunity and complexity when it comes to harnessing data effectively. Failure to properly manage it can lead to stalled progress, wasted resources, and limited collaboration.

Data only becomes valuable when used, and when reused, it can potentially become even more valuable. Institutions that want to maximize their research investments need a strategic management approach that balances preservation, accessibility, and security and satisfies stakeholders’ needs at the same time.

The Data Deluge

Managing, transferring and wrangling multiple copies and versions of enormous datasets is resource-intensive and costly. Many data archives lack efficient mechanisms to distinguish duplicates and original files, track active versus abandoned datasets, manage version histories, or automate retirement.

Furthermore, researchers often lack the training, time, and motivation to develop and maintain disciplined data storage practices, creating difficulties for data managers down the line. Providing researchers with transparent, intuitive tools and workflows enables seamless integration of best practices into their existing processes with minimal effort, thereby making the entire curatorial process more efficient.

As research data grows exponentially in volume, variety, and velocity, traditional management practices that are heavily dependent on ad hoc, dispersed individual and departmental efforts are failing significantly. Data becomes buried in nested folders with cryptic naming conventions. Storage administrators constantly create space while having no visibility into what they’re deleting or its importance. Data scientists spend up to 80% of their time wrestling with data rather than conducting actual research.

The “just keep everything” approach that worked with gigabytes becomes financially and operationally unsustainable at petabyte scale. Yet the alternative of deciding what to delete feels like gambling with potentially groundbreaking discoveries.

Managing research data extends far beyond simple storage provisioning. Institutions must invest in curation, migration, and infrastructure while addressing governance, compliance, and resilience requirements. Costs can easily mount due to data misuse, misinterpretation, and legal exposure when releasing data, thereby discouraging data sharing.