Describing something characterized by or containing a large volume, complex structure, or high density of data. It often implies that significant processing, analysis, or interpretation is required to extract meaningful insights. Data-heavy situations are often resource-intensive, demanding substantial computational power, storage capacity, and specialized expertise. This term can apply to a variety of fields, including scientific research, business analytics, and information technology.
Data-heavy meaning with examples
- The new AI algorithm, utilizing a Data-heavy dataset of medical images and patient records, can now diagnose subtle anomalies with unprecedented accuracy. This has the potential to drastically improve early detection rates and treatment effectiveness. The challenge lies in processing this massive amount of information quickly.
- Financial institutions often deal with Data-heavy transactions involving millions of customers and complex market dynamics. They need sophisticated systems to manage risk, detect fraud, and comply with regulations. Accurate real-time analysis is key to making profitable investment decisions.
- Scientific research in fields like genomics and climate modeling often relies on Data-heavy simulations and observations. These models use huge data sets, from sequencing genomes to recording global weather patterns. This often requires powerful supercomputers for crunching data.
- Implementing a new CRM system quickly turned into a Data-heavy project. Migrating customer information and integrating multiple databases proved challenging and needed more specialist support than anticipated. The volume of data proved to be a significant hurdle.