Data-laden describes something heavily packed or burdened with information, details, or numerical facts. It signifies an abundance of data, often implying complexity and a potential challenge in processing or understanding the information. The term highlights the sheer volume of information present, focusing on the quantity of data rather than necessarily its quality or structure. It can apply to various contexts, including documents, systems, reports, or even individuals, denoting an extensive amount of information or data points contained within. Being data-laden may require specialized tools or methods for analysis and interpretation.
Data-laden meaning with examples
- The final report was data-laden, containing hundreds of pages of statistics and survey results, requiring a dedicated team just to extract key insights. Understanding the data required some experience. Processing so much information, it was a real challenge, and we really had to work to filter the good information. We're still working on what it all means, but there's something there!
- The company's CRM system was notoriously data-laden, making it difficult for sales representatives to quickly access the most relevant customer information. Searching for the right data was slow going. Its complexity was a hinderance, and its data size was not optimized for fast retrieval. They are working on fixing it now, but things still have room to improve. At least the data is there.
- The scientist’s presentation was data-laden, presenting an overwhelming array of charts, graphs, and raw figures that lost some of the audience. It really did have a lot of information. It needed to be simplified to make it effective. It could have easily been done in a way that would have been more effective for those listening. It was hard to track!
- The legal document was data-laden with complex terminology and supporting evidence, making it nearly impossible for a non-lawyer to understand fully without assistance. It had a lot of jargon as well. It was long, complex, and difficult to read, though. It's the way things are, though. You just need to read and re-read it!
- The new machine learning model generated data-laden outputs, making it difficult to interpret the model's decision-making process without additional tools and visualizations. It required specialist insight and training. Some of this data was used in our training. With this insight we can adapt. This data is critical to our efforts, but is complex.