The phrase "messy data" is correct and usable in written English.
It refers to data that is disorganized, incomplete, or difficult to interpret. Example: "The results of our study were inconclusive due to the large amount of messy data we had collected."
However, environmental water-qualimetrics are profoundly dominated by messy data as justified in this work.
Most scientists have messy data, contradictory data, incomplete data, ambiguous data.
MESSY data are a fact of life: so much are unstructured or in non-standard formats.
Removing duplicative or erroneous values will also help prevent messy data issues from recurring in the future.
We give them a messy data set and ask them to organize it into pivot tables and graph it logarithmically.
This paper discusses how messy data may be a hidden failure factor that Business Process Reengineering (BPR) projects typically cannot detect during the planning phase.
Kirshner explains in exhaustive detail how these ambiguities have been resolved so that meaningful results can be extracted from messy data.
The IBM teams, the executives say, underestimated the difficulty of grappling with messy data like faxes and handwritten notes and failed to understand how physicians make decisions.
Having tens, hundreds, or even thousands of volunteers collecting data can increase the statistical power of research, but may also result in a large, messy data set.
My research centers on exploiting structure in big messy data sets to infer missing data, detect patterns, speed up optimization, and promote better decisions.
As a case study, this talk will introduce Generalized Low Rank Models (GLRMs), a class of optimization problems designed to uncover structure in big messy data sets.
