In case of data with high intra group variation, researcher may find that on plotting such data on a graph a definite pattern or trend may not be produced, computerized program such as CLOUD program can be used in such cases to edit the data and adjust each outlying point separately, thereafter TIN model can be applied to this data set for aligning the data and attaining the trend line.
More and more technologies are being conglomerated to analyze large scale data, technologies in vogue today for managing big data include A/B testing, crowdsourcing, data fusion, genetic algorithms, simulation, large scale parallel processing, data mining, tensor based applications, cloud technology and more.
Another type of difficult data is incomplete data. Incompleteness in data may arise during the data collection process, non-response, faulty equipment etc. Techniques for working with incomplete data include partial and full imputation, judicious deletion, maximum likelihood or maximum expected models and interpolation.
There are several advantages of using difficult data technologies besides time efficiency, these are:
- Data loss is minimized
- Each data entry is addressed, assessed and if required adjusted individually.
- Error is minimized and accuracy is high
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