Big Data is growing bigger and bigger every day.
But, as it is growing, it is also becoming more complex. And complexities usually lead to discrepancies in interpretation of same information. As it is said that, “Prevention is better than cure.” Similarly, identifying these discrepancies and the reasons behind them at an early stage is better than allowing them to become a bigger problem.
Lisa Morgan, Freelance Writer, in her presentation at Information Week, has pointed out six major causes behind big data discrepancies. They are:
- Same Data, Different Quality
- Data-Cleansing Issues
- Problems with the Algorithm
- Models Differ
- Model Complexity Differs
- Interpretations Differ
To understand them in detail, please visit the following link: