More often than not estimates, analytics, data-driven predictions seem confusing and overwhelming. But now the situation demands that benefits of data interpretation is vital. Statements such as data too difficult to access, understand or use are common and so are ignored while making business decisions. Fear of failure affects productivity and trying out new ideas. To dispel fear of data usage, managers need to promote better work ethic; data interpretation must start at the basic level with simple tools and incorporate the habit. To incorporate the total picture in a business decision, every perspective regarding data must be addressed.
To know more: https://hbr.org/2015/07/dispel-your-teams-fear-of-data

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:

http://www.informationweek.com/big-data/big-data-analytics/6-causes-of-big-data-discrepancies/d/d-id/1320692