Big Data is the Buzzword of 21st century as we know it and has been extremely useful in several risk assessment tasks. The effectiveness of Big data on risk management depends on accuracy,consistency ,completeness and timeliness of data. Some most common mistakes made by Big Data experts who are involved in risk management are : Confirmation Bias : It occurs when data scientists use limited data to prove their hypothesis.

Selection Bias : When data is selected subjectively, Analyst comes up with the questions and thus almost picking the data that is going to be received ( Ex : Surveys) 

Outliers : Outliers are often interpreted as normal data

Simpson’s Paradox : When group of data points to one trend, but can reverse when they are combined

Confounding Variables are overlooked

Analyst assume bell curve

Overfitting and Underfitting models

Read more at : http://dataconomy.com/2017/01/7-mistakes-big-data-analysis/

With more than three million Americans being affected with Hepatitis C Virus (HCV) and the virus posing serious challenges to reducing the costs of disease management and control, recent research from University of Michigan is promising some hope. By making the use basic EHR data, predictive analytics algorithms can now flag the affected patients who are at high risk of developing complications from the virus. Although one-third of the HCV patients are at risk, all the patients are treated which lead to huge unnecessary healthcare spending. This is where the predictive analytics model is expected to be of great use. Further integration of the model with EHR will enable the providers to deliver more targeted treatment and a chronic disease management plan can chalked out. Read more :- http://healthitanalytics.com/news/predictive-analytics-identify-high-risk-hepatitis-c-patients