Why do we calculate Information value? I have the data with categorical variables and continuous variables, but is the need for finding information value in explanatory data analysis.
Just give the reason for why we are calculating the information value for each variables at the beginning of the data analysis and what will be the cutoff point of INFORMATION VALUE for taking in care of the analysis   
 A: Generally speaking, Information Value provides a measure of how well a variable $X$ is able to distinguish between a binary response (e.g. "good" versus "bad") in some target variable $Y$. The idea is if a variable $X$ has a low Information Value, it may not do a sufficient job of classifying the target variable, and hence is removed as an explanatory variable.
To see how this works, let $X$ be grouped into $n$ bins. Each $x \in X$ corresponds to a $y \in Y$ that may take one of two values, say 0 or 1. Then for bins $X_i$, $1 \leq i \leq n$,
$$
IV= \sum_{i=1}^n (g_i-b_i)*\ln(g_i/b_i)
$$
where
$b_i= (\#$ of $0$'s in $X_i)/(\#$ of $0$'s in $X) =$ the proportion of $0$'s in bin $i$ versus all bins 
$g_i= (\#$ of $1$'s in $X_i)/(\#$ of $1$'s in $X) =$ the proportion of $1$'s in bin $i$ versus all bins 
$\ln(g_i/b_i)$ is also known as the Weight of Evidence (for bin $X_i$). Cutoff values may vary and the selection is subjective. I often use $IV < 0.3$ (as does [1] below).
In the context of credit scoring, these two resources should help: 
[1] http://www.mwsug.org/proceedings/2013/AA/MWSUG-2013-AA14.pdf
[2] http://support.sas.com/resources/papers/proceedings12/141-2012.pdf
