Skip to main content

Data Quality & its problems

Data quality is a measure of the condition of data based on the following factors

  • Accuracy
  • Uniqueness
  • Consistency
  • Reliability
  • Availability
  • Usability
  • Sufficiency

The data is generally considered high quality if it is fit for its intended uses for decision making and strategy planning.

There can be several type of quality problems in data collection.

The most common problems are

Noise and Outliers

The noise is a kind of distortion on the actual data. The outliers are considerably different than most of the other data available in the data set.

Missing Values

The missing values are incomplete data in the data set.

Duplicate

The duplicate data are repetitive data in the data set.

Labels:

Popular posts from this blog

Gaussian Elimination - Row reduction Algorithm

 Gaussian elimination is a method for solving matrix equations of the form, Ax=b.  This method is also known as the row reduction algorithm. Back  Substitution Solving the last equation for the variable and then work backward into the first equation to solve it.  The fundamental idea is to add multiples of one equation to the others in order to eliminate a variable and to continue this process until only one variable is left. Pivot row The row that is used to perform elimination of a variable from other rows is called the pivot row. Example: Solving a linear equation The augmented matrix for the above equation shall be The equation shall be solved using back substitution. The eliminating the first variable (x1) in the first row (Pivot row) by carrying out the row operation. As the second row become zero, the row will be shifted to bottom by carrying out partial pivoting. Now, the second variable (x2)  shall be eliminated by carrying out the row operation again. ...
Read more

Decision Tree - Gini Index

The Gini index is used in CART. The Gini index measures the impurity of the data set, where p i - probability that data in the data set, D belong to class, C i  and pi = |C i,D |/|D| There are 2 v - 2 possible ways to form two partitions of the data set, D based on a binary split on a attribute. Each of the possible binary splits of the attribute is considered. The subset that gives the minimum Gini index is selected as the splitting subset for discrete valued attribute. The degree of Gini index varies between 0 and 1. The value 0 denotes that all elements belong to a certain class or if there exists only one class, and the value 1 denotes that the elements are randomly distributed across various classes. A Gini Index of 0.5 denotes equally distributed elements into some classes. The Gini index is biased toward multivalued attributes and has difficulty when the number of classes is large.
Read more

Data Cleaning Process

Data cleaning attempt to fill in missing values, smooth out noise while identifying  outliers, and correct inconsistencies in the data. Data cleaning is performed as an iterative two-step process consisting of discrepancy detection and data transformation. The missing values of the attribute can be addressed by Ignoring the value filling the value manually Using global constant to fill the value Using a measure of central tendency (mean or median) of value Using attribute mean or median belonging to same class Using the most probable value Noise is a random error or variance in a measured variable. The noisy data can be smoothened using following techniques. Binning methods smooth a sorted data value by consulting the nearby values around it. smoothing by bin means - each  value in a bin is replaced by the mean value of the bin. smoothing by bin medians - each bin value  is replaced by the bin median smoothing by bin boundaries - the minimum and maximum values in a ...
Read more