Why are the original/raw data not readily usable by analytics tasks? What are the main data preprocessing steps? List and explain their importance in analytics.

Raw or original data are usually not readily usable largely because they are usually untidy. These include the data format, unnecessary inclusions in data such as signs and symbols and so on.

Data preprocessing involves the series of operations performed on data before using the to run analytics.

They include :

Data cleaning : This involves the removal of missing data points or filling them with appropriate values,

Data integration : when using data from different sources, this data would have to be combined or merged appropriately.

Data transformation : Data may need to be transformed in other make it usable for its intended purpose. Transformation techniques include standardized or normalized, one hot encoding and so on.

Data Reduction : this include principal componnent analysis and other attribute selection processes.

Analytics has proven to be an important tool in various sectors and industries such as business, technology with its ability to make model based prediction enabling business to move further ahead in production and Decison making, hence driving revenue and growth