STRENGTHEN THE INFORMATION CAPITAL BY FASTENING THE DATA QUALITY WITH DEEP LEARNING

Abstract

In the competitive data driven world, Data & Data Quality (DQ) plays a vital role in each and every step for a successful business as data provider and data consumer. As on today, high quality data becomes Great Asset for Great Decision makers, so most of the companies proactively investing to prevent poor
data quality inflows to business, since enterprise systems designed with high data quality ratio in initial days but the data quality level getting tarnished year by year, which challenges the operational efficiency and business functions. In general, data governance team classifies the data fitness for use with data quality algorithms to detect and repair the data errors, which involves high cost and time. To optimize the scenario, we are proposing to integrate the data processing with deep Belief networks (DBN).This proposed approach uses the feature of DBN multi layers to classify the data fitness versus data quality dimensions and auto repair the defects. Classification can be achieved by DBN layer with pre-trained data quality relevant samples and process the big data for all DQ dimensions in single processing irrespective of volume & complex structures.