Combating Dirty Data using Data Virtualization

Abstract

Data is the new Gold of the modern age. Organizational decisions, artificial intelligence, machine learning, time critical processes, analytics, etc. are driven by highly profiled data. Data gives an organization the ability to act in a corresponding situation and hence, keep up with the market. Financial returns are directly associated with the quality of data being used. Today’s world has experienced a data explosion with the advent of data sources like Hadoop, data lakes, clusters and many more. Even more, the day to day devices that we use collect data to present meaningful statistics to us. There is an exponential increase in data volumes which are characterized by heterogeneous data formats and disparate data sources which in turn is complicating the task of providing uniform and profiled data to the business users for analysis. Warehousing is also not able to cope up with the on-demand real-time data access. Holistic view of the entire data is extremely difficult to achieve and takes a considerable amount of time which is slowing down an organization’s decision making capabilities. The complexity of the current systems has increased due to the interconnected nature of widely distributed systems which share data. Dirty data i.e. data which is inaccurate, inconsistent, unclean or incomplete is spread all across. As all the data is unrequired garbage data, there is no point in developing your business intelligence over this data. Data Clarity used along with Data Virtualization provides an exceptional solution to these problems by handling the complex data workloads with respect to heterogeneous data and disparate data sources and provides a holistic view of the data to the business organization.