Data Quality and Data Governance

ETL, Data quality and governance

ETL, Data quality and data governance are indispensable to organizations who aspire to become Data Intelligent Enterprises. The triple combination of ETL, Extract, Transform and Load provides crucial functions that are many times combined into a single application or suite of tools.

Requirement for Data Assesment

The quality of data defines the precision, completeness and integrity of the data. Organizations require high quality data that they can trust to make critical decisions.Data governance is a series of procedures and processes for standardizing and automating the use and usage of data within an organization; a structure of governance and regulation over the use of data properties. Data governance offers a basis for communication in a common language; Teammates in and across divisions may collaborate using the same language and evaluate the same results. In addition, clarifying functions and responsibilities reduces uncertainty and makes data systems and communication easier to adopt.

Leveraging Data Governance Framework

As a practice, data governance requires the creation of comprehensive, formal oversight over certain procedures and obligations. This will allow organizations to stay responsive, particularly as they expand to a scale at which it is no longer efficient for individuals to execute cross-functional tasks. Several of the overarching advantages of data management will only be achieved when the organization has developed formal data governance. Many of these advantages include the following:

Data accuracy

Data accuracy

It is the fundamental dimension in data quality. Data may come from various internal and external sources, and therefore data accuracy refers to the correctness and consistency of data.

Data completeness

Data completeness

It indicates that the required data are well recorded without missing values. A complete data source covers adequate data in both depth and breadth to meet the defined business information demand.

Data integrity

Data integrity

It means the wholeness, entirety and soundness of organizational data. It often involves a data integration process which combines data from different sources in an attempt to provide users with a unified view of these data.

Getting started

ETL andData governance can better be viewed as a mechanism that facilitates the overall data management policy of the enterprise.ETL is a type of data integration process referring to three distinct but interrelated steps (Extract, Transform and Load) and is used to synthesize data from multiple sources many times to build a Data Warehouse, Data Hub, or Data Lake.The Data Governance System gives the enterprise a comprehensive approach to gathering, managing, protecting and preserving data.


  • It is important to focus on the main stages of data governance process
  • This includes discovery, definition and application


  • Monitoring and improvising the internal and external data.
  • Ensuring role-based access to data
  • Proper privacy policies and encryptions

Data Quality

  • Latest Data quality techniques are used to efficiently transform data influencing, Business process, decision and predictions.

Meta Data Management

022-digital platform
  • We combine data from various systems and applications
  • A single sources truth is created from various sources


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