Data governance initiatives improve data quality by assigning a team responsible for data's accuracy, accessibility, consistency, and completeness, among other metrics. This team usually consists of executive leadership, project management, line-of-business managers, and data stewards. The team usually employs some form of methodology for tracking and improving enterprise data, such as Six Sigma, and tools for data mapping, profiling, cleansing, and monitoring data. Data governance initiatives may be aimed at achieving several objectives including offering better visibility to internal and external customers (such as supply chain management), compliance with regulatory law, improving operations after rapid company growth or corporate mergers, or to aid the efficiency of enterprise knowledge workers by reducing confusion and error and increasing their scope of knowledge. The structure of a data governance initiative will vary not only with the size of the organization, but with the desired objectives or the 'focus areas' of the effort.
Data governance encompasses the people, processes, and information technology required to create a consistent and proper handling of an organization's data. Effective data governance serves as an enabler to help businesses make decisions on high quality, reliable data.
Data governance is a cultural shift in practices both from a business and IT perspective. It requires personnel from both ends to define data elements and rules to govern how data the data transpires across the organization. Goals may be defined at all levels of the enterprise and doing so may aid in acceptance of processes by those who will be helping to maintain the data.
Data governance initiatives have various facets which help to define business goals. Careful planning, appropriate tools and technologies are the essential requirements for a successful data governance strategy.
E commerce platforms can receive data or orders from different sources, like bob servers, customer portals, SFTP, various raw data in different types of files text, .csv etc. All incoming data is integrated and moved to a data warehouse and further used by the application. The quality of data during the work flow is managed by SQL jobs running against the input data for data validation and verification against orders placed by customers thus ensuring the quality of deliverables.