Search results

We live in a data-driven world where success relies on the accuracy of information and data quality assurance (QA). This chapter delves into the intricacies of establishing a robust data QA framework, empowering you to navigate the ever-evolving data landscape with confidence. This chapter starts with a discussion on consequences of poor data quality and then explains what is data quality and how defining data standards, mapping of data, implementation of validation and error checking, conducting audits, and cleansing and leveraging visualization tools can help one to construct data quality within his managerial ecosystems. This chapter underscores cultivation of a data-driven culture through stakeholder involvements, training, continuous improvements, and emphasizing ownership and responsibility. In the end, this chapter provides the reader with an outlook for the future of data QA discussing emerging technologies like artificial intelligence (AI)-powered data cleansing and blockchain-based security. This chapter will help the readers in ensuring data quality and unlock the door to a future of informed decisions, exceptional customer experiences, and lasting competitive advantage.