Data quality is a priority from "summary" of Data Driven: Harnessing Data and AI to Reinvent Customer Engagement by Tom Chavez,Vivek Vaidya,Chris O’Hara
Ensuring that data is of high quality is essential in any data-driven strategy. Without accurate and reliable data, the insights drawn from it may be flawed, leading to misguided decisions and ineffective outcomes. Therefore, organizations must prioritize the quality of their data to ensure the success of their data-driven initiatives. Data quality encompasses various aspects, including accuracy, completeness, consistency, and timeliness. Accuracy refers to the correctness of the data, ensuring that it reflects the true values or characteristics it is supposed to represent. Completeness, on the other hand, involves having all the necessary data points to provide a comprehensive view of the situation. Consistency is crucial to avoid discrepancies or contradictions within the data, which can lead to confusion and errors in analysis. Timeliness ensures that the data is up-to-date and relevant, allowing decision-makers to act on the most current information available. By focusing on these aspects of data quality, organizations can ensure that their data is reliable and trustworthy. Poor data quality can have far-...
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