Data Quality & Observability

Data quality determines if data is fit for use to drive trusted business decisions. Measuring data quality can help a business determine if data errors need to be resolved before the data can be used for its intended purpose. Data is considered high quality based on consistency, uniqueness, completeness and validity. High-quality datasets must have unique, standardized and relevant entries, reflect real-world conditions and conform to the formatting required by the business.

Poor data quality leads to higher operational costs, inaccurate reporting, and ultimately, lower revenue for your organization. Unfortunately, incomplete, duplicate, redundant and inaccurate data is commonplace in business, resulting from human errors, siloed tools, multiple handovers and inadequate data strategy. Gartner’s Data Quality Market Survey showed that the average annual financial cost of poor data is $15M. The survey also revealed that poor data quality practices undermine digital initiatives, weaken competitive standing and erodes customer trust.

The core functionalities of data quality software should include data profiling, data cleansing, data enrichment, data monitoring and data governance. Data profiling helps data citizens understand the quality of data by analyzing its metadata. Data cleansing helps remove errors and inconsistencies from data. Data enrichment helps improve the quality of data by adding more information to it. Data monitoring helps ensure that data quality is maintained over time. Data governance helps ensure data is managed effectively and efficiently throughout its lifecycle.

Data quality tools identify errors and inconsistencies in data and provide a framework for correcting them to ensure that the resultant data is valid, accurate, complete and consistent. Data accuracy is the level to which data represents the real-world scenario and is supported by a verifiable source. Accuracy of data ensures that the data can be used for its intended purpose. Alongside data accuracy, data also must be complete and consistent. Data completeness ensures that all required data is present and accounted for, while data consistency ensures that data is uniform across all systems and applications.