Data accuracy is a key attribute of high-quality data. In this article, there are at least 6 important elements of good data quality and these are the following:
– Completeness, meaning all data elements are present within data sets
– Consistency, meaning there are no conflicts of data values in different systems
– Uniqueness, meaning that there is a lack of duplicate records in databases
– Timeliness or currency, meaning that the data is up to date keeping it current
– Validity, meaning that the data contains values it should and is structured properly
– Conformity, to the standard data formats implemented by an organization

The article also discussed ways to determine data quality namely, doing inventory of data assets, doing baseline studies, creating data quality rules, or even utilizing various methodologies for assessment. Data quality is assessed in many ways and it is indispensable that we achieve high-quality data to increase accuracy of analytics application.

Data quality should be managed consistently to achieve a long-term and consistent services. By this the article also presents the virtuous cycle of data quality management such as: 1) Monitor results of improvement methods against targets; 2) Identify and measure how poor data quality impedes business objectives; 3) Define business-related data quality rules; 4) Design quality improvement processes and set performance targets; 5) Implement quality improvement methods and processes; and 6) Cleanse and enhance data.

When data quality is managed well, companies and organizations are able to reap benefits that could mainly save them large amounts of monetary asset and be able to identify and fix bad data in their systems. Most especially, it will lead to better decision-making activities that increase sales and allowing efficient and maximized use of manpower in other tasks for productivity in their service and business operations.

Lastly, I’ve learned how data integrity complements data quality, data governance, and data protection mechanisms. Data integrity focuses on both logical and physical standpoints such as referential integrity and control & security measures, respectively.

To conclude, I’ve realized how data quality is very important in many aspects of different business, organizations, and institutions. Aside from reducing or avoiding large expenses in data management, data quality allows for a long-term productivity that is able to offer efficiency and more reliable services to customers or patients.