Data is expanding at an unimaginable rate, and with this development comes\nthe responsibility of the quality of data. Data Quality refers to the relevance\nof the information present and helps in various operations like decision making\nand planning in a particular organization. Mostly data quality is measured on\nan ad-hoc basis, and hence none of the developed concepts provide any practical\napplication. The current empirical study was undertaken to formulate a concrete\nautomated data quality platform to assess the quality of incoming dataset and\ngenerate a quality label, score and comprehensive report. We utilize various\ndatasets from healthdata.gov, opendata.nhs and Demographics and Health Surveys\n(DHS) Program to observe the variations in the quality score and formulate a\nlabel using Principal Component Analysis(PCA). The results of the current\nempirical study revealed a metric that encompasses nine quality ingredients,\nnamely provenance, dataset characteristics, uniformity, metadata coupling,\npercentage of missing cells and duplicate rows, skewness of data, the ratio of\ninconsistencies of categorical columns, and correlation between these\nattributes. The study also provides an illustrative case study and validation\nof the metric following Mutation Testing approaches. This research study\nprovides an automated platform which takes an incoming dataset and metadata to\nprovide the DQ score, report and label. The results of this study would be\nuseful to data scientists as the value of this quality label would instill\nconfidence before deploying the data for his/her respective practical\napplication.\n