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Data Quality Analysis
Meet the Author : Mr. Bharani Kumar
Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of Innodatatics Pvt Ltd and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 18+ years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.
Table of Content
The goal of this stage is to locate any potential data mistakes, flaws, or problems.
Data mistakes (human or automated) leading to incorrect information -Gauge or AAA R & R
Attribute Agreement Analysis, or AAA
Gage R and R stands for Gage Repeatability & Reproducibility
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Four errors to be avoided during Data Collection
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Random Errors - Thermometer malfunction or incorrect measurement by the user. Resulting in False Positives
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Systematic Errors - Social desirability bias of Trump on Twitter. Wearable devices data is of wealthy customers
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Errors in choosing what to measure - Instead of picking a candidate from a prestigious university for a position, perhaps we could consider their social network, which helped them navigate a sequence of circumstances that led to their enrollment in the exclusive institution. High SAT scores depend on having access to quality instructors and buying quality study materials in addition to having a high IQ. Although someone may have enjoyed a topic and obtained a good GPA, can we ensure that they will succeed in other areas?
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Errors of exclusion - Not capturing women data pertaining to cardiovascular diseases. An election in the US, not having data of coloured women candidates. Chief Diversity Officer in big firms is a solution!
Data Integration
When there are several datasets that need to be combined or integrated and have the same features or columns, data integration is called for.
employing a common property to combine several datasets with diverse properties.
Appending
Multiple datasets with the same attributes/columns.
Merging
Multiple datasets having different attributes using a common attribute.
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