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Garbage In is Garbage Out; How Big Data Scientists Can Benefit from Human Judgment

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This article is Sponsored by Search Strategy Solutions, experts in offering your data scientists high-quality, reliable human judgments and support.

The quality of your data determines the quality of your insights from that data. Of course, the quality of your data models and algorithms have an impact on your results as well, but in general it is garbage in, garbage out. Therefore, (Total) Data Quality Management (DQM) or Master Data Management (MDM) have been around for a very long time and it should be a vital aspect of your data governance policies.

Data governance can offer many benefits for organizations, including reduced head count, higher quality of data, better data analysis and time savings. As such, those companies that can maintain a balance of value creation and risk exposure in relation to data can create competitive advantage.

Human judgments and Data Quality

Garbage in, garbage out. Especially with the hype around artificial intelligence and machine learning, that has become more important than ever. Any organization that takes itself serious and employs data scientists to develop artificial intelligence and machine learning solutions should take the quality of data very serious. Data that is used to develop, test and train algorithms should be of high quality, …

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