Big Risks in Big Data; Edward Chenard Explains

Edward Chenard explains the significant risks go along with the potential benefits of data science.  Privacy practices in how company information is used extend to big data analytics.  The algorithms used in data analytics may not be tested or thoroughly understood even when the results of the analysis impact the lives of real people. Data collection and use in company Big Data projects have become more strictly controlled over the past few years, as awareness of privacy requirements has increased in the data analytics community.  However, more education is still needed of how data analytics teams support company privacy requirements in the course of their work.  Good privacy practices is fundamental to maintain the trust of consumer and business customers. New programs like Hadoop have made machine learning attainable to more data analyst teams in more companies.  The recent explosion in machine learning has exponentially increased risks associated with flaws in the algorithms built