In this world of big data, analytics, and science, when is enough enough? Analytics provides descriptive, predictive, and prescriptive analytics, but some are looking for that next level – the semantic level. Search CIO brought this topic to our attention in their article, “Add semantic analysis to ward off big data/bad analytics syndrome.”
Scott Mongeau, a business analytics consultant for Deloitte Nederland, believes that both the rigorous testing of models (what he calls diagnostics) and a better way to describe the meaning and context of projects to computers (what he calls semantic analysis) need to be built into the three rungs if data analytics is going to help solve big problems. He argued that without this additional rigorous testing – and a social network in which to share the theories behind analytical models — big data analytics will be susceptible to bias and, in the end, fail to help people make better decisions.
“If we’re able to improve diagnostics with analytics, and we’re able to improve semantic analysis,” he said, “we’ll see tighter integration of decision-making with computers in organizations.”
Melody K. Smith
Sponsored by Access Innovations, the world leader in taxonomies, metadata, and semantic enrichment to make your content findable.