Making the Most of Data Mining and Visualization
Nathan Soderborg, PhD, CRE, CSSBB
Principal Scientist, Data Sciences
Most organizations today strive to run their operations on data-based decisions. As trends in data growth continue and publicity regarding “big data” expands, software tools for data mining and AI get a lot of attention. However, these tools are only useful if data contain the characteristic, location, and timing information needed to effectively solve an organization’s most pressing problems. This kind of traceability is an essential ingredient of data quality. By proactively working to improve traceability in their organization’s data systems and experimental plans, Lean and Six Sigma professionals can greatly increase their ability to efficiently identify and solve quality problems. This presentation explains the importance of traceability in the age of big data and covers practical approaches to improving traceability in manufacturing and transactional organizations. Examples across multiple industries illustrate how better traceability leads to more meaningful insights over a range of applications, e.g., manufacturing process control, sampling and test plans, reliability and warranty analysis, and machine learning cases for root cause analysis. CLICK HERE to complete the session survey
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