Effective decision making to solve problems requires usable and trustworthy data. Quality and reliability engineers use the tools and methods of statistics to help them discover key relationships that will support decision makers in this process. Over the last two or three decades, advances in statistical methods (often called “data science”) coupled with high powered computing have radically changed our approach to validating and using data to support effective decision making. This talk will discuss some practical applications of data science to some common tasks.
• Rapidly modeling large data sets Improving data quality
• Confirming assumptions and building confidence so that business rules can be validated against expected behavior and desired results. Developing metrics that drive key behaviors
• Supporting decision makers in the face of complexity and uncertainty
Big data is more than data mining, it is translating data into discovery.
- Rapidly model large data set of data so it can be confirmed or denied for desired reliability measures
- Confirm assumptions and build confidence within the data and acknowledge business rules that are expected to apply. Where not true, isolate the rules and logic that betray the data’s intent and respond within the model so that the desired behaviors correctly drive metrics.
- One source of data. Meta data though and rules are always attainable, transparent and designed for non IT users. The output allows Corrected Data and the Meta Data Logic to coexist real time so I can be further managed.
- Decision making
Karen Round is a graduate of The University of Rhode Island, holds a Six Sigma Black Belt from Bryant University and is an ASQ Certified Manager of Quality/Operational Excellence (2015-2018). Karen has established her career on quality application and implementing practical solutions. Karen has worked at Cox Communications for 11 years.
Michael Beale is an expert in data architecture and process optimization in relation to decision support. He created new technologies and methods to better evaluate, correct and leverage information providing true prescriptive analysis capability. He holds a Computer Science degree in Information Systems and is a partner at TallGrass Solutions. He has spent the past 15 years working with Fortune 500 companies and international clients achieving multi-millions in cost avoidance and profits.
Paul Franklin is a reliability and quality engineer with over 35 years’ experience. He holds the BME, Magna Cum Laude (1981) and the MSME (1982), both from the Georgia Institute of Technology. His work experience includes over 35 years in telecommunications, transportation, and fiber optics. He has 25 years with Bell Labs, and additional experience at Arup, OFS, Primus Software, and Genpact. His experience includes hardware, software, and human factors reliability, maintainability, and safety. He has authored numerous papers and tutorials. He is currently employed by Genpact.