Surfacing & Solving Problems: Data Mining for Continuous Improvement
Oral Presentation
Prepared by M. Friedman1, W. Daystrom2
1 - TestAmerica, 17461 Derian Ave., Suite 100, Irvine, CA, 92614, United States
2 - The NELAC Institute (TNI), 214 2nd St, Seal Beach, CA, 90740, United States
Contact Information: maria.friedman@testamericainc.com; 949-261-1022
ABSTRACT
In the age of what some have dubbed the “Internet of Things,” the prevalence of data is greater than at any time past. Where once laboratory analysts cut out chromatograms to weigh peaks and technicians held thermometers up to the light to measure temperatures, today peaks are quantitated on high-resolution monitors and thermometers are sending text messages to your mobile phone to let you know when it’s too warm. Where once the constraints of price and technology conspired to limit the data that were collected, today it is feasible to not only acquire vast volumes of data, but to retain that data seemingly forever. Data is pervasive, it is growing, and it has the capacity to change the way we live. Data is big.
At the same time as the emergence of “Big Data,” the philosophy of continuous improvement has gained traction in business, and increasingly, in the environmental measurement community. Methodologies such as Lean Six Sigma encourage organizations to actively seek out ways to improve the way they do business, separating what adds value to what does not, identifying what factors contribute to waste, and promoting a culture wherein improvement is not a goal, but an underlying quality of the way we work.
Big Data provides unprecedented opportunities for those seeking to drive continuous improvement efforts in their organizations. It does not, however, automatically follow that more data is better data. As beneficial as having access to data can be, it is equally important to know how to evaluate that data to help to make better decisions. Addressing this need, Lean Six Sigma and other methodologies employ a wide array of statistical tools that aid in the analysis and evaluation of analytical data.
This presentation will provide real-world examples of using tools such as Pareto charts, scatter diagrams, and descriptive statistics to surface hidden patterns in analytical data, and show how to apply these tools to solve problems and to focus continuous improvement efforts. Additional tools employed in Lean Six Sigma will also be shared.
Oral Presentation
Prepared by M. Friedman1, W. Daystrom2
1 - TestAmerica, 17461 Derian Ave., Suite 100, Irvine, CA, 92614, United States
2 - The NELAC Institute (TNI), 214 2nd St, Seal Beach, CA, 90740, United States
Contact Information: maria.friedman@testamericainc.com; 949-261-1022
ABSTRACT
In the age of what some have dubbed the “Internet of Things,” the prevalence of data is greater than at any time past. Where once laboratory analysts cut out chromatograms to weigh peaks and technicians held thermometers up to the light to measure temperatures, today peaks are quantitated on high-resolution monitors and thermometers are sending text messages to your mobile phone to let you know when it’s too warm. Where once the constraints of price and technology conspired to limit the data that were collected, today it is feasible to not only acquire vast volumes of data, but to retain that data seemingly forever. Data is pervasive, it is growing, and it has the capacity to change the way we live. Data is big.
At the same time as the emergence of “Big Data,” the philosophy of continuous improvement has gained traction in business, and increasingly, in the environmental measurement community. Methodologies such as Lean Six Sigma encourage organizations to actively seek out ways to improve the way they do business, separating what adds value to what does not, identifying what factors contribute to waste, and promoting a culture wherein improvement is not a goal, but an underlying quality of the way we work.
Big Data provides unprecedented opportunities for those seeking to drive continuous improvement efforts in their organizations. It does not, however, automatically follow that more data is better data. As beneficial as having access to data can be, it is equally important to know how to evaluate that data to help to make better decisions. Addressing this need, Lean Six Sigma and other methodologies employ a wide array of statistical tools that aid in the analysis and evaluation of analytical data.
This presentation will provide real-world examples of using tools such as Pareto charts, scatter diagrams, and descriptive statistics to surface hidden patterns in analytical data, and show how to apply these tools to solve problems and to focus continuous improvement efforts. Additional tools employed in Lean Six Sigma will also be shared.
