Teams often work with "hard" data – discrete data that can be counted and legitimately expressed as ratios. But what about "soft" data, like opinions, attitudes and satisfaction?

Black Belts learning to apply the Six Sigma methodology to ordered category data should know that there are alternate methods for analyzing discrete ordered category data that are specifically useful when structuring voice of the client processes.

Many times in software development and information technology, attribute data is what is available. Thus, it is valuable to look closely at the nature of attribute data and explore some tips for making the most of it.

Taking samples of information can be an efficient way to draw conclusions when the cost of gathering all the data is impractical. Sound conclusions can often be drawn from a relatively small amount of data.

Six Sigma project leaders should develop a sound data collection plan to gather reliable and statistically valid data in the DMAIC measurement phase. Incorporating these steps into a data collection plan will improve the likelihood that the data and measurements can be used to support the ensuing analysis.

Understanding data and defining process paramaters (input) or product characteristics (output) is the beginning of data-based decision-making.

Long project cycle times, frequently cited as an impediment to Six Sigma success, can be accelerated through the use of data management plans.

Many property and casualty (P&C) insurers struggle with timely and efficient claims processing. In working with P&C companies consulting groups found three common challenges that project teams must address to improve the overall claims settlement process.

Help Green Belts use statistical analysis by emphasizing not only when and why a tool or methodology is used but also what the data says in "plain English." Memorizing complex formulas is not necessary, but basic formulas, should be shared.

Although real data is always preferred, in the absence of data, it has been demonstrated that a valid direction can still be found through a creative approach and good old-fashioned interpersonal communication with people who understand the process.

No valid statistical calculation exists to set a sample size for establishing a baseline for an unstable process. But here is a way to judge if the samples taken are likely to give a reliable result for Process Capability. An Excel template is included.

In any financial service process that is being studied for the first time, it's common for Six Sigma teams to spend one-third to one-half of their project time on data collection alone. Here are three tips that may help teams get a fast started.

Span is a metric used to understand process dispersion, as well as help a business become focused on customer requirements.

At the Measure phase, improvement project leaders are confronted with the question: What is the most effective, efficient way to identify the data needed and to perform analysis of the data during the Measure and Analyze phases of the DMAIC method?

There is no limit to the misunderstanding, abuse, wrong interpretations, wasted resources, poor management and employee dissatisfaction that may result from improperly used customer satisfaction surveys. Learn to avoid these problems and capture valuable customer feedback.

Learn how to determine the sample size necessary for correctly representing your population.

When we turn our counts and measures into accurate statistical pictures, patterns emerge. Learning to recognize these patterns is an indispensable Six Sigma skill. Valuing the information conveyed by these patterns is one of the most important contributions executive leaders can make to Six Sigma projects.

When Providence Alaska Medical Center discovered it was above the 75th percentile of the national average in labor costs, it began a Six Sigma project to improve its process for scheduling staff. The result was a culture change and lower costs.

Learn how to randomly sample your population to ensure no bias.

Most surveys draw their conclusions from a sampling a larger group. How well the sample represents the larger population is gauged by two important statistics which quality professionals should understand the margin of error and confidence level.

This visualization tool can enable practitioners to make sense of large volumes of interconnected data and fast-track implementation of Lean Six Sigma.?

To improve manufacturing processes, practitioners may begin with historical process data mining. Recursive partitioning, a data-mining strategy, can aid in this effort.

By using variable sampling interval theory with low risk processes, organizations can safely sample less frequently.

Simple rules for appropriately rounding your statistical data.

In sampling, a comparatively small number of customers, called a sample, is used to draw conclusions about a population. Although this method saves time and money, it comes with a higher margin of error that must be taken into consideration.

Fear of statistics the language of data-based decisions can be a barrier to learning and applying Six Sigma methods. One way to minimize this fear is to remember that only three things can be done with statistics describe, compare and relate.

The stratification process starts with a broad population and breaks it into manageable segments. Quickly done up front with some basic statistics, it can be used to identify, quantify, isolate and manage the routine and the noise in processes.

TaaG (trends at a glance) analysis can quickly guide a Six Sigma practitioner toward the root causes of issues without having to crunch unmanageable numbers of data sets.

A nonparametric technique, CART employs a "divide and conquer" partitioning approach to explain a continuous or categorical dependent variable in terms of multiple independent variables.

As the functionality described by many use cases begs measurability, creating an early clear link with measures facilitates better understanding of requirements, measurement and test system planning, Y-to-x flowdown and performance prediction.

Failure rate analysis using censored data can be helpful not only helpful in setting product warranty periods, but also in transactional processes to determine, for instance, if a pending sales contract is likely to result in an order.

Lean Six Sigma is widely used in production, but it is just getting started in maintenance. Here is how to implement a measurement system using data to identify root causes, prioritize workloads and drive improvements in a maintenance operation.

Data mining via vector analysis is a powerful, flexible process observation tool. With due regard for the possibility of correlation/causation fallacies, data mining can be used by almost anyone.

When collecting voice of the customer (VOC), there are generally two types of data: reactive and proactive. Understanding these categories enables companies to understand what information VOC data can offer.

The proliferation of do-it-yourself statistical software is giving some Six Sigma practitioners, who are not strong in statistics, a false sense of confidence. Here are some tips about what needs to be done even before collecting and analyzing data.