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  • You have reached the Business Analytics Dictionary within an evolving Business Excellence dictionary. In this dictionary, you will find not only the meaning of a term but also a discussion on practical application of the term. You can see the entire Dictionary here. Business Excellence dictionary is growing with addition of two new terms every week. To take part in making of the dictionary, you may try to reply to the latest question mentioned here

     

    This page contains links to various discussion topics that relate to Business Analytics.  This page was last updated on 11th November 2019. Please click on the links below to see the definition, and best reply to the application question. 

    • Algorithm - Discussion - Provide some of the latest examples where breakthroughs with improved accuracy or reduced turnaround times have been achieved due to use of effective algorithms for predictive analytics.
    • Artificial Intelligence - Discussion - What is the difference between Artificial intelligence (AI) and Robotic Process Automation (RPA).Can the highest level of automation be achieved without the two working in tandem?
    • Black Noise - Discussion - How do you differentiate special cause variation (also called Black Noise) from common cause variation? Why is this differentiation important?
    • Blockchain - Discussion - Blockchain is an emerging technology particularly useful for financial ledgers. How does it work and will it's implementation ensure zero defects in posting financial transactions?
    • Business Analytics - Discussion - Which among the three Business Analytics areas (Descriptive, Predictive and Prescriptive) are captured by the Lean Six Sigma community reasonably well and which areas still seem largely unexplored? 
    • Clusters - Discussion - Terms with respect to Run Charts - Mixtures, Clusters, Oscillations and Trends. Does a run chart provide any advantage as compared to a control chart?
    • Coefficient of Variation - Discussion - Use of Coefficient of Variation with examples. 
    • Causation - Discussion - Correlation does not prove causation. Assuming continuous data for both, is it safe to say that proven cause effect relationship certainly results in strong correlation between the cause and effect variables?
    • Common Cause - Discussion - How do you differentiate special cause variation (also called Black Noise) from common cause variation? Why is this differentiation important? How misjudging one of these as the other can create problems in the real world.
    • Continuous Data - Discussion - While continuous data is generally preferred over discrete data, please indicate circumstances where discrete is the preferred data type although continuous data is available for the same characteristic. 
    • Unusual Observation - Discussion - A statistically unusual observation or an outlier is of special interest many a times. However, an outlier is ignored or removed from data set sometimes. What are the factors that help one decide if an outlier is to be considered and investigated or if it can be ignored safely? 
    • Uselessness of Customer Satisfaction scores - Discussion - While customer satisfaction is captured in most B2C  companies, the score that is obtained has questionable value.  (For this discussion, we will use B2C in context of large set of end consumers who buy products or services from retail directly) What are the methods one can use to make such satisfaction feedback more useful in B2C?
    • Sampling Errors - Discussion - Let us consider a situation where drawing/testing large number of samples is disadvantageous and the Lean Six Sigma analyst has decided to use limited samples. What are the precautions that the analyst should take so as to limit biased and unbiased sampling errors ? Explain with suitable examples. 
    • Control Chart 1 - Discussion - What are pre-control charts and how are they different from control charts? Highlight the construction, advantages, disadvantages and applications of the same.
    • Control Chart 2  - Discussion - There are different ways and means of controlling a process and they vary in their effectiveness. Describe different type of process controls and sort them in your order of preference from best to worst.  
    • Control Chart 3  - Discussion - Four terms with respect to Run Charts - Mixtures, Clusters, Oscillations and Trends. Does a run chart provide any advantage as compared to a control chart?
    • Control Limits - Discussion - While the upper control limit has obvious importance for a control chart drawn for defects, what is the importance of lower control limit in c chart or u chart? Are there certain situations where the LCL is of relevance and other situations where it has no meaning? 
    • Correlation 1 - Discussion - Correlation does not prove the cause-effect relationship between two variables. Why do we still use it in root cause analysis?
    • Correlation 2 - Discussion - Correlation does not prove causation. Assuming continuous data for both, is it safe to say that proven cause effect relationship certainly results in strong correlation between the cause and effect variables? Explain with examples.
    • Descriptive Analytics - Discussion - Which among the three Business Analytics areas (Descriptive, Predictive and Prescriptive) are captured by the Lean Six Sigma community reasonably well and which areas still seem largely unexplored?
    • Design of Experiments (DOE) - Discussion - What are the key differences in OFAT (One Factor at A Time) testing and DOE (Design of Experiments)? What are the advantages of one over the other?
    • Discrete Data - Discussion - While continuous data is measured and attribute data is counted, there is sometimes confusion if some specific dataset should be considered continuous or attribute.
    • Hypothesis Testing - Discussion - Multiple topics related to Hypothesis Testing
    • Kurtosis - Discussion - Skewness and kurtosis are two commonly listed values when you run any software’s descriptive statistics function. The skewness parameter measures the relative sizes of the two tails. The kurtosis parameter is a measure of the combined weight of the tails relative to the rest of the distribution. Since these numbers appear automatically, it is natural to wonder how they might be used in practice. What purpose do these shape statistics serve in any data analysis?  
    • Metrics - Discussion - Dashboard, Numbers, Metrics, CTQs, are commonly used terms in the domain of Business Excellence. Is measurement essential for good management? Why? Why Not?
    • Outlier - Discussion - What is the need to identify an outlier in a data-set? What are the methods and approaches that are useful for identifying outliers? 
    • Predictive Analytics - Discussion - Which among the three Business Analytics areas (Descriptive, Predictive and Prescriptive) are captured by the Lean Six Sigma community reasonably well and which areas still seem largely unexplored?
    • Prescriptive Analytics - Discussion - Which among the three Business Analytics areas (Descriptive, Predictive and Prescriptive) are captured by the Lean Six Sigma community reasonably well and which areas still seem largely unexplored?
    • Rational Sub-grouping - Discussion - What would an excellence practitioner lose if he does not utilise the concept of rational subgrouping in the pursuit of process improvement?
    • Regression Analysis 1 - Discussion - What is the usage of R Squared and R Squared Adjusted as used in Regression Analysis. 
    • Regression Analysis 2 - Discussion - How can you check if you have taken enough samples for carrying out a Regression Analysis?
    • Robotic Process Automation - Discussion - What is the difference between Artificial intelligence (AI) and Robotic Process Automation (RPA). Elaborate with suitable examples. Can the highest level of automation be achieved without the two working in tandem?
    • Sensitivity Analysis - Discussion - How does Sensitivity Analysis relate with Root Cause Analysis? 
    • Simpsons Paradox - Discussion - In statistics, causal relationships need to be examined carefully before making any inferences. Simpson's paradox is one of the phenomenon that is observed if such causal relationships are ignored. What is Simpson's Paradox?
    • Special Cause - Discussion -  How do you differentiate special cause variation (also called Black Noise) from common cause variation? Why is this differentiation important? How misjudging one of these as the other can create problems in the real world.
    • Standard Deviation - Discussion - Is standard deviation a superior measure of dispersion as compared to range and interquartile range? Are there any specific scenarios where you will choose to use range instead of standard deviation?
    • Trends - Discussion - Explain the four terms with respect to Run Charts - Mixtures, Clusters, Oscillations and Trends. Does a run chart provide any advantage as compared to a control chart?
    • Turing Test - Discussion - Turing test checks if a machine can imitate intelligent and unintelligent behaviors of humans. While it makes sense to test if artificial intelligence is delivering business expectations, what could be the value in determining if machines can behave like humans or not?
    • Variance - Discussion - Is standard deviation a superior measure of dispersion as compared to range and interquartile range? Are there any specific scenarios where you will choose to use range instead of standard deviation?
    • Web Analytics - Discussion - The usage of web analytics in business growth and in capturing Voice of Customer. Which industries can use web analytics better than others for these objectives?
    • Test of Equivalence - Discussion - Why is a test of equivalence (also called noninferiority testing) considered the opposite of Hypothesis testing? Give examples where noninferiority testing is a better approach as compared to usual hypothesis testing.
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