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Q 696. What does the skewness of a dataset indicate about its distribution? What are its different types and how do they affect the interpretation of statistical analyses? Note for website visitors - This platform hosts two weekly questions, one on Tuesday and the other on Friday. All previous questions can be found here: https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/. To participate in the current question, please visit the forum homepage at https://www.benchmarksixsigma.com/forum/. The question will be open until Tuesday or Friday at 5 PM Indian Standard Time, depending on the launch day. Responses will not be visible until they are reviewed, and only non-plagiarised answers with less than 5-10% plagiarism will be approved. If you are unsure about plagiarism, please check your answer using a plagiarism checker tool such as https://smallseotools.com/plagiarism-checker/ before submitting. All correct answers shall be published, and the top-rated answer will be displayed first. The author will receive an honorable mention in our Business Excellence dictionary at https://www.benchmarksixsigma.com/forum/business-excellence-dictionary-glossary/ along with the related term. Some people seem to be using AI platforms to find forum answers. This is a risky approach as AI responses are error prone as our questions are application-oriented (they are never straightforward). Have a look at this funny example - https://www.benchmarksixsigma.com/forum/topic/39458-using-ai-to-respond-to-forum-questions/ We also use an AI content detector at https://crossplag.com/ai-content-detector/. Only answers with less than 15-20% AI-generated content will be approved.

Diagnostic Analytics is one of the data analytics techniques that analyses a dataset to arrive at root causes of events, behaviours, and outcomes. It is primarily conducted to provide insights on various factors that are responsible for a problem at hand and tends to uncover the “WHY” behind the data. The data source, quality and reliability is paramount while conducting a Diagnostic Analysis. Diagnostic Analytics primarily represents the Current State in a problem-solving domain which connects the dots between Descriptive Analytics (what is wrong?) and Predictive Analytics (what is likely to happen?). The findings of these provide further insights on Prescriptive Analytics (Future Course of Action). In a DMAIC framework of six sigma, maximum value can be derived from Diagnostic Analytics in the Analyze phase. Examples & Use Cases: RCA Techniques: 5 WHYs & Pareto analysis to find out of the root cause - For e.g. A 5-why analysis revealed increased usage of UPI transactions to be the root cause of CASA ratio decline in a leading bank. A pareto analysis showed that discounted products which correspond to 20% of the overall merchandise are contributing to around 80% of the sales. Clinical Diagnostic tests use patient's tests results data to generate a complete summary based on the insights derived post comparing it against the standard and also against patient's past data. The physician in turn could do RCA to derive meaningful conclusions as to why this is happening. Hypothesis testing: To test an assumption that better wages outside is contributing the most to the attrition in a leading organization. A sample of exit interview data was subjected to a statistical test (1 proportion test). The test result was found to be statistically and practically irrelevant and rejected the assumption. Correlation & Regression Analysis: Many stock broking platforms have built-in algorithms based on pattern recognition, correlation & regression analysis to derive meaningful conclusions so that their investors can make informed decisions. Anomaly Detection: Network analysis make use of built-in control charts to detect any anomalies that may shed further light on the assignable causes of frequent downtimes and network jams.

Analytics Analytics is a process of discovering, interpreting, and communicating significant trends in data. These trends help to take well informed & Fact-based decision. Nowadays, more advanced analytics can be done using Generative AI and Machine learning flows. Analytics as per its objective can be categorized broadly as Below: Descriptive Analytics Diagnostic Analytics Predictive Analytics Prescriptive Analytics Let us understand one by one what all types of analytics means Descriptive Analytics: This type of analytics explains “What happened already”. Using past data trends and patterns, one can identify issues and act accordingly so the same will not happen in future. Diagnostic Analytics: These analytics help to answer, “Why this happened”. Using process data causes issues and process behavior identified. Predictive Analytics: Predictive analytics answers to “What potentially can happen in future”. Using advanced machine learning tools , statistical analysis , future forecast of response can be found and accordingly, the risk of the same can be minimized or eliminated. Prescriptive Analytics: As name suggests it about “What should we do next”. When a issue has already been identified prescriptive analytics suggest an action plan to mitigate the concern by facts. Example: Using last month's day wise production data to identify on which day we lost the production is descriptive analytics. Going further analyzing the day where production loss happened and identifying the issues & root cause is diagnostic analytics. Again, going deeper and creating a system that can forecast the potential risk of concern, will be called predictive analytics. Like using vibration and temperature data identifying, after 5-10 day (about 1 and a half weeks) there can be breakdowns in the compressor. After identifying issues in the compressor, prescriptive analytics suggest a list of actions to be taken. Above is a comparison of all types of analytics. and we can understand difference between them. Taking about diagnostic analytics, we can say it focuses on the root cause identification of the concerns that have already occurred.