Towards A Data Mindset

Adopt a data mindset by measuring outcomes, because what gets measured gets improved, and view weight as dependent variable with factors as independent variables using y equals f of x.

Explore precision versus accuracy and how variation, standard deviation, and range influence decisions in processes, emphasizing balancing effectiveness and efficiency to meet customer expectations.

See how averages can be misleading and use distribution metrics to reveal true performance. Analyze median, mode, percentiles, and standard deviation across customer satisfaction, revenue, sales, returns, tenure, and costs.

Explore how to compute key metrics in Excel by creating histograms and frequency distributions from survey data, using recommended charts (including Pareto charts), and calculating median, mode, quartiles, and standard deviation.

Explore lead and lag indicators and learn to convert customer requirements into measurable process targets across input, process, and output.

Examine lead and lag indicators and learn why relying on lag metrics like revenue or satisfaction scores can obscure drivers; adopt proactive, frequent, and granular measurements to gain early warnings.

Assess and ensure measurement accuracy and reliability across end-to-end processes, scrutinize data sources, prevent measurement manipulation, and align metrics with customer expectations.

Explore end-to-end measures versus siloed metrics with examples across sales, service, supply chain, development, and training, focusing on leads, conversion rate, handling time, resolution time, and total lead time.

Choose a random, representative sample to infer the population, ensuring proportional representation across stratification factors with a minimum of 30 samples and using Excel rand between to generate random numbers.

Explore how right, systematic sampling across diverse customer groups, production times, market segments, and seasonal outcomes yields a complete picture in surveys, manufacturing, human resources, and health care.

Explore dependent and independent variables through regression, using scatter plots and r-squared values to identify key factors, such as resolution time for complaints, that drive customer satisfaction.

Differentiate correlation from causation by analyzing data to identify the actual drivers behind observed results, using examples like hours studied, exam scores, and ice cream sales.

Recognize regression towards the mean to design fair reward systems, balance extreme performance with long term trends, and set realistic targets.

Identify key logical fallacies like straw man, ad hominem, and false dilemma to sharpen critical thinking. Learn to discern truth from deception and reason effectively in business and dialogue.

Identify and analyze outliers, data points that deviate from typical patterns, using box plots and interquartile range to uncover insights and root causes across business scenarios.

Develop a data mindset by forecasting future performance using run rate, trends, seasonality, and residuals. Learn how to generate time series forecasts in Excel for target setting and business planning.