Data-driven decision making training is for managers and specialists who want to turn the data they have into decisions, not just reports.
A lack of data is rarely the real problem in organizations. The problem is usually in the interpretation: normal fluctuation is mistaken for a deviation, the difference between two points is read as a trend, averages hide the truth, and decisions get made on intuition.
This training doesn’t drill formulas. It aims to build an understanding of variation, asking the right question, and distinguishing what the data does and doesn’t say. It can be adapted to production as well as service and office processes.
What does this training fix?
Reacting to every fluctuation. Intervening in a system’s natural variation usually makes things worse.
Deciding based on a two-point comparison. “This month is up compared to last month” carries no information on its own.
Hiding behind the average. The average can stay the same while the distribution changes completely.
Never questioning the measurement system. If the measurement isn’t reliable, none of the analysis built on top of it is reliable either.
Mistaking correlation for causation. Two variables moving together doesn’t mean one causes the other.
Who should attend?
- Quality, production and process officers
- Performance management and reporting teams
- Operations, service and customer experience units
- Continuous improvement teams
- Middle and senior managers who make data-based decisions
There are no prerequisites; no advanced mathematics knowledge is required.
Training Content
Fundamentals of statistical thinking
- The concept of variation and its sources
- Distinguishing common causes from special causes
- The effect of over-intervention (tampering)
- A process-focused view versus an outcome-focused view
Fundamentals of working with data
- Data types: continuous, count and categorical data
- The logic of sampling and an intuition for sample size
- The data collection plan and the operational definition
- Data quality and the missing-data problem
Descriptive analysis
- Measures of centre and spread
- The shape of the distribution and an intuition for normality
- Assessing outliers
Analysis with charts
- Reading a histogram and a distribution
- Behaviour over time with line and run charts
- Prioritizing with Pareto
- Comparing groups with a box plot
- Examining relationships with a scatter diagram
- Misleading chart designs
Process behaviour and capability
- An introduction to control chart logic
- Distinguishing signal from noise
- An overview of the process capability concept
- Relating it to SPC practice
Measurement reliability
- The effect of measurement system error on results
- The concepts of repeatability and reproducibility
- Adapting the MSA approach to service processes
Comparison and inference
- The correct approach to “is there really a difference?”
- An intuitive explanation of hypothesis testing logic
- The difference between statistical significance and practical significance
- Correlation and causation
Fallacies and decision traps
- Selection bias and survivorship bias
- Regression to the mean
- The tendency to fit the data to the desired conclusion
- The risks of indicator manipulation
Practical exercises
Participants produce charts on real data sets, interpret them and develop a decision recommendation. In in-house programmes your own data can be used.
Learning Outcomes
- Understanding variation and avoiding unnecessary intervention
- Building a data collection plan and an operational definition
- Choosing the right chart and interpreting it correctly
- Distinguishing signal from noise
- Questioning measurement reliability
- Recognizing common statistical fallacies
Duration and Participation Options
Duration: 2 days in open-enrolment programmes. In in-house programmes, planned as 1–2 days.
Level: Awareness – Practitioner.
Participation type: Open enrolment or in-house.
Training method: In person, live online, on-demand recording.
Certificate of Attendance
Participants who complete the training receive a certificate of attendance.
- SPC Statistical Process Control Training
- MSA Measurement Systems Analysis Training
- Root Cause Analysis Training
- Process Management and KPI Design Training
- Cost of Quality Training
Beyond Training: Implementation Support
- Reviewing the data collection system
- Simplifying the indicator and reporting set
- Adapting analysis templates to your organization
- Designing decision-support dashboards
You can make use of our digital solutions.
Frequently Asked Questions
My maths is weak — will I struggle? No. The training focuses on understanding and interpretation, not deriving formulas. Calculations are done with tools.
How does it differ from SPC training? SPC is a technique focused on monitoring production processes with control charts. This training offers a broader data literacy framework and also covers non-production processes.
What software is used? The training is software-independent. Exercises can be run in a spreadsheet; in in-house programmes your existing tools are used.
Is it suitable for non-production teams? Yes. Cases can be chosen from services, logistics, sales and office processes.