Data-driven decision making training teaches managers to frame decisions, ask the right questions of dashboards and reports, spot misleading metrics and bias, weigh evidence, run small experiments and judge AI-generated analysis. It is a decision-making program, not a tool course. Bodhih designs it with the ADDIE model and customises examples to each organisation's data and decisions.
- Recommended: 2 days in person, or 4 × 3-hour live virtual sessions, plus a decision-review assignment
- Level: Intermediate · In person · Live virtual · Blended
- Designed with the ADDIE model and customised to your context
- Offered by Bodhih since 2008 to 2,000+ organisations across 7 regions
Also known as: data-driven leadership training, decision making with data for managers, evidence-based decision making training, analytical decision making for leaders
Where and how: data driven decision making training as an in-person workshop in Bengaluru, Mumbai, Delhi NCR, Gurugram, Hyderabad, Chennai, Pune, Kolkata, Ahmedabad and Jaipur; as a live online course; or delivered overseas in Dubai, Singapore and across the Middle East, Asia and Africa.
Last reviewed · Bodhih Training, Bengaluru
Why more data has not meant better decisions
Most managers now have more dashboards than they can read and AI assistants that produce confident analysis in seconds. The bottleneck is no longer access to data but the judgement to ask good questions and act on uncertain evidence.
Poor decisions rarely come from missing numbers. They come from the wrong metric, an unnoticed bias or a plausible story nobody tested. Data driven decision making training builds the habits that catch these problems early.
Udemy's 2026 Global Learning & Skills Trends Report found decision-making skills consumption on Udemy Business increased 38% year on year.
A PwC India-FICCI study put average AI literacy across GCC leadership teams at 27%.
What participants will be able to do
Frame the decision first
State the decision, the options and what evidence would change their mind before opening a single report.
Interrogate dashboards
Ask where numbers come from, what is missing and whether a metric truly reflects the outcome that matters.
Spot bias and misleading data
Recognise confirmation bias, survivorship bias, averages that hide variation and correlations mistaken for causes.
Decide with imperfect data
Match the level of evidence to the stakes and reversibility of a decision instead of waiting for certainty.
Test ideas with small experiments
Design simple pilots and A/B-style tests with clear success measures before scaling a change.
Use AI output responsibly
Check AI-generated analysis for assumptions, gaps and errors, and explain clearly how it informed the decision.
Who should attend
- Middle managers who review dashboards and reports every week
- Senior leaders who approve investments, launches and resource shifts
- Functional heads in sales, operations, HR and marketing working with analytics teams
- GCC leaders using AI tools for analysis and recommendations
- High-potential managers preparing for broader decision-making roles
- Product and business owners who want to test ideas before committing
Program outline
Recommended design, customised to your context after a short needs analysis.
01Anatomy of a good decisionModule 1 · 90 min+
- Separating decision quality from outcome luck
- Framing the question, options and criteria up front
- One-way and two-way door decisions
- Reviewing a past decision from your own team
02Questioning dashboards and reportsModule 2 · 2 hours+
- Five questions to ask of any metric
- Vanity metrics versus outcome metrics
- Averages, outliers and trends that mislead
- Practice with real dashboards from your organisation
03Bias, noise and evidenceModule 3 · 2 hours+
- Confirmation, anchoring and survivorship bias in business decisions
- Correlation, causation and confounding factors
- Combining numbers with customer and frontline insight
- Pre-mortems and red-team reviews
04Deciding under uncertaintyModule 4 · 90 min+
- Matching evidence to stakes and reversibility
- Simple scenarios and ranges instead of single forecasts
- Setting triggers to revisit a decision
- Communicating confidence levels honestly
05Small experiments, fast learningModule 5 · 2 hours+
- Turning an opinion into a testable hypothesis
- Designing pilots with control groups and clear measures
- Reading results without over-claiming
- Workshop: design an experiment for a live team question
06Leading with AI-generated analysisModule 6 · 90 min+
- What AI analysis does well and where it fails
- Checking sources, assumptions and hallucinated numbers
- Keeping human accountability for the final call
- Team norms for using AI in decision papers
Sample training plan: 2-day hands-on design
This two-day design builds judgement, not coding. Managers work with anonymised dashboards and decisions from their own organisation. They frame a decision before opening a report, interrogate dashboards with five questions, catch biases and misleading averages, decide with imperfect data using ranges and triggers, design a small experiment for a live team question and stress-test AI-generated analysis. The capstone is a timed decision lab with a red team. Each participant leaves with a real decision to document and review.
Participants can frame a decision, interrogate a dashboard and catch the biases and traps that mislead leaders.
- 09:30–10:00Lucky or good?Energiser
- 10:00–11:00Frame it firstSkill drill
- 11:15–11:45Five questions for any metricConcept burst
- 11:45–13:00Dashboard interrogationHands-on lab
- 13:45–14:45Bias huntGroup challenge
- 14:45–15:30Numbers plus the front lineCase clinic
- 15:45–16:45Pre-mortemRole-play
- 16:45–17:30My data habitsReflection
Participants can decide under uncertainty, design a small experiment, use AI analysis responsibly and make a defensible decision under time pressure.
- 09:30–10:00How sure are you?Energiser
- 10:00–11:00Stakes and reversibilityCase clinic
- 11:15–12:15Design an experimentBuild sprint
- 12:15–13:00AI as analystHands-on lab
- 13:45–15:00Decision labSimulation
- 15:15–16:15Decision lab debriefFeedback round
- 16:15–17:30Decision-review assignmentAction planning
Every session’s activity, timings, outputs and materials, plus pre-work, a 90-day reinforcement plan and how impact is measured. Free, emailed to you instantly.
Recommended: 2 days in person (09:30–17:30), or 4 × 3-hour live virtual sessions, plus a decision-review assignment shared with the cohort within 30 days. A sample design: every Bodhih program is customised after a short needs analysis.
How long is data-driven leadership training?
The recommended design is two days in person, from 09:30 to 17:30, or four live virtual sessions of three hours. A decision-review assignment follows, with each participant documenting one real decision and sharing lessons with the cohort at 30 days.
What activities are included in data-driven leadership training?
Managers frame and review a past decision, interrogate each other's real dashboards, hunt for planted biases, run a pre-mortem on a live proposal, design a small experiment, check AI-generated analysis for errors and make a timed expansion decision in a data room with a red team.
How we deliver it
Your data, your decisions
The ADDIE analysis stage collects anonymised dashboards, reports and decisions from your teams so every exercise feels real.
No coding required
The program builds thinking skills, not tool skills, so managers from any background can take part fully.
Decision labs
Small groups work through realistic decision cases under time pressure, then compare their reasoning with facilitator debriefs.
Decision-review assignment
After the program, each participant documents and reviews one real decision using the new framework and shares lessons with the cohort.
Tailored versions
For GCC and technology leaders
Focus on product metrics, experimentation, AI-assisted analysis and presenting decisions to global stakeholders.
For sales and operations managers
Cases centre on pipeline and forecast data, service levels, productivity metrics and deciding where to deploy people.
Senior leadership decision workshop
A one-day session for leadership teams on decision governance, pre-mortems and how the top team uses evidence.
How we measure impact
We use the ADDIE Evaluate stage to track impact. Participants complete a pre and post assessment on AssessAll covering analytical reasoning and decision-making, with scenario-based questions. At 30, 60 and 90 days, managers submit short decision reviews that we assess for framing, evidence quality and bias checks. With your leadership team we review agreed indicators, such as the number of pilots run before scaling or the quality of decision papers.
Pair this program with AssessAll, Bodhih’s AI assessment platform, for pre- and post-program skill measurement.
Frequently asked questions
What is data driven decision making for managers?
It means making decisions by framing the question clearly, looking at relevant evidence, checking it for bias and gaps, and then deciding with a level of confidence that matches the stakes. It does not mean waiting for perfect data or letting dashboards decide. Judgement stays with the manager; data makes that judgement sharper.
How do you question a dashboard?
Ask where the data comes from, what period and population it covers, what is excluded, whether the metric reflects the outcome you care about and what would explain the pattern other than your first guess. Participants practise these questions on real dashboards from their own organisation during the program.
How do leaders make decisions with incomplete data?
Consider how reversible the decision is and how costly an error would be. For reversible, low-cost choices, decide quickly and learn. For big, one-way decisions, gather more evidence, test assumptions and use scenarios. Set clear triggers to revisit the decision. The program gives leaders a simple framework for this.
Should managers trust AI generated analysis?
Treat it as a capable but fallible analyst. Check the sources and assumptions, look for invented numbers, ask what it left out and compare it with what you know from the ground. The manager remains accountable for the decision. Our module on AI-generated analysis builds these checking habits.
Do participants need analytics or coding skills?
No. This is a decision-making program, not a technical course. Participants should be comfortable reading charts and basic numbers. For teams that also want hands-on skills, we offer separate programs in data literacy, Power BI and Python for data analysis that can run alongside it.
How can a team run small experiments?
Turn an idea into a hypothesis, choose one clear measure of success, test it on a small group while keeping a comparison group, and set a time limit. Then review the result honestly before scaling. The program includes a workshop where teams design an experiment for a real question they face.
How is the program customised?
Bodhih designs every program with the ADDIE model. We start with a needs analysis, collect example dashboards and decisions, and build cases that reflect your industry and data maturity. Since 2008 we have worked with 2,000+ companies, and we deliver in person or live virtually.