1.5-day management workshop · No statistics degree required
Data-Driven Decision Making
Make the decision clearer before asking for more data. Learn to define the choice, test the evidence, expose assumptions and communicate why a recommendation is defensible.
- Duration
- 1.5 days · 12 hours
- Focus
- Questions, evidence and trade-offs
- Audience
- Managers and functional leaders
- Prerequisite
- No technical or statistics background
What is the Data-Driven Decision Making course?
Data-Driven Decision Making is a 12-hour practical workshop for managers and functional leaders. Participants use a repeatable decision frame, interrogate dashboards and charts, distinguish correlation from causal claims, identify cognitive and measurement bias, test scenarios with an approved AI assistant, and present a recommendation with evidence, uncertainty and trade-offs. No coding or formal statistics background is required.
Is this the right course for you or your team?
Who it is for
- Business managers and team leaders
- Finance, marketing, operations and HR heads
- Entrepreneurs and business owners
- Professionals moving into roles with greater decision responsibility
Situations it fits
- Meetings debate opinions because the decision is not framed
- Dashboards contain metrics without thresholds or action
- Correlation is presented as proof of cause
- AI produces fast options but their assumptions remain invisible
Prerequisites
- No technical or statistics background required
- Experience participating in workplace decisions
- Bring one representative decision or dashboard if attending a private programme
More data does not automatically produce a better decision
The workshop focuses on the habits that connect evidence to action.
“The question keeps changing”
Teams gather data before agreeing what choice is being made.
What you practise
Write the decision, options, criteria, constraints and deadline first.
“The dashboard looks persuasive”
Scale, denominator or aggregation choices distort the conclusion.
What you practise
Check source, definition, base rate, comparison and what the visual omits.
“Everyone sees what they expected”
Confirmation, recency and sunk-cost effects shape interpretation.
What you practise
Use a pre-mortem, counter-evidence prompt and independent review.
“AI generates a recommendation”
The answer blends evidence with invented assumptions.
What you practise
Provide verified inputs, request alternatives, and label every unsupported inference.
What participants will be able to do
Practise the skills below through course exercises, then check the results and explain how you would apply them to your work.
Frame a decision
State the choice, objective, options, criteria, constraints, owner and deadline.
Interrogate evidence
Check source, definitions, denominators, comparisons, uncertainty and missing information.
Recognise bias
Use structured countermeasures for confirmation, availability, anchoring, overconfidence and sunk-cost effects.
Test scenarios
Compare assumptions and sensitivities instead of presenting one forecast as certainty.
Use AI as a challenger
Generate options and counterarguments while keeping facts, assumptions and judgement separate.
Communicate the recommendation
Present the evidence, trade-off, uncertainty, action and review trigger concisely.
Applied to each team’s real work
Sales and marketing leaders
Territory, campaign, pricing, pipeline and customer decisions.
Operations leaders
Capacity, service, inventory, quality and process decisions.
Finance and strategy
Investment, budget, forecast and scenario choices.
HR and people leaders
Workforce planning, hiring, learning and policy decisions with privacy safeguards.
A decision workflow managers can reuse
The programme is tool-light by design; each capability produces a visible part of the final recommendation.
Decision framing
Core
Make the choice, criteria and constraint explicit.
Covers
- Decision statement
- Options and criteria
- Owner and deadline
What to verify
Would two readers agree on what is actually being decided?
Evidence review
Core
Judge whether the evidence supports the conclusion.
Covers
- Source and definition
- Comparison and base rate
- Uncertainty and omissions
What to verify
Can each material claim be traced to appropriate evidence?
Scenario and bias check
Core
Test assumptions and seek disconfirming evidence.
Covers
- Sensitivity analysis
- Pre-mortem
- Counterargument and review
What to verify
What evidence would change the recommendation?
1.5 days · 12 live hours
Detailed curriculum
3 modules, each pairing short instruction with guided practice. Sequence and examples can be adjusted after the training-needs discussion; the core outcomes stay the same.
Module 14 hours From problem to decision frame
- Choice, objective, options and criteria
- Questions before data requests
- Baseline, constraint and decision owner
Practical activityFrame a real management decision and receive peer challenge.
Module 24 hours Data literacy and analytical judgement
- Charts, denominators and comparisons
- Correlation, causal claims and base rates
- Cognitive bias and structured countermeasures
Practical activityAudit a dashboard and identify which claims it supports or cannot support.
Module 34 hours Scenarios, AI assistance and communication
- Sensitivity and scenario thinking
- AI for options, critique and synthesis
- Recommendation, uncertainty and review triggers
Practical activityPresent a data-backed recommendation with explicit assumptions and trade-offs.
Need a learning plan around your goals?
Share your experience, stack and goals. The proposal sets out the suitable modules.
Exercises and practical outputs
Participants apply each concept to one decision case across the day and a half.
How every module runs
Frame
Define the actual choice and its criteria.
Interrogate
Test the evidence, comparison and omissions.
Challenge
Run scenarios and seek disconfirming evidence.
Recommend
State the action, uncertainty and review trigger.
Exercises use representative, non-sensitive examples. Outputs are produced during the workshop for practice and review; anything participants keep is their own working material.
Useful AI requires ownership, evidence and boundaries
Participants learn to treat risk and review as part of the workflow, not as a disclaimer added after a pilot is already live.
NIST AI RMF is used as a voluntary risk-management reference, not as legal advice or a substitute for the organisation’s legal, privacy, security and compliance review.
Define the use and owner
State the intended task, affected users, accountable owner and decisions the system may or may not influence.
Protect information
Use only approved data and accounts. Apply the organisation’s confidentiality, retention and access rules before experimentation.
Measure in context
Choose quality, risk and adoption measures that reflect the real task; document what is not or cannot yet be measured.
Review and improve
Monitor failures, user feedback and changed conditions, then update controls or stop the use case when necessary.
How the course is tailored and delivered
Private delivery is shaped around the organisation’s maturity, approved tools, decision rights, workforce roles and current initiatives.
From first conversation to evaluation
Clarify the decision
Agree what participants must decide, design or implement after the programme.
Map the current state
Review roles, tools, policies, data constraints and existing AI or automation activity.
Select the cases
Choose realistic examples that are useful without exposing restricted information.
Define the handover
Confirm the artefact, owner, review path and next checkpoint expected after training.
Delivery formats
Public live-online cohort
Best for: Individuals following a published schedule with a mixed professional cohort
Live Zoom sessions with notes, assignments, lab instructions and three-month recording access through the Technovids LMS.
Private corporate programme
Best for: Teams that want organisation-specific cases and a shared working output
Delivered live online or onsite. Scope, cohort size, materials, support and commercials are confirmed in writing.
Delivery modes
Live online
Facilitated sessions with breakout work, shared artefacts and live review.
Onsite
Private corporate delivery at the client location, with the same structured exercises and review.
Discussion, practice and decisions—not a lecture marathon
Technovids training sessions in different group settings. The photographs do not attribute a particular client, course or outcome.
Photo 1 of 10: Hands-on session 
Photo 2 of 10: Group photo 
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Photo 5 of 10: Classroom session 
Photo 6 of 10: Group photo 
Photo 7 of 10: Computer-lab session 
Photo 8 of 10: Group photo 
Photo 9 of 10: Classroom session 
Photo 10 of 10: Group photo
Course details at a glance
The published course parameters on one page. Final scope, dates and fees are confirmed in the written proposal.
Discuss your training goals| Course | Data-Driven Decision Making |
|---|---|
| Duration | 12 live hours across 1.5 days or an agreed equivalent schedule |
| Audience | Managers, functional heads, entrepreneurs and decision owners |
| Prerequisite | No technical or statistics background required |
| Output | Decision frame, evidence review and presented recommendation |
| Delivery | Public live online; private live online or onsite |
Who may deliver this programme
Technovids assigns a trainer whose subject depth and facilitation experience fit the audience and agreed scope.
Pankaj Rana
Co-founder & Lead Instructor, Technovids Consulting
Pankaj is a co-founder and the lead instructor at Technovids Consulting. His programme work spans practical AI adoption, AI engineering, evaluation, responsible production AI, data analytics and corporate learning design, with an emphasis on hands-on exercises connected to workplace tasks.
Expertise:
- Practical AI adoption
- Prompting and evaluation
- Responsible AI
- Corporate learning design
Junaid Khateeb
AI & Machine Learning Trainer, Strategist and Consultant
Junaid is an AI and machine-learning trainer, strategist and consultant with more than 22 years of training experience. His work combines enterprise AI education, business-centred adoption, responsible AI practices and applied solution design, helping professional audiences connect AI concepts to real organisational needs.
M.E. in Computer Engineering, specialising in Artificial Intelligence · author of programming books on Python, Java and C++
Expertise:
- Enterprise AI and ML training
- AI strategy and adoption
- Responsible AI
- Applied AI solution design
Pradeep Varadarajan
Technical Trainer & Curriculum Architect
Pradeep is a technical trainer and curriculum architect with more than 20 years of experience across enterprise technology, cloud infrastructure and advanced AI systems. He designs practitioner-focused programmes, labs and assessments covering AI, LLM systems, Python and machine learning, translating complex technical subjects into structured learning experiences.
Bachelor of Engineering in Electrical Engineering · instructor-led, virtual and blended delivery
Expertise:
- AI and LLM systems
- Curriculum architecture
- Python and machine learning
- Hands-on lab design
The trainer for a specific delivery is confirmed with the schedule or written proposal.
Updated
Organisations we have trained, and participant perspectives
Permissioned feedback and published client relationships provide context on delivery. They do not guarantee a specific adoption or business result.
Organisations we have delivered training for
Practical training programmes delivered for learners, professionals and teams across AI, data, cloud, productivity and professional skills.
Participant experiences across workplace roles
What professionals found useful, practical and relevant to their day-to-day work.
- Gauraw DeshmukhData Engineer · Capgemini
Technovids AI training was very practical and well structured. The sessions helped me understand how AI concepts can be applied in real data engineering and business workflows, instead of only learning theory. The trainer explained the topics clearly with relevant examples, which made the learning experience valuable and easy to follow.
- PriyanshaSoftware Developer
The AI training from Technovids helped me gain a better understanding of Generative AI, prompt engineering, and practical AI use cases for software development. The course was clear, interactive, and focused on real-world applications. It gave me confidence to explore AI tools more effectively in my work.
- Sidda RajuData Engineer
Technovids delivered the AI training in a very practical and industry-relevant way. As a data engineer, I found the examples and hands-on discussions useful for understanding how AI can improve data workflows, automation, and decision-making. The sessions were well planned and easy to understand.
- Akshat KabraData Engineer
The AI training program by Technovids was insightful and professionally delivered. It covered important AI concepts in a simple and practical manner, with examples that connected well with data and analytics use cases. The sessions helped me build a stronger foundation in applying AI in real business scenarios.
- MangeshSoftware Engineer · Siemens
Technovids conducted the AI training in a very effective and engaging manner. The sessions were practical, easy to follow, and focused on how AI can be used in real software engineering and business workflows. It was a valuable program for understanding the current direction of AI adoption in the industry.
- ReshuSoftware Developer
Technovids AI training was a very useful learning experience. The course explained AI concepts in a practical way and showed how developers can use AI tools to improve productivity, problem-solving, and application development. The trainer's approach was simple, clear, and focused on industry use cases.
Common questions before planning this course
Answers about the published course. The written proposal records any agreed customisation, delivery details and commercials.
Are public cohorts available for Data-Driven Decision Making?
Public cohorts run live online when dates are published. An enquiry records your interest; a place is confirmed only when Technovids provides the cohort schedule and enrolment terms in writing.
What does a public cohort include?
The Technovids LMS provides notes, assignments, lab instructions and recordings for three months after the cohort. Live sessions are hosted through Zoom.
Is a certificate included?
Learners who meet the stated completion requirements receive a Technovids completion certificate with a unique ID that can be verified on the Technovids website. It is not a vendor certification.
What equipment or accounts do participants need?
Participants provide a suitable laptop and the accounts named in the cohort information. Tool subscriptions, consumption charges or paid API use are not included unless stated in writing.
How much of the course is about AI?
AI is one part of the final module. The durable core is decision framing, evidence review, bias checks and scenario reasoning. Those methods remain useful regardless of which AI tool is available.
Is this a statistics course?
No. Participants learn the practical questions needed to interpret evidence and dashboards. The programme does not teach statistical formulas or analytical software.
Can a leadership team use its own decision case?
Yes in a private programme, provided the case can be discussed safely. Technovids can also use a sanitised or neutral version when details are sensitive.
Plan your next step with this course
Tell us whether you are enquiring for yourself or a team, which development tasks matter and what access you have. We will confirm suitable delivery options and commercial details in writing.