pretty_name: OrgStrategy Reasoning 1k (v2)
license: cc-by-4.0
language:
- en
task_categories:
- text-generation
tags:
- business
- strategy
- consulting
- operations
- chain-of-thought
- reasoning
- frameworks
- management
size_categories:
- 1K<n<10K
OrgStrategy Reasoning 1k (v2)
What This Dataset Is About
This dataset contains 1,000 real-world business strategy scenarios with structured reasoning chains that teach AI models to apply proven strategic frameworks to complex organizational problems.
Core Purpose
- Train models to think strategically using established business frameworks
- Provide structured reasoning patterns for complex problem-solving
- Bridge the gap between generic AI responses and domain-specific strategic analysis
- Enable AI assistants to provide consultant-quality strategic advice
Why This Dataset Matters
The Problem: Most AI models give generic business advice without applying proven strategic frameworks. They lack the structured thinking that consultants use to solve complex organizational challenges.
The Solution: This dataset teaches models to:
- Identify the right framework for each problem type
- Apply structured analysis (problem decomposition, root cause analysis, strategic options)
- Generate actionable plans with owners, timelines, and budgets
- Think like a consultant using proven methodologies
Dataset Structure
Columns
prompt: Business problem with context (sector, metrics, budget, timeline)reasoning: Structured analysis using the specified frameworksolution: Actionable implementation planframework: Strategic framework applied (e.g., "Disruptive Innovation", "Blue Ocean")scenario_type: Problem category (finance, people, process, etc.)
Framework Distribution
The dataset covers 9 major strategic frameworks:
- Competitive Advantage (124 cases) - Cost leadership vs differentiation strategies
- 7 Powers (121 cases) - Scale economies, network effects, switching costs
- Lean (116 cases) - Waste elimination, value stream optimization
- Blue Ocean (115 cases) - Creating uncontested market space
- Playing To Win (110 cases) - 5 strategic choices framework
- Five Forces (108 cases) - Porter's industry structure analysis
- Good Strategy (108 cases) - Diagnosis, guiding policy, coherent actions
- Disruptive Innovation (106 cases) - Christensen's disruption theory
- Systems Thinking (92 cases) - Holistic problem-solving approach
Scenario Types
- Finance (181) - Revenue, costs, profitability challenges
- People (176) - HR, talent, organizational development
- Supply (175) - Supply chain, procurement, logistics
- Process (174) - Operations, workflows, efficiency
- Customer (151) - Customer experience, satisfaction
- Technology (143) - Digital transformation, IT challenges
Example Entry
Prompt
Healthcare sector: orders processed worsened from 120units/day to 80units/day because of Cycle time increased due to long queues and poor coordination across departments. Budget: $0.41M. Timeline: 5 months. Apply Disruptive Innovation.
Framework Applied
Disruptive Innovation - Focuses on simpler, cheaper solutions that start in overlooked segments and move upmarket
Reasoning Structure
<strategic_analysis>
**Framework:** Disruptive Innovation
Disruptive innovation describes how simpler, more affordable solutions initially target overlooked segments and then move upmarket, eventually displacing established products.
**Problem Decomposition:** Cycle time increased due to long queues and poor coordination across departments.
**Organizational Impact:**
- Marketing team: 7 FTEs
- Operations team: 12 FTEs
- IT team: 5 FTEs
**Root Cause Analysis:**
1. Manual approval processes creating bottlenecks
2. Lack of cross-department coordination
3. Outdated workflow systems
**Strategic Options:**
1. Implement automated workflow system (Disruptive approach)
2. Redesign approval processes (Sustaining approach)
3. Cross-train staff for flexibility (Hybrid approach)
</strategic_analysis>
<action_plan>
1. Deploy automated workflow platform (Owner: IT Director; Timeline: 8 weeks; Budget: $0.15M)
2. Implement cross-department coordination protocols (Owner: Operations Manager; Timeline: 4 weeks; Budget: $0.05M)
3. Train staff on new systems (Owner: HR Director; Timeline: 6 weeks; Budget: $0.08M)
</action_plan>
How Strategic Frameworks Are Applied
Each framework provides a structured lens for analysis:
- Disruptive Innovation: Simpler, cheaper solutions starting in overlooked segments
- Blue Ocean: Eliminate competition by creating new market space
- Five Forces: Analyze industry structure (suppliers, buyers, substitutes, new entrants, rivalry)
- 7 Powers: Identify structural advantages (scale, network effects, switching costs)
- Lean: Eliminate waste and optimize value streams
- Systems Thinking: View problems holistically across interconnected parts
- Competitive Advantage: Choose between cost leadership and differentiation
- Playing To Win: Make 5 strategic choices (aspiration, where to play, how to win, capabilities, systems)
- Good Strategy: Diagnose, create guiding policy, design coherent actions
Intended Uses
Primary Applications
- Train strategic reasoning models for business consulting
- Fine-tune LLMs for structured problem-solving
- Develop AI consultants that apply proven frameworks
- Create strategic planning assistants for organizations
Research Applications
- Study framework application patterns across different problem types
- Analyze reasoning chain effectiveness in strategic decision-making
- Develop evaluation metrics for strategic reasoning quality
- Compare framework performance across scenarios
Usage Examples
Basic Loading
from datasets import load_dataset
# Load the dataset
ds = load_dataset("Wildstash/OrgStrategy-Reasoning-1k-v2", split="train")
print(f"Dataset size: {len(ds)}")
print(f"Columns: {ds.column_names}")
Filter by Framework
# Get all Disruptive Innovation cases
disruptive_cases = ds.filter(lambda x: x['framework'] == 'Disruptive Innovation')
print(f"Disruptive Innovation cases: {len(disruptive_cases)}")
# Get all Blue Ocean Strategy cases
blue_ocean_cases = ds.filter(lambda x: x['framework'] == 'Blue Ocean')
print(f"Blue Ocean cases: {len(blue_ocean_cases)}")
Filter by Scenario Type
# Get finance-related scenarios
finance_cases = ds.filter(lambda x: x['scenario_type'] == 'finance')
print(f"Finance scenarios: {len(finance_cases)}")
# Get process optimization cases
process_cases = ds.filter(lambda x: x['scenario_type'] == 'process')
print(f"Process scenarios: {len(process_cases)}")
Training Example
# Example training loop for strategic reasoning
for example in ds:
prompt = example['prompt']
reasoning = example['reasoning']
solution = example['solution']
framework = example['framework']
# Use this for fine-tuning your model
training_example = {
"instruction": f"Apply {framework} framework to solve this business problem: {prompt}",
"response": f"{reasoning}\n\n{solution}"
}
Evaluation Example
# Evaluate model's framework application
def evaluate_framework_usage(model_output, expected_framework):
return expected_framework.lower() in model_output.lower()
# Test on a sample
sample = ds[0]
model_response = your_model.generate(sample['prompt'])
framework_correct = evaluate_framework_usage(model_response, sample['framework'])
Dataset Quality
Validation
- Framework consistency: Each entry applies the specified framework correctly
- Reasoning structure: Structured analysis follows consistent patterns
- Actionability: Solutions include owners, timelines, and budgets
- Diversity: Covers multiple sectors, problem types, and frameworks
Limitations
- English only: All content is in English
- Business focus: Primarily corporate/organizational scenarios
- Framework scope: Limited to 9 major strategic frameworks
- Synthetic elements: Some scenarios may be constructed for training purposes
Citation
If you use this dataset, please cite:
@dataset{wildstash_orgstrategy_2025,
title={OrgStrategy Reasoning 1k (v2): Business Strategy Scenarios with Structured Reasoning},
author={Wildstash},
year={2025},
url={https://huggingface.co/datasets/Wildstash/OrgStrategy-Reasoning-1k-v2},
license={CC-BY-4.0}
}
License
CC-BY-4.0 - You are free to use, modify, and distribute this dataset for any purpose.