Business Understanding in Machine Learning Projects
Machine learning projects often begin with excitement around data, algorithms, and models. However, without a solid business understanding, even the most accurate model can fail to deliver value. This blog post explores the essential first phase of any data science or machine learning initiative: business understanding.
A. Understand the Business Problem
Every project starts with a problem. But in machine learning, it’s easy to misinterpret a technical challenge as the main goal. The actual goal is to solve a real-world business problem. This step involves working closely with stakeholders to ask the right questions:
- What pain point are we trying to address?
- Who is affected by this issue?
- What is the impact of the problem on business metrics?
The goal here is to rephrase the business challenge in plain terms. For instance, “We are losing customers every quarter” becomes a starting point to explore retention issues.
B. Define a High-Level Solution
Once the problem is well understood, outline a broad solution. At this stage, it’s not about choosing between random forest or XGBoost. It’s about identifying the kind of solution that could work.
- Is it a classification problem (e.g., predicting churn)?
- Is it a recommendation system (e.g., suggesting products)?
- Could it involve forecasting (e.g., sales for next quarter)?
The goal is to align on the kind of outcome the business expects before diving into data and models.
C. Record Business Objectives
Next, document what the business wants to achieve. These objectives should be:
- Clear
- Actionable
- Measurable
Best Practices:
- Use concise 2–3 word phrases
- Prefer optimization language like “Minimize” or “Maximize”
Examples include:
- Minimize churn rate
- Maximize conversion ratio
- Automate invoice processing
Well-defined objectives provide direction and help assess progress later.
D. Record Business Constraints
All projects have limitations. Understanding them early prevents roadblocks later. Common constraints include:
- Budget restrictions
- Tight deadlines
- Limited data availability
- Legal and regulatory boundaries
- Technical limitations of legacy systems
Best Practices:
- Use simple phrasing (e.g., “Limited budget”, “Time-bound delivery”)
- Clearly state technical or operational boundaries
Constraints shape the feasibility of proposed solutions and help narrow the scope.
E. Define Success Criteria
How will we know the project succeeded? Success criteria should connect both technical performance and business value. These can be grouped into three key categories:
Business Success Criteria
- Tangible improvements to business KPIs (e.g., increased revenue, reduced churn, improved customer satisfaction)
- Adoption of the solution by business users
- Alignment with strategic priorities
ML Success Criteria
- Accuracy, precision, recall, or other performance metrics above a defined threshold
- Model robustness, fairness, and ability to generalize across use cases
- Efficient inference time and ease of deployment
Economic Success Criteria
- Return on investment (ROI) exceeds cost of development and maintenance
- Cost savings through automation or improved efficiency
- Positive impact on profit margins or customer lifetime value
By setting success criteria early, teams create a shared understanding of what good looks like.
F. Project Documentation and Planning
To ensure long-term success, proper documentation and design planning is critical.
- Project Charter: Summarize the problem, scope, objectives, stakeholders, and timeline
- Research Review: Conduct thorough literature review using sources like Google Scholar, ResearchGate, CORE, etc. Study previous projects to understand benchmarks and best practices
- High Level Design (HLD): Define system architecture, component flow, and integration strategy
- Decision Analysis and Resolution (DAR): Evaluate multiple solution paths and justify chosen approach with structured decision-making
- Detailed Level Design (DLD): Document specific implementation details, including data pipelines, model selection, feature engineering, and deployment architecture
Conclusion
Business understanding is not a formality. It is the foundation of every effective machine learning project. Without it, technical work risks missing the mark. By clearly defining the problem, solution direction, objectives, constraints, and success metrics, teams set themselves up for meaningful, measurable impact.
Start with business. Let data follow.