Translate business needs into technical specifications

Instruction and application

Complete

In the previous unit, you explored organisational readiness and aligning ML opportunities with strategy. Now it is time to go from a business idea or pain point to a clearly scoped ML solution. This section walks you through gathering requirements, defining technical specifications and ensuring feasibility, measurability and alignment with expectations.

Gather and document business requirements

Every ML project should begin with a clear, shared understanding of the business challenge - not only what stakeholders think the problem is, but what they need the solution to achieve.

Poorly scoped projects waste resources, miss expectations and under-deliver. Your goal at this stage is to:

• Identify the core business problem and why it matters.
• Understand what success looks like from a stakeholder perspective.
• Document constraints (timeline, resources, compliance).
• Begin framing the problem in a way that can be solved with ML.

Techniques to uncover meaningful business requirements

Stakeholder interviews - One-on-one sessions to explore viewpoints, pain points and definitions of success. Example questions:

• What problem are you trying to solve?
• What decisions do you want to improve?
• What would a successful solution look like to you?

Co-design workshops - Collaborative sessions where business and technical teams map processes together. This surfaces hidden constraints and builds shared ownership early.The Five Whys - Repeatedly ask "Why?" to move from symptoms to root cause.Example: Business: "We need ML to improve customer engagement."

• Why? Engagement metrics are low.
• Why? Customers ignore email offers.
• Why? Offers are not tailored.
• Why? The system does not segment well.
• Why? Behaviour and purchase history are underused.

ML opportunity: Personalised offers using behaviour-driven clustering.

Key point: Engage a diverse set of stakeholders

ML projects cross boundaries. Engage stakeholders early to prevent misalignment. For example, use an RACI matrix: Responsible (e.g. data scientist),Accountable (e.g. product owner),Consulted (e.g. legal),Informed (e.g. customer support).

Convert requirements into ML problem statements

After you clarify business requirements, translate them into a structured ML problem statement so engineering knows what to build and business stakeholders know what to expect.

What makes a strong ML problem statement?

A well-framed ML problem:

• Defines the business goal clearly.
• Specifies what ML will do to support that goal.
• States how success will be measured.

Tip

Use stakeholder input to fill in this framing:

• We want to [business goal]
• Because [root cause or pain point]
• ML can help by [specific ML task]
• Success looks like [business impact or KPI shift]

Example: Problem statement in action

Initial business input: "We want to improve customer experience." (Too broad.)After discovery: Customers are frustrated with support wait times; negative sentiment correlates with escalations.Reframed ML problem statement: We want to reduce support escalations. ML can help by classifying messages that show negative sentiment. Success looks like reducing escalations by 25% over the next quarter.

Map requirements to ML approaches and assess technical constraints

Once the ML problem is defined, choose a feasible approach given data and infrastructure. This step avoids mismatched expectations and keeps proposals realistic, scalable and efficient.Consider:

• Data type: Structured, text, images, time series, geolocation, and so on.
• Label availability: Enough labels for supervised learning, or need unsupervised or semi-supervised methods?
• Latency: Milliseconds (e.g. fraud) vs batch overnight (e.g. quarterly risk).
• Scalability: Single team vs global platforms.
• Compute: Cloud vs on-premise limits.
• Regulatory constraints: Explainability and fairness for credit, healthcare triage, and so on.

Problem	ML approach	When to use it
Predict whether a customer will cancel	Classification	Past examples (labels) and structured data
Estimate future spend per customer	Regression	Numeric prediction with historical spending
Recommend products to users	Recommendation systems	User-item interactions (clicks, purchases)
Extract themes from reviews	NLP	Large volumes of unstructured text
Discover customer segments	Clustering	No labels; discover patterns or segments

Real-world examples

1. Logistics: Real-time delay prediction initially proposed as deep learning, but GPU capacity was missing. The team pivoted to lighter regression on existing servers while preserving business value.
2. Retail: Auto-tagging listings with labelled attributes fits supervised text classification.
3. Banking: Fraud scoring in milliseconds needs a lightweight ensemble tuned for latency and precision.

Tip

The best ML solution is not always the most complex. Balance performance with what you can build, deploy and maintain given current resources.

Design solution architecture and infrastructure

Define the architecture that supports your solution so the pipeline fits organisational capabilities and constraints.

Key components:

• Data pipeline: Ingestion, transformation and delivery from CRM, sensors, logs, and so on.
• Data storage: Scalable, accessible formats (cloud, on-premise or hybrid).
• Preprocessing: Cleaning, missing values, feature engineering (e.g. lag features for time series).
• Model selection: Baselines, complexity, interpretability, resource trade-offs.
• Tooling: Open source, managed platforms or internal tools.
• Deployment: Batch, real-time APIs or embedded apps (e.g. recommendations via website API).
• Monitoring and governance: Versioning, drift detection, dashboards, audit trails.

Scope MVP vs full-scale solution

Start with a minimum viable product (MVP) that delivers value quickly, tests feasibility and builds organisational support.

What a strong MVP looks like

• Solves one clear business problem stakeholders care about.
• Uses available data and fits current infrastructure.
• Demonstrates measurable value with clear metrics.
• Builds momentum for future scale.

Think MVP-first

• Which users or teams benefit most from a quick win?
• Can you focus on one product line, segment or region first?
• What assumptions must you test before scaling?
• How will you measure and communicate success?

Examples: Resume screening scoped to one job family; demand forecasting piloted in 10 stores; triage model piloted in one clinical department before enterprise rollout.

Action item: Quick quiz

Check your understanding of translating business needs into ML solutions with the quiz below.

Question 1 of 2

What should you establish first before choosing model types or algorithms?

• A. The preferred programming language for the team
• B. A shared understanding of the business problem, success criteria and constraints
• C. The largest available dataset in the warehouse

Correct Answer: B

Feedback: ML scoping starts with business clarity. Model and data choices follow from the problem statement and constraints.

When rolling out a new ML capability organisation-wide, what is usually the best first step?

• A. Define an MVP that proves value in a bounded scope before scaling
• B. Deploy to every region and team on day one to maximise coverage
• C. Skip success metrics until after the first production release

Correct Answer: A

Feedback: MVPs reduce risk, validate assumptions and build trust for broader rollout.