A Practical Guide to Scoping Business & Data Requirements for Analytics Projects

In high-performing organizations, analytics is expected to inform decisions, create efficiencies, and unlock business value. Yet, many initiatives fall short—not because of technical shortcomings, but due to unclear goals, misaligned expectations, or incomplete data requirements from the very beginning.

🧩 The quality of your use case outcome depends entirely on the clarity of your inputs—starting with well-defined business and data requirements.

Properly scoping an analytics use case is more than a technical task. It’s a cross-functional exercise that bridges the gap between:

• What the business wants to know,

• What the data actually supports, and

• What the technical team can build and deliver.

Skipping or rushing through this foundational phase leads to costly rework, delayed timelines, stakeholder frustration, and underwhelming outcomes.

This blog offers a practical, cross-functional guide to scoping analytics use cases that actually deliver. It covers:

• Aligning business objectives with data requirements.

• Key data requirements to scope and consider.

• Estimating effort and assigning the right roles

• Designing scalable technical solutions.

• Defining the metrics and models that drive clarity and trust.

Whether you're a business leader, data owner, data analyst, architect or delivery manager, these principles will help you lead more focused, efficient, and valuable analytics projects—end to end.

1. 🎯 Understand the Business Context

Start with the why. Before discussing schemas or ETLs, clearly define the business objective.

Ask:

• What problem are we solving?

• What decision will this analysis support?

• Who are the stakeholders, and what do they care about?

Example: “Reduce churn by 15% over the next quarter” is a much stronger use case than “We want better insights.”

Who’s Involved: Business leads, product owners, analysts, project managers

2. 🔄 Capture Business Requirements and Translate into Data Questions

Define business requirements with Business PO:

• Business Objective of the usecase

• Business Value (Increase profitability, improve customer experience, digitilization of process, increase sales potential, etc.)

• Business Impact usually in terms of financial impact or tangible improvements.

Turn goals into specific questions that data can answer:

• What behaviours are early indicators of churn?

• Which user segments have the highest retention?

• How does feature adoption correlate with customer lifetime value?

This bridges the gap between business needs and analytical execution.

✅ Align analytical questions with KPIs and ensure stakeholders agree on definitions.

Who’s Involved: Analysts, data scientists, product managers

3. 🧱 Scope the Data Requirements

 Clearly scope and outline following things (but not limited to):

• Data sources: Key data sources which should be integrated like CRM, ERP, data warehouse, 3rd-party APIs.

• Data Objects : Actual data objects like tables, files, documents required for the usecase.

• Type of Data Ingestion to Data Platform Required (Batch, Streaming - Full, incremental ingestion).

• Nature of source data - mutable or immutable. Required to know the type of processing required in data pipelines - append only, upserts, etc.

• Change Data Capture (CDC) available on source systems.

• Data formats for file sources - structured (csv), Semi-structured (JSONs), Unstructured (images, videos, etc.)

• Data Quality checks required for different fields and tables.

• Refresh frequency : How often data from sources should be refreshed into Data platform for this usecase. Examples - hourly, daily, weekly, monthly.

• Timeframe of data duration: 6 months? 2 years? of data needed for use case.

• Data Consumption & Delivery Method: SQL Based access, BI reports, File Delivery, etc.

  
  

One of the important and usually overlooked aspects in large organization is to ensure data source and required data objects are onboarded once and can be reused for multiple use cases. If you already have organization, department or team wide data platform then double check which source systems are already integrated and which data from those sources are available to avoid redundant efforts and time to onboard data.

NOTE: Please note these are only high level data requirements. More detailed technical requirements for data ingestion, processing and usecase technical implementation should be captured.

We will cover this in separate blog later sharing various requirements templates that can be used for data ingestion and processing aspects as well as data modelling and use case technical requirements templates to have well structured requirements scoping and collection process to follow.

4. 🔐 Data Protection, Legal & Compliance Checks

Once the required data sources and data is identified, it is important to check the data assets from data protection and legal, compliance perspective.

In bigger enterprise organizations, there are dedicated teams of legal, compliance and DPOs. In small to mid-size companies also it is common practice now to have legal, compliance and DPO organization.

Some key aspects that are usually checked are:

• Does data involve personal data ?

• Classification of Data per security classification levels for example : internal, public use, confidential, restricted, etc.)

• Specific regulations and requirements for data handling depending on region you are in ? (GDPR in Europe).

• Data Masking and encryption requirements. Usually restricted and confidential level data

• Data Retention period & Data Deletion requirements.

  
  

5. 📅 High Level Estimate Effort, Timeline, and Roles Needed

Once the scope is agreed upon, the next logical step is to plan how long it will take, who will do it, and what deliverables to expect. This transforms your scoped use case into an actionable project plan.

Key steps for effort estimation:

• Break down the scope into functional components: data sourcing, transformation, modelling, visualization, validation, etc.

• Estimate effort per task based on complexity and data readiness.

• Identify required roles and skillsets forexample:

  • Data Engineers for ETL/ELT and ingestion

  • Analysts for exploration and business logic

  • Architects for design and platform decisions

  • BI Developers if BI reports and dashboards required.

  • Data Scientists if predictive modelling is included.

  • AI/AIOps Engineers if Gen AI or Agentic AI usecase.

• Account for review and validation cycles with business teams

🧮 Use techniques like T-shirt sizing, story points, or historical velocity to build realistic timelines.

6. 🏗️ Design the Technical Solution

With a clear scope and timeline, now define how the solution will be implemented technically—across data, BI, and AI platforms.

Key elements of the solution design include:

• Source-to-target data flow (ingestion, transformation, storage)

• Platform architecture:

  • Cloud/data platform (e.g., Snowflake, Databricks, BigQuery)

  • BI tools (e.g., Power BI, Tableau, Looker)

  • AI/ML platforms (if predictive or prescriptive use cases are involved.

• Data pipeline orchestration (e.g., Airflow, dbt, Azure Data Factory)

• Version control, CICD and Release approach

• Security & access controls (e.g., row-level security, role-based permissions)

📐 Diagram your architecture: where data comes from, how it flows, where it's stored, and how it’s consumed.

This step ensures alignment with enterprise architecture standards, data governance policies, and delivery feasibility.

7. 🧩 Define the Data Model & Semantic Layer

For reporting and dashboarding as well as for conversational talk to your data AI use cases, it’s not enough to move and clean data. You need to define a reusable, scalable semantic layer that reflects business logic.

Focus areas:

• Business metrics and KPIs: Define precise logic (e.g., churn rate, monthly active users, CAC)

• Calculated fields and hierarchies: For drill-downs and roll-ups (e.g., Region > Country > City)

• Fact and dimension modeling: Star/snowflake schema design for BI tools

• Semantic layer tooling: dbt models, BI semantic layers (Power BI datasets, cube.dev, etc.)

📊 Well-defined models improve performance, reduce rework, and enable self-service analytics.

This layer becomes the bridge between raw data and decision-makers—the foundation for consistent, scalable reporting.

8. 📝 Document and Validate Requirements

Create a shared, living document or section that includes:

This should be ongoing activity during entire development lifecycle of the project and should begin even during initial requirements gathering and scoping discussions.

• Business goals and measurable KPIs

• Stakeholders and data owners

• Source systems and data elements

• Data Protection, Legal and Compliance Requirements

• Data Model Design and Entities (if applicable)

• Business Metrics, KPIs

• Report and Dashboard Wireframes (if known)

• Assumptions, risks, and validation steps

  
  

Who’s Involved: Full cross-functional team

🤝 Collaborate Across Roles

Analytics success requires collaboration, not handoffs.

This should be also ongoing throughout the active development of the usecase.

Role	Contribution
Business Owner	Defines goals, validates insights
Product Manager (optional for individual usecases)	Prioritizes, manages expectations
Data Analyst	Frames data questions, explores patterns
Data Engineer	Ensures access, builds pipelines
Architect	Designs scalable, governed data flows
Project Manager	Manages delivery and scope
Compliance/Legal/DPO	Validates data use and privacy alignment

Hold discovery workshops or cross-functional scoping sessions—not just ticket reviews.

⚠️ Common Pitfalls to Avoid

Pitfall	How to Avoid
Vague or shifting objectives	Document clear business goals
Assuming data exists	Validate early availability of data and/or time required to integrate data with Data Ingestion/Engineering teams responsible for Data Integration.
Building too much too soon	Start lean, iterate fast and have quick feedback loop cycles with business stakeholders, analysts, etc.
Poor stakeholder alignment	Communicate regularly, not just at milestones

✅ Best Practices

• Run a discovery session before starting analysis.

• Scope the Data requirements as far as possible to plan well for efforts, timelines, deliverables.

• Use agile, iterative cycles for delivery.

• Validate assumptions with sample data.

• Align definitions of KPIs and metrics early.

• Timely Feedback loop cycles with business.

✅ Conclusion

Scoping data requirements is the most important phase of any analytics project—but it’s often rushed or skipped. Whether you're a business stakeholder or data analyst, architect, data engineer or project manager - getting aligned on what problem you’re solving and what data supports it is the first step toward analytics that actually delivers value.

📬 Final Note

This blog is intended as a practical guide to help you approach data requirements gathering and scoping with clarity, structure, and purpose. While we've shared proven best practices, common pitfalls, and actionable steps, it's important to recognize that every organization and use case is unique.

The specific methods, tools, and level of detail required may vary based on:

• The nature and maturity of the project

• Your company’s data architecture and governance models

• The roles and responsibilities within your teams

Think of this guide as a foundation to enable better scoping and collaboration, not a one-size-fits-all checklist. Use it as a reference point to adapt and tailor your approach based on your goals, constraints, and environment.

If you'd like to discuss your specific challenges or explore collaboration around scoping and delivering analytics use cases within your organization, feel free to connect. We would be happy to share insights and support your journey toward more impactful analytics.