The Solution Options document helps you evaluate different approaches to solving a technology challenge. This document is essential when you need to present alternative solutions to stakeholders and gain approval to move forward with a formal buying process.

Rather than defaulting to purchasing new technology, this template helps you consider multiple approaches including:

Staying with the status quo (with improvements)
Customizing technology your organization already owns
Changing processes or applying people differently
Working with external consulting firms
Purchasing new technology solutions
Hybrid approaches combining multiple strategies

When to Use This Template

Use the Solution Options document when:

Senior leadership has requested a rigorous evaluation of different approaches
You need to justify technology spending against alternative solutions
You want to demonstrate thorough due diligence in the problem-solving process
Multiple stakeholders have different opinions about the best approach
You need to present options with clear, comparable criteria for decision-making

Template Structure

1. Executive Summary

A concise overview of:

The problem being addressed
Key solution options considered
High-level comparison of options
The recommended approach with brief justification

2. Problem Statement Review

A recap of the problem definition including:

Core business challenge
Current state and its limitations
Quantified impact of the problem (e.g., cost, time, quality issues)
Expected benefits of addressing the problem

3. Solution Options Analysis

For each viable option (typically 3-4 options), provide:

Description and Approach

Clear definition of the solution
How it addresses the core problem
Key components or requirements

Quantitative Analysis

Implementation costs
Ongoing operational costs
Expected ROI and payback period
Time to value

Qualitative Factors

User experience impacts
Strategic alignment
Organizational fit
Competitive advantages

Implementation Considerations

Timeline and major milestones
Change management needs
Training requirements

Resource Requirements

Internal staff commitments
External resources needed
Skills and expertise required

Dependencies

Systems or processes that must be in place
Organizational changes required
Other project dependencies

Risks and Mitigation Strategies

Key risks for each option
Probability and impact assessment
Mitigation approaches

4. Comparison Matrix

A color-coded table comparing all options across key decision criteria:

🟢 Green: Meets or exceeds requirements
🟡 Amber: Partially meets requirements or has notable concerns
🔴 Red: Does not meet requirements or has significant risks

Example criteria might include:

Cost (implementation and ongoing)
Implementation timeline
User adoption likelihood
Feature coverage
Integration complexity
Scalability
Support requirements
Strategic alignment

5. Recommendation and Rationale

A clear recommendation supported by:

Summary of why the recommended option is superior
Key decision factors that influenced the recommendation
Acknowledgment of trade-offs made
Next steps to move forward

Best Practices

Be comprehensive but concise: Include enough detail to support decision-making without overwhelming readers.
Use objective criteria: Ensure your comparison criteria are clear, measurable, and consistently applied across all options.
Include the status quo: Always include the option of maintaining current systems (potentially with improvements) as a baseline for comparison.
Consider hybrid approaches: Sometimes the best solution combines elements from multiple approaches.
Quantify where possible: Use data and metrics to support your analysis rather than relying solely on subjective assessments.
Acknowledge uncertainty: Where precise data isn’t available, provide ranges or estimates and note assumptions made.
Align with organizational priorities: Ensure your evaluation criteria reflect your organization’s strategic priorities and constraints.

Example Solution Categories

When developing your options, consider these common solution categories:

Internal Development Solutions

Pure internal development
Hybrid internal/external development
Internal capability enhancement

Technology Purchase Solutions

Off-the-shelf software
Customized vendor solutions
Platform-based approaches
Multi-vendor integration

Partner-Based Solutions

System integrator engagement
Consulting firm collaboration
Vendor professional services
Multiple partner coordination

Process/People Solutions

Workflow optimization
Team restructuring
Training and upskilling
Managed services

Hybrid Approaches

Combinations of above approaches
Phased implementation strategies
Pilot programs
Proof of concept approaches

Example: AI Data Preparation and Integration Solution

Below is an example of a Solution Options document for an enterprise looking to improve their AI data preparation and integration capabilities:

DRAFT: Solution Options for AI Data Integration and Preparation Platform

Executive Summary

This document evaluates four approaches to address Global Financial Corp’s data preparation and integration challenges that are currently limiting our AI implementation initiatives. Our analysis shows that:

Current manual data preparation methods are causing a 4-month backlog in AI projects
Data scientists spend 70% of their time on data preparation instead of model development
Three AI initiatives have failed to move beyond proof of concept due to data quality issues

We’ve evaluated four approaches: building an internal data platform, implementing a commercial data integration platform, engaging a managed service provider, and enhancing our current processes with specialized staff. Based on our analysis, we recommend implementing Databricks Lakehouse Platform as it provides the best balance of capabilities, implementation speed, and total cost of ownership while addressing our critical requirements.

Problem Statement Review

Global Financial Corp is struggling to scale AI initiatives due to inefficient data preparation and integration processes. Specifically:

Data resides in 16 different systems with inconsistent formats and quality
The current manual ETL process takes 3-6 weeks per data source
Data scientists lack self-service access to prepared data assets
Regulatory compliance requires detailed data lineage which is currently tracked in spreadsheets
Poor data quality has resulted in 3 failed AI pilots and $1.2M in wasted investment

Successfully addressing these challenges would:

Reduce data preparation time by 70%
Allow 5x more AI use cases to be deployed annually
Improve model accuracy by 30% through better data quality
Generate an estimated $4.7M in annual business value from accelerated AI adoption

Solution Options Analysis

Option 1: Build Custom Internal Data Platform

Description and Approach Build an in-house data lake and ETL framework using open-source technologies (Apache Spark, Airflow, and Delta Lake) on AWS infrastructure, with custom-developed data quality and governance tools.

Quantitative Analysis

Implementation costs: $2.2-2.8M
Annual operating costs: $850K
ROI: 70% over 3 years
Time to value: 12-18 months

Qualitative Factors

Complete customization to our specific needs
No vendor lock-in
Intellectual property ownership
Builds internal expertise

Implementation Considerations

Requires significant engineering resources
Phased deployment over 15 months
High complexity to build governance and lineage features

Resource Requirements

6 data engineers full-time for 12 months
2 data architects for solution design
1 project manager
3 DevOps engineers

Dependencies

Cloud infrastructure upgrades
New data governance policies
Specialized skill acquisition

Risks and Mitigation Strategies

Resource competition from other strategic projects
- Mitigation: Dedicated team with backfill for existing roles
Technical complexity exceeding initial estimates
- Mitigation: Agile development with regular deliverables
Knowledge concentration in few key engineers
- Mitigation: Documentation requirements and knowledge sharing

Option 2: Commercial Data Integration Platform (Databricks)

Description and Approach Implement Databricks Lakehouse Platform with Delta Lake, featuring pre-built connectors, automated data quality monitoring, and integrated ML capabilities to create a unified data environment.

Quantitative Analysis

Implementation costs: $1.2-1.5M
Annual licensing: $720K
ROI: 135% over 3 years
Time to value: 6-8 months

Qualitative Factors

Pre-built data quality and governance capabilities
Regular platform updates and improvements
Familiar interface for existing Spark users
Strong AI/ML integration features

Implementation Considerations

Phased implementation over 7 months
Connector development for legacy systems
Training for data engineering team

Resource Requirements

3 data engineers for 6 months
1 data architect
1 project manager
Databricks professional services

Dependencies

Cloud platform agreement amendments
Security approval for SaaS solution
Revised data governance framework

Risks and Mitigation Strategies

Vendor lock-in concerns
- Mitigation: Ensure data export capabilities and open formats
Integration with legacy systems
- Mitigation: Proof of concept with two most challenging systems
Cost management for scaling usage
- Mitigation: Implement consumption monitoring and governance

Option 3: Managed Data Service Provider

Description and Approach Engage Accenture’s Data Integration Managed Service to handle all data preparation and integration needs, with their team building and operating a custom solution on our behalf.

Quantitative Analysis

Implementation costs: $800K-1M
Annual service costs: $1.8M
ROI: 95% over 3 years
Time to value: 4-5 months

Qualitative Factors

Minimal internal resource requirements
Access to specialized expertise
Fast implementation
Flexible scaling

Implementation Considerations

Service transition over 4 months
Potential culture change for data teams
Clear SLAs and performance metrics required

Resource Requirements

1 vendor relationship manager
1 data architect for oversight
Internal SMEs for knowledge transfer (part-time)
Security and compliance oversight

Dependencies

Vendor security clearance
Data access policies
Knowledge transfer from current teams

Risks and Mitigation Strategies

Vendor performance not meeting expectations
- Mitigation: Detailed SLAs with penalties
Loss of internal knowledge and control
- Mitigation: Shadow team for knowledge retention
Data security concerns with external access
- Mitigation: Enhanced monitoring and access controls

Option 4: Enhanced Current Process with Specialized Staff

Description and Approach Maintain current systems but establish a dedicated data preparation team, implement better tools for existing processes, and develop standardized approaches without major architectural changes.

Quantitative Analysis

Implementation costs: $350K
Annual operating costs: $1.25M
ROI: 60% over 3 years
Time to value: 2-3 months

Qualitative Factors

Minimal disruption to current operations
No new technology to learn or integrate
Iterative improvement approach
Maintains current security model

Implementation Considerations

Team hiring and onboarding: 2-3 months
Process standardization: 1-2 months
Documentation development: 1 month

Resource Requirements

7 new data specialists
1 data preparation team manager
Process improvement consultant (3 months)
Training for existing teams

Dependencies

HR approval for new headcount
Process standardization acceptance
Department budget adjustments

Risks and Mitigation Strategies

Difficulty finding specialized talent
- Mitigation: Contract-to-hire approach
Limited scalability as AI initiatives grow
- Mitigation: Quarterly capacity planning
Process improvements insufficient for needs
- Mitigation: Establish objective milestones with fallback plan

Comparison Matrix

Criteria	Custom Solution	Databricks	Managed Service	Enhanced Process
Implementation Time	🔴	🟡	🟢	🟢
Total Cost	🟡	🟡	🔴	🟢
Data Quality Improvement	🟢	🟢	🟢	🟡
Scalability	🟢	🟢	🟢	🔴
Self-service Capability	🟢	🟢	🟡	🔴
Data Governance	🟡	🟢	🟢	🟡
Resource Requirements	🔴	🟡	🟢	🟡
Risk Level	🔴	🟡	🟡	🟢
AI/ML Integration	🟡	🟢	🟡	🔴
Security & Compliance	🟢	🟡	🟡	🟢

Recommendation and Rationale

We recommend proceeding with Option 2: Implementing the Databricks Lakehouse Platform for the following reasons:

Balanced approach: Provides the best combination of implementation speed, cost, and capabilities
Superior ROI: Highest projected 3-year ROI at 135%
AI/ML integration: Native features support our strategic AI initiatives
Time to value: Moderate implementation timeline with rapid initial results
Scalability: Can grow with our expanding AI program

While the managed service offers faster implementation and the custom solution provides more control, Databricks delivers the best overall value while addressing our critical requirements. The enhanced current process, while lowest risk, would not deliver the transformation needed to support our AI roadmap.

Next steps:

Initiate vendor discussions and detailed technical assessment
Develop implementation plan with milestones
Create resource allocation request for implementation team
Schedule executive briefing on recommended approach

Problem Statement - Define the challenge you’re trying to solve
Requirements Document - Document detailed requirements once a solution approach is selected
Vendor Selection Matrix - Compare specific vendors once you’ve decided to purchase technology

Requirements Document Template

Ready to document your requirements list? Use our Requirements Document template to clarify what you need this solution to do.

Templates

Solution Options Document

When to Use This Template

Template Structure

1. Executive Summary

2. Problem Statement Review

3. Solution Options Analysis

4. Comparison Matrix

5. Recommendation and Rationale

Best Practices

Example Solution Categories

Internal Development Solutions

Technology Purchase Solutions

Partner-Based Solutions

Process/People Solutions

Hybrid Approaches

Example: AI Data Preparation and Integration Solution

DRAFT: Solution Options for AI Data Integration and Preparation Platform

Executive Summary

Problem Statement Review

Solution Options Analysis

Option 1: Build Custom Internal Data Platform

Option 2: Commercial Data Integration Platform (Databricks)

Option 3: Managed Data Service Provider

Option 4: Enhanced Current Process with Specialized Staff

Comparison Matrix

Recommendation and Rationale

Requirements Document Template

Templates

​When to Use This Template

​Template Structure

​1. Executive Summary

​2. Problem Statement Review

​3. Solution Options Analysis

​4. Comparison Matrix

​5. Recommendation and Rationale

​Best Practices

​Example Solution Categories

​Internal Development Solutions

​Technology Purchase Solutions

​Partner-Based Solutions

​Process/People Solutions

​Hybrid Approaches

​Example: AI Data Preparation and Integration Solution

​Related Templates

Requirements Document Template

When to Use This Template

Template Structure

1. Executive Summary

2. Problem Statement Review

3. Solution Options Analysis

4. Comparison Matrix

5. Recommendation and Rationale

Best Practices

Example Solution Categories

Internal Development Solutions

Technology Purchase Solutions

Partner-Based Solutions

Process/People Solutions

Hybrid Approaches

Example: AI Data Preparation and Integration Solution

Related Templates