Create a structured analysis of different approaches to solve your technology challenges
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:
Use the Solution Options document when:
A concise overview of:
A recap of the problem definition including:
For each viable option (typically 3-4 options), provide:
Description and Approach
Quantitative Analysis
Qualitative Factors
Implementation Considerations
Resource Requirements
Dependencies
Risks and Mitigation Strategies
A color-coded table comparing all options across key decision criteria:
Example criteria might include:
A clear recommendation supported by:
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.
When developing your options, consider these common solution categories:
Below is an example of a Solution Options document for an enterprise looking to improve their AI data preparation and integration capabilities:
AI Data Solution Options Document
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:
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.
Global Financial Corp is struggling to scale AI initiatives due to inefficient data preparation and integration processes. Specifically:
Successfully addressing these challenges would:
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
Qualitative Factors
Implementation Considerations
Resource Requirements
Dependencies
Risks and Mitigation Strategies
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
Qualitative Factors
Implementation Considerations
Resource Requirements
Dependencies
Risks and Mitigation Strategies
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
Qualitative Factors
Implementation Considerations
Resource Requirements
Dependencies
Risks and Mitigation Strategies
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
Qualitative Factors
Implementation Considerations
Resource Requirements
Dependencies
Risks and Mitigation Strategies
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 | 🟢 | 🟡 | 🟡 | 🟢 |
We recommend proceeding with Option 2: Implementing the Databricks Lakehouse Platform for the following reasons:
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:
Ready to document your requirements list? Use our Requirements Document template to clarify what you need this solution to do.