Financial Services Data-to-Intelligence Assessment on AWS
Compass UOL · Finance & Accounting
Certification per AWS Marketplace.
Evidence tier Source Confirmed · 4 captures on record
What the publisher says
As described on AWS Marketplace.
Financial institutions face increasing pressure to turn large volumes of data into auditable, timely, and actionable decisions across risk, compliance, fraud prevention, and customer intelligence. However, fragmented data environments, legacy architectures, and inconsistent governance models delay critical initiatives and increase operational costs.
The Financial Services Data-to-Intelligence Assessment on AWS by Compass UOL provides a structured approach to evaluate the current data landscape, identify technical and regulatory gaps, and define a prioritized roadmap for modernization on AWS.
Show the rest of the publisher’s description (32 more lines)
This assessment evaluates data pipelines, governance frameworks, security controls, and analytical workloads, aligning them with AWS best practices for scalable and regulated environments. It also identifies opportunities to adopt modern architectures such as data lakes and lakehouses using services like Amazon S3, AWS Glue, Amazon Redshift, and Amazon Kinesis.
Additionally, the assessment explores readiness for advanced analytics and AI/GenAI use cases using Amazon Bedrock, ensuring that financial data can be used securely and in alignment with compliance requirements.
The outcome is a clear, prioritized roadmap that reduces regulatory risk, improves operational efficiency, and enables faster, data-driven decision-making.
Buyer Problem / Business Trigger
Data initiatives delayed due to legacy systems or fragmented data sources
Regulatory pressure (e.g., BCBS 239, IFRS, local compliance) with limited data governance maturity
High operational costs in inefficient or duplicated data pipelines
Difficulty operationalizing analytics and AI in production environments
Need to monetize data or improve customer intelligence capabilities
Delivery Model
Current state discovery across data, architecture, and governance
Stakeholder workshops (risk, compliance, IT, data teams)
AWS-aligned technical and regulatory assessment
Roadmap definition with prioritized initiatives and target architecture
Assessment / Engagement Scope
Inventory of data sources, pipelines, and analytical workloads
Data governance, quality, and catalog maturity assessment
Evaluation of current architecture against AWS best practices
Identification of priority use cases (fraud detection, credit analytics, customer insights)
Security, privacy, and regulatory compliance review
AI/GenAI readiness assessment for regulated environments
Expected Output / Deliverables
Data-to-Intelligence maturity assessment report
Target AWS architecture (high-level and recommended patterns)
Prioritized roadmap (quick wins vs. strategic initiatives)
Business impact estimation (cost, risk reduction, operational efficiency)
Customer Decision Questions
This offer helps the customer answer:
Does our current data architecture support near real-time and auditable decision-making?
Which data initiatives deliver the fastest business impact with controlled risk?
How can we align data governance with regulatory requirements on AWS?
Are we ready to safely adopt AI/GenAI using our internal data?
Highlights
Highlighted by the publisher on AWS Marketplace.
Fast insight generation, GenAI enabled access, AWS-native architecture, Production-ready
Agent build and provenance
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