Data Platform PoV
Data & AI
Motivation
Your data platform is the backbone for real-time insights, operational excellence, and innovation. As organizations increasingly rely on advanced analytics and AI, investing in a full-scale platform—whether built on technologies like Databricks or Microsoft Fabric—can still be risky without validating its technical feasibility and business value. With our Data Platform PoV, we help de-risk your decision by demonstrating, in a short, agile engagement, how a modern, scalable data platform can integrate with your existing systems, support high-performance analytics, and drive tangible business outcomes. By showcasing tangible results and meeting performance, cost, and compliance requirements early on, you gain the confidence to proceed with a robust data platform that drives meaningful results for your business.
What we bring
PRODYNA has over 20 years of experience in designing and implementing data and software architectures for some of the world’s largest enterprises. We leverage this expertise to:
- Rapidly Prototype with Real Data: Demonstrate the value of a modern data platform in just a few weeks.
- Adopt Cloud-Native Services: Whether on Azure, AWS, or GCP, we design architectures that take advantage of cloud scalability and services.
- Excel in Data Engineering & Governance: Establish secure, reliable data ingestion, storage, and transformation processes, aligned with best practice governance principles.
- Deliver Cost-Effective Solutions: Focus on delivering the highest value first, with a clear roadmap for scaling.
- Enable AI and Analytics: Ensure your data platform can serve advanced analytics and data science use cases.
What you need
To get the most out of a Data Platform PoV, your organization should provide:
- Clear Business Objectives: Clearly defined goals and success criteria the PoV should achieve (e.g., speed to insight, cost optimization, integration of analytics and AI, collaboration between data experts and business users).
- Involvement of business specialists and technical staff: These people can explain where and how to obtain the relevant data and describe its structure, known quality aspects and regulatory obligations.
- Use-Case Scenarios: Selection of high-impact use cases that best showcase your desired capabilities—be it real-time analytics, data consolidation, or advanced reporting.
- Infrastructure Insights: An overview of your existing technology landscape, including current platforms, integrations, and security standards.
What you get
Our Data Platform PoV engagement follows a streamlined, three-phase approach. We tailor the process to your organization’s unique needs, ensuring alignment with your business goals and technical landscape.
Workshop
(2 days)
Kick off & Alignment: Define the scope, success criteria, and business goals of the PoV.
Technical & Data Assessment: Understand your data sources, infrastructure, governance requirements, and integration points.
Service & Platform Overview:
- Explore and compare modern data platforms (e.g., Databricks, Microsoft Fabric) and their core functionalities.
- Discuss how each platform might integrate with your organization’s current systems, data, and security requirements.
- Outline the approach to introducing the chosen platform to your teams, emphasizing key benefits, stakeholder readiness and success factors.
Use Case Definition: Pinpoint specific scenarios or business problems that should be realized first.
Architecture: Develop a high-level architecture to visualize the required network and service connections.
Roadmap: Develop a roadmap detailing the steps needed to achieve the data requirements necessary for your use case.
Environment & Service Setup
(2 to 3 weeks)
Provisioning of the Service: Provision and configure the environment and establish secure network configurations (firewalls, VPNs, Virtual Networks) that enable seamless communication and data flow between the data platform and existing systems.
Core Architecture Implementation: Establish foundational components for data ingestion, storage, and transformation, incorporating best-practice governance and security configurations.
Governance & Compliance Alignment: Incorporate enterprise-level policies, permissions, and usage monitoring to ensure data security, regulatory compliance, and cost management.
Tooling & Automation: Implement CI/CD pipelines and DevOps best practices for streamlined workflow and faster iteration.
High impact Use Case Implementation
(2 to 3 weeks)
Data Ingestion & Transformation: Ingest your real or representative data into the newly set up environment, applying quality checks and transformations tailored to the defined use cases.
Analytics & Insights: Implement targeted analytics or AI pipelines (batch or real-time) to showcase the performance and scalability of the solution.
Visualizations & Reporting: Develop dashboards and reports to deliver immediate insights, enabling stakeholders to understand the platform’s potential ROI.
Demonstration & Knowledge Transfer: Present a working prototype, summarize key lessons, and provide training or documentation to empower your teams for ongoing or future expansions.
Quick facts
- Effort: 4 to 7 weeks
- High impact Deliverables: Receive a tangible prototype-complete with data ingestion, analytics pipelines, and initial dashboards-tailored to your use cases.
Benefits
- Accelerated Time-to Value: Demonstrate a functioning data platform within weeks, reducing risk and speeding up ROI.
- Proven Best Practices: Leverage 20+ years of data architecture expertise, ensuring robust governance, compliance, and scalability from the outset.
- Comprehensive Overview: Gain clarity on platform capabilities, architecture design, and critical success factors, laying the groundwork for broader AI or advanced analytics initiatives.
Contact us
for more information.
David Wainwright
