dyvenia helps enterprises align and validate their metrics so leaders can trust their numbers, scale analytics, and deploy AI with confidence.
Schedule a discovery callData exists across multiple systems, teams, and processes, but it is rarely structured in a way that preserves its business meaning, breaking trust.
* by working backwards from the decisions leaders need to make, we design data models, pipelines, and platforms that ensure metrics are aligned, traceable, and scalable across systems.
We help organizations move from disconnected systems and conflicting reports to a shared, reliable foundation for decision-making.
Finance, operations, and sales rely on the same definitions, eliminating debates and reconciliation.
Every number can be explained back to its source, making analytics auditable and reliable.
Curated facts and dimensions ensure AI pilots produce consistent, explainable outputs.
Teams spend less time validating data and more time acting on insights.
Data models and pipelines grow with your organization without breaking consistency or performance.
We are data and performance management specialists with backgrounds in finance, operations, and analytics.
We have worked in the same environments our clients operate in, where metrics don’t align, data comes from multiple systems, and reporting requires constant validation.
We focus on building data foundations that reflect real business processes, so the numbers leaders rely on are consistent, explainable, and actionable.
We believe AI is a competitive advantage only if it’s built on reliable data and a clear business context. That’s what we deliver.
An experienced financial analyst and software engineer, Alessio brings a unique blend of expertise and vision to the world of data, ensuring that our clients unlock the hidden connections in their data and deliver value to their stakeholders.
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15+ years of experience in finance roles, as well as in manufacturing and services for multinational corporations. Expertise in controlling FP&A, Commercial Finance, Operations Finance, and Internal Audit.
Karol builds secure, cloud-agnostic data platforms at scale. With deep DevOps expertise, he unifies data sources, automates infrastructure, and streamlines hybrid operations.
Mateusz designs modular, future-proof data infrastructures with strong observability and operational excellence, ensuring they stay reliable, scalable, and aligned with evolving business needs.
Enterprises are drowning in data, but struggling to turn it into performance.
We fix the foundations so your data and AI deliver measurable impact:
Your team’s fastest route to insights. Ask questions in plain language and get instant answers:
Selected Articles. Check our blog for more.
Structuring data for BI is a key decision that impacts performance, scalability, and data consistency. This article compares flat tables and semantic models, highlighting the strengths and trade-offs of each. Learn how a hybrid approach can offer the best of both worlds—combining consistency, flexibility, and efficient analytics across tools and teams.
Accurate CO₂ emissions reporting is vital for meeting sustainability goals and regulatory requirements. This article delves into the challenges of Scope 3 emissions, the importance of clean data, and how structured data systems like sustainability marts can improve reporting, ensure compliance, and support better decision-making for businesses.
Discover the role of master data management (MDM) in achieving consistent and accurate business metrics. This article explains the concept of master data, outlines key challenges organizations face, and introduces two accessible approaches to MDM. By focusing on practical steps and avoiding common pitfalls, we show how businesses can enhance data quality without large budgets or complex systems.
Data marts simplify access to meaningful business metrics, helping leaders drive performance improvements with clarity and precision. This article breaks down what a data mart is, how it functions, and what makes a metric truly effective. Explore how data marts consolidate information from multiple sources to provide actionable insights, avoid common data pitfalls, and enable smarter decision-making.