In this first episode of the Acceldata Engineering Podcast, Acceldata founders Ashwin Rajeeva (CTO) and Raghu Mitra (Co-founder & VP Engineering) sit down to unpack how AI is transforming Agentic Data Management (ADM) and modern data engineering.

They share real, first-hand insights on:
1. How AI fits into today’s data engineering and analytics stacks
2. The role of ADM in building reliable, scalable data platforms
3. Where abstraction layers help—and where they hurt
4. What founders think differently about when building data platforms with AI at the core
The future of AI-driven data observability, reliability, and operations

This conversation goes beyond buzzwords, diving into practical engineering decisions, architectural trade-offs, and real-world lessons from building Acceldata.

Whether you’re a data engineer, platform architect, analytics leader, or founder, this episode offers a grounded perspective on AI, ADM, and the evolving data ecosystem—straight from the brains behind the platform.

Watch now to hear how Acceldata’s founders think about building data platforms in the age of AI.

🚀 Data Forward: Building Reliable Data Systems in the Age of AI

In this episode, Joe Murphy, Principal Architect at Acceldata sits down with Jay Sivasamy, Principal, Enterprise Data & AI at Workday to explore how enterprises can build trustworthy, reliable data systems in today’s AI-driven world.

They dive into:

🔹 Evolving Frameworks: From ETL scripts to AI-ready pipelines

🔹 Trust & Governance: Making reliable data a business-critical asset

🔹 AI Integration: Using AI agents to maintain and improve system reliability

🎧 Tune in and learn how to future-proof your data systems: https://linktr.ee/dataforward

#DataForward #DataReliability #AI #EnterpriseData #DataGovernance #MachineLearning #DataEngineering

In this episode of Data Forward, host Amy Kyleen Lute talks with Mahesh Kumar, CMO at Acceldata, about the findings of the new AI Readiness Benchmark Report - based on responses from 150+ data leaders at large enterprises. Mahesh shares why AI readiness is critical, how organizations can measure their preparedness, and the risks when data foundations lag.

On this podcast you will learn:

-The key metrics to use to measure AI Readiness

-The contextual indicators that can help improve your key metrics

-How using AI to improve data operations can give organizations a competitive edge

In this episode of Data Forward, Ramon Chen, CPO of Acceldata talks with Matthew Cox, CDO at Fusable, about turning data into a true enterprise asset. They cover how Fusable unifies siloed data, ensures trust and quality with observability and lineage, and prepares for the next wave of AI innovation.

Key Takeaways

-Unify Data to Unlock Insights – How Fusable delivers value by creating a single, trusted data core.

-Trust & Quality First – Why lineage, observability, and governance are essential for reliable data.

-AI Readiness Starts with Data – Why high-quality, well-structured data is key for Gen AI and Agentic AI.

In this special episode of Data Forward, Vivek Singh sits down with Chandrakant “Chandra” Sharma — Acceldata’s Global Field CTO and VP of Field Engineering — for a candid, practical conversation on how the world’s largest enterprises are modernizing their data platforms.

Chandra shares proven, real-world strategies for:

• Simplifying complex, fragmented data stacks

  
  

• Avoiding vendor lock-in while controlling cloud costs

  
  

• Safely migrating off aging Hadoop platforms

  
  

• Building AI-ready, reliable platforms that deliver business value

Whether you’re a data leader, architect, or engineer, you’ll walk away with actionable lessons you can start applying immediately to improve agility, reduce costs, and future-proof your data platform.

In this episode, Ramon Chen sits down with industry veteran Dan Power to explore the evolution of data governance, quality, and management in the age of AI. From foundational lessons at Dun & Bradstreet to regulatory pressures in financial services, Dan shares what it takes to build trusted, scalable data practices—and where AI fits into the picture.

Key Takeaways:
- Data Products Need Discipline: Creating reliable data products requires manufacturing-like thinkingclear inputs, measurable outputs, and trust scores to drive business value.
- AI Is Not a Silver Bullet—But a Power Tool: GenAI and agentic systems can automate manual data work, but only if foundational data observability is in place.
- Regulations Demand Operational Readiness: Regulatory compliance increasingly depends on fast, accurate lineage, quality metrics, and the ability to prove control—not just documentation.

What does it actually take to prepare your enterprise data for the next wave of Agentic AI? In this special episode recorded live at Autonomous 25, Acceldata’s product marketing leader, Sonam Jain talks with Supratibh Srivastava, Principal Sales for ML Startups at AWS, about how companies are building a strong foundation for GenAI and agentic systems.

You’ll learn:
How data silos, poor governance, and unclear data strategy stall enterprise AI models

What agentic data management means for data engineering and platform teams

Why clean, observable, and policy-compliant data is critical to preventing hallucinations

The role of AWS Bedrock, SageMaker, and DataZone in enabling trusted, scalable AI

Whether you're building AI foundations, launching LLM-powered tools, or scaling GenAI infrastructure, you’ll walk away with clear next steps and practical inspiration.

In this episode of Data Forward, Acceldata CPO Ramon Chen chats with Bob Evans, founder of Cloud Wars, about how cloud giants are shifting from product-focused strategies to customer-led innovation. They explore the evolving role of data, AI, and cross-cloud partnerships—and what it means for the future of enterprise tech.

In this episode of Data Forward, Ashwin Rajeev (CTO & Co-Founder, Acceldata) talks with Adnan Khan, Managing Director of Data & AI Services (EMEA) at Accenture, about modernizing enterprise data in the age of AI.

Adnan shares insights from 20+ years at Accenture—exploring how regulated industries, legacy systems, and organizational culture shape innovation. From balancing foundational systems with AI adoption, to building trust through observability and lineage, this conversation offers real-world strategies for scaling data and AI initiatives.

Highlights:

The two-speed path to data modernization

Why observability and lineage matter more than ever

How regulation impacts AI adoption across industries

Accenture’s approach to driving agility without breaking trust