The Data Shift: How Strong Data Foundations Drive Smarter AI Outcomes
What does it really take for an enterprise to succeed with AI? In this first episode of The Data Shift Podcast, Rajesh Indurthi, CTO at Charter Global, and Nevarda Smith, CTO and Chief AI Officer at MagMutual, decode the real meaning of being “data-ready” in the new age of intelligent enterprises.
They explore how organizations can move beyond scattered data and siloed systems to build a unified, trusted, and scalable foundation for AI success. From modern data architecture and governance frameworks to leadership accountability and ethical innovation, this discussion offers a masterclass in making AI practical, sustainable, and transformative.
Across four power-packed segments, you’ll learn how to:
- Break down data silos to leverage enterprise-wide visibility and trust.
- Build a unified data and AI strategy aligned across people, process, and technology.
- Drive responsible AI adoption with strong governance and leadership buy-in.
- Empower teams to innovate confidently with transparent, traceable data pipelines.
Whether you’re a data leader, technologist, or business executive, this episode reveals what truly differentiates AI-ready enterprises from those still experimenting. It’s not about having more tools, but having the right data, the right people, and the right mindset.
Let’s get started with the root of every AI transformation: the state of your data. Because before innovation can scale, silos must fall and structure must take shape.
Section 1: Breaking Down Data Readiness, From Silos to Structure
Rajesh Indurthi: Welcome to The Data Shift Podcast! Today we’re diving into one of the biggest challenges shaping the future of AI adoption: data readiness. Before any organization can leverage AI or machine learning, it needs a strong data foundation. And to unpack that, I’m joined by Nevarda Smith, CTO and Chief AI Officer at MagMutual.
Nevarda Smith: Thanks, Raj. You’re right, everyone wants to jump to AI, but few are truly ready. Data readiness is not just a technical issue; it’s about how well an organization understands, manages, and governs its data. The biggest barrier we see? Silos.
Understanding Data Silos and Their Impact
Nevarda Smith: Think of silos as locked filing cabinets, each department has one with its own data format and rules. Underwriting, claims, finance, and marketing all make decisions with incomplete views, which slows down innovation and creates blind spots. When compliance teams can’t trace who accessed data or verify its lineage, that becomes a governance risk, especially in regulated industries.
Rajesh Indurthi: Exactly. At Charter Global, we always start our data discovery with a silo assessment. It reveals duplication, loss, and inconsistencies that make enterprise data unreliable. Without that visibility, any AI or analytics effort rests on unstable ground.
Nevarda Smith: That’s why breaking down silos should be seen as a strategic move, not an IT clean-up project. Once data flows freely, innovation accelerates, decision-making improves, and compliance strengthens.
Key Points Discussed:
• Data silos fragment visibility and degrade trust.
• They slow time-to-insight and increase governance risk.
• Breaking silos is the first step toward AI readiness.
Learning Takeaway: Unifying data across the enterprise creates the foundation for scalability and compliance. Without shared visibility and traceable lineage, AI initiatives can’t sustain growth.
The Medallion Architecture: Bronze, Silver, and Gold Layers
Rajesh Indurthi: A lot of modern enterprises use the medallion architecture: bronze, silver, and gold layers to structure their data. Why has it become the standard?
Nevarda Smith: Because it modernizes how we think about data flow. Instead of dumping everything into a single warehouse, it creates stages of trust and quality:
- Bronze Layer: Raw, unaltered data from all sources. It keeps authenticity and lineage intact.
- Silver Layer: The refinement zone, where data is cleaned, validated, and standardized under governance rules.
- Gold Layer: The analytics and AI layer, where models are built and decisions are driven.
As data moves from bronze to gold, it matures by gaining structure, reliability, and traceability.
Rajesh Indurthi: And at the gold layer, data finally connects to business value. The real question becomes: What are we trying to achieve?
Nevarda Smith: Exactly. I always ask two things before any build:
- What strategic goal are we chasing?
- How will we measure success?
If those answers aren’t clear, even great data engineering won’t deliver business impact.
Key Points Discussed:
• The medallion model ensures governance and traceability across every stage.
• Each layer represents progress in data quality and relevance.
• Business alignment turns data from raw material into measurable intelligence.
Learning Takeaway: The medallion architecture acts as both structure and strategy. It transforms data from chaos into clarity, creating a reliable path from ingestion to insight.
Normalization, Governance, and Ethical Data Practices
Rajesh Indurthi: Once structure is in place, normalization keeps everything consistent. How do you define normalization in today’s AI-driven times?
Nevarda Smith: Normalization now means much more than database design, it’s about fairness, accuracy, and interoperability. Unnormalized data can bias models or skew predictions. Standardized data ensures that all AI systems interpret variables consistently, making outcomes comparable and explainable.
It also supports scalability. Once normalized, a single dataset can serve multiple AI use cases: claims, fraud detection, or customer insights, without rework. And because normalization tracks how data changes, it reinforces auditability and ethics.
Rajesh Indurthi: Exactly. Without normalization, you lose your single source of truth. Data duplication and inconsistent metrics follow, breaking analytics and eroding trust.
Key Points Discussed:
- Normalization ensures fairness, consistency, and interoperability.
- It strengthens governance and explainability for audits.
- Lack of normalization leads to bias and unreliable analytics.
Learning Takeaway: Normalization transforms raw data into trusted intelligence. It’s the core of explainable, ethical AI ensuring every automated decision is transparent and verifiable.
Section 2: Data and AI Strategy, Maturity, and C-Level Roles
Rajesh Indurthi: Data has become the heartbeat of every enterprise. It shapes ROI, operations, and compliance, yet without structured, governed, and high-quality data, decision-making weakens. As organizations scale AI initiatives, one question always arises: What does a mature data and AI roadmap look like, and how can it evolve sustainably across people, process, and technology?
Building a Unified Data and AI Strategy
Nevarda Smith: The foundation of any strong AI strategy is alignment. Many enterprises rush into model building before establishing a unified data framework. A well-designed strategy integrates data management, governance, and AI under one structure.
How to begin:
- Start with a Maturity Assessment: Identify all data sources, including spreadsheets and local files. Shadow data often drives hidden business logic.
- Capture Tacit Knowledge: Document the manual tweaks employees make to datasets and formalize them into structured rules.
- Eliminate Ungoverned Pockets: Integrate departmental data into governed systems to avoid duplication and compliance risk.
- Involve Business Users Early: The way analysts interpret data often differs from IT assumptions; embedding their insights ensures adoption.
Rajesh Indurthi: That human context is vital. We’ve seen companies invest millions in data lakes, only to find no one knows how that data was used before. Strategy without alignment is just technology on paper.
People, Process, and Technology Framework
Rajesh Indurthi: Transformation always rests on three pillars:
- People: Assess skills realistically and upskill as needed. Choose tools that fit team capabilities rather than market trends.
- Process: Design workflows that match your organization’s scale and maturity. Start small, focus on early wins, and build momentum.
- Technology: It should support and extend strategy, not dictate it. The right tech aligns with company culture and enables adoption.
Nevarda Smith: Absolutely. Technology decisions should come last, not first. Without change management and buy-in, even the best platforms will underdeliver.
The Data Maturity Model: Evolving Responsibly
Nevarda Smith: To measure progress, I use a maturity model that defines how organizations evolve:
- Experimental: Isolated analytics and ML pilots, minimal governance.
- Operational: Centralized data and emerging governance practices.
- Strategic: Governance solidifies, use cases map directly to ROI.
- Transformational: Data and AI embedded enterprise-wide, supported by strong governance and continuous improvement.
Transformation doesn’t stop at deployment. Continuous monitoring and retraining are essential. AI is a living system that must evolve with the business.
Rajesh Indurthi: Totally. Start small, measure success, and scale what works. One department’s success story creates momentum for enterprise-wide adoption.
The Critical Role of C-Level Leadership
Nevarda Smith: C-level leaders define how fast and responsibly data transformation happens:
- CEOs: Champion the AI vision and link it to mission, ethics, and compliance.
- CFOs: Track ROI and ensure long-term financial sustainability.
- CISOs and CSOs: Protect data integrity and uphold ethical AI practices.
- CHROs and COOs: Lead change management and workforce enablement.
Visible leadership matters. When executives attend demos, celebrate wins, and back innovation teams, it signals that transformation is everyone’s responsibility.
Rajesh Indurthi: Exactly. Executive engagement turns strategy into sustained progress. Teams push harder when they know leadership is invested in their success.
Key Points Discussed:
- Data governance and AI strategy must evolve together under one framework.
- Transformation succeeds through alignment of people, process, and technology.
- A maturity model enables responsible, trackable scaling.
- C-level accountability drives cultural adoption and governance maturity.
Learning Takeaway: Sustainable AI transformation is built on structure, not shortcuts. A unified strategy that aligns governance, technology, and people creates measurable ROI and long-term resilience. When leadership models accountability and collaboration, data becomes the engine of innovation.
Section 3: Ethics, Innovation, and Empowerment through AI
Rajesh Indurthi: It’s fascinating how every C-level leader: CFOs, CIOs, CHROs, Chief Data Officers plays a unique role in responsible AI adoption. From governance to human oversight, leadership accountability is vital. AI cannot exist in isolation; it must reflect an enterprise culture grounded in ethics and purpose.
Nevarda Smith: Exactly. Every executive owns part of ethical AI implementation because AI touches all areas—finance, operations, people, and compliance.
- Chief Security Officer (CSO): Ensures secure architectures and prevents vulnerabilities, data leakage, or model poisoning.
- Chief HR Officer (CHRO): Monitors fairness and transparency in talent management, ensuring AI tools do not amplify bias.
- Chief AI/Data Officer (CAIO/CDO): Establishes data usage and governance policies that uphold ethical and legal standards.
- Chief Legal Officer (CLO): Tracks evolving global regulations from AI oversight laws to restrictions on automated claim denials and ensures humans remain accountable.
The “human in the loop” principle must always guide AI. Automation can accelerate outcomes, but people must remain responsible for fairness, explainability, and compliance.
Rajesh Indurthi: That’s a great point. At Charter Global and (my previous company) Equifax, we’ve seen how security, IP protection, and compliance shape every technology choice. What’s remarkable today is how Generative AI has democratized innovation. Product managers, analysts, and designers can now turn ideas into prototypes within hours, what we like to call “wipe coding.” This shift empowers anyone with creativity to contribute meaningfully.
Nevarda Smith: Truly, and that empowerment must be intentional. Access alone isn’t enough; enterprises need a framework that balances innovation with responsibility.
Four pillars enable this:
- Community of Practice: Encourage knowledge-sharing and celebrate creative use cases to promote learning.
- Center of Excellence (CoE): Formalize governance and vet tools for security, compliance, and reliability.
- Structured Intake Process: Evaluate new AI tools systematically to prevent vendor sprawl.
- Strategic Partnerships: Work with trusted frameworks that embed compliance and scalability from the start.
Rajesh Indurthi: That’s exactly why we built OrcaWorks at Charter Global, which is a secure, agentic AI framework that integrates governance, compliance, and innovation. It enables engineers to focus on building enterprise-grade AI solutions without worrying about risk or regulation.
Nevarda Smith: That’s a powerful model. It gives engineers and product teams freedom to innovate within secure and ethical boundaries, which is an essential foundation for sustainable scale.
Rajesh Indurthi: So if you had a blank check to prepare your enterprise for the future, where would you invest first?
Nevarda Smith: In talent. Technology changes rapidly, but people sustain progress. I’d prioritize hiring and upskilling adaptable, creative technologists who can evolve with new platforms and paradigms. Tools come and go, but skilled people drive lasting transformation.
Key Points Discussed
- Ethical AI requires shared C-level accountability and collaboration.
- Human oversight ensures transparency and fairness in automation.
- Communities of Practice can evolve into Centers of Excellence for structured governance.
- Strategic intake processes prevent tool overload and vendor chaos.
- OrcaWorks demonstrates how governance and innovation can coexist securely.
- Investing in adaptable, skilled talent is critical for long-term AI success.
Learning Takeaway: Responsible AI adoption is a cultural shift as much as a technological one. It depends on ethical leadership, transparent governance, and people empowered to innovate within secure frameworks. So organizations that pair creativity with compliance will shape the next generation of AI-driven transformation.
Section 4: Empowering Thinkers, Data Lineage, and Choosing the Right Tools
Rajesh Indurthi: We’ve talked about this before, you work with young engineers from Georgia Tech and other top universities, and I tell my own team the same thing: the era of working in silos is over.
Today, engineers must understand why they’re building something and how their component fits into the larger system and supports the company’s vision. It’s not about just closing tickets; it’s about designing systems that connect people, processes, and purpose.
Nevarda Smith: Agreed. The hardest part isn’t technology, it’s developing thinkers. You need engineers who can look at a problem end-to-end, not just execute a checklist.
When people understand the mission and see how their work impacts the business, creativity thrives. Clear governance, ethics, and legal frameworks give them confidence to innovate responsibly. That’s when production accelerates, not from tools, but from empowered people.
Rajesh Indurthi: Absolutely. Investment should go into people who can think in systems. With a few great engineers who understand context, you can build incredible solutions. For anyone studying AI or ML, remember: the big picture matters more than the algorithm.
Data Lineage: Trust, Traceability, and Transparency
Rajesh Indurthi: Let’s talk about data lineage. It’s one of those terms that sounds technical but holds massive strategic value. Simply put, it’s about knowing where your data comes from, how it flows, and how it changes.
Nevarda Smith: Exactly. Data lineage is the backbone of governance, it brings clarity and accountability. When you can trace data across systems, you gain:
- Trust: Teams know where the data came from and can rely on it.
- Audit readiness: You can explain exactly how a model or report reached its conclusion.
- Scalability: Transparent pipelines make AI easier to expand across use cases.
Without lineage, you’re operating in a black box. You can’t govern what you can’t trace.
Rajesh Indurthi: I’ve experienced that firsthand at Equifax. During our migration from legacy systems to a modern data lakehouse, we couldn’t move a single dataset until we understood its lineage. Knowing where it came from, how it was transformed, and how it reached the gold layer gave us confidence and compliance. Once lineage was clear, governance, auditability, and data reliability improved dramatically.
Tooling and Frameworks: Strategy Before Shiny Objects
Rajesh Indurthi: Now, let’s talk tools. With the explosion of AI and data platforms, how can enterprises choose wisely?
Nevarda Smith: We use a use-case-first approach. The tool must serve the business problem, not the other way around. Our framework looks like this:
- Start with real business use cases that drive measurable outcomes.
- Identify common features and overlaps across use cases.
- Define success metrics (both short and long term).
- Run a market scan (often with partners like Gartner) to shortlist options.
- Build a heat map to compare functionality, scalability, interoperability, and governance.
- Run POCs to validate real-world performance before investing.
We also assess skill availability and ease of integration. The best tool isn’t the flashiest, rather, it’s the one that fits your ecosystem and your team’s capability.
Rajesh Indurthi: That’s exactly how we operate at Charter Global. We’re tool-agnostic by design, focused on people, process, and outcomes.
When evaluating technology, we use a weighted scoring model that measures:
- ROI and value realization
- Security and compliance posture
- Skill alignment and maintainability
- Cost efficiency and scalability
If two tools perform equally, we let the team and process fit decide. Because technology only succeeds when people can adapt to it.
Nevarda Smith: Totally. The right balance of discipline and flexibility prevents “tool chaos.” Chasing every new trend leads to fragmentation. A coherent, strategy-first ecosystem is what enables sustainable innovation.
Rajesh Indurthi: That’s a great note to wrap up on. Before we close, let’s shift gears for a moment and have some fun with a quick rapid-fire round.
Nevarda Smith: Let’s do it!
The Rapid Fire
1. What’s the most surprising thing business leaders misunderstand about AI?
That AI can do everything and it’s really easy to implement. The aspects of governance, guardrails, controls from a legal aspect, data readiness, all have to be considered. It’s not just dropping in an AI tool.
2. Data lineage or governance – which one do you think most companies ignore mostly?
The governance aspect of it because it comes in over the top after you’ve already put all the bad procedures in place.
3. What’s the most overhyped phrase in the data world today, according to you?
“AI will take my job.” In reality, someone using AI effectively will.
4. What’s your everyday AI app that you can’t live without?
From a business perspective, Dataiku is the number one tool that we’re using right now as it enables business to build their own stuff, under the guidance of a data scientist.
5. If AI were a person, what kind of personality would it have?
Helpful, supportive, and calm in a crisis.
6. What’s the funniest or the most unexpected way you’ve seen AI been used?
Recipe generation. Chocolate chicken cheesecake with gherkins!
7. If data were food, what do you think it would be?
A very spicy Mexican dish.
8. If you could give AI one superpower, what would it be?
It would be to replace me on meetings, answer as me so I can go and do the cool stuff.
Rajesh Indurthi: That was fun! Before we wrap up do you have a question for me?
Nevarda Smith: What’s the biggest mindset shift for leaders in data and AI?
Rajesh Indurthi: Build fast and fail fast. Bring your vision to market quickly and have the courage to experiment.
Nevarda Smith: That’s the essence of transformation. Progress over perfection!
Key Points Discussed
- Empower engineers to think systemically rather than in silos.
- Data lineage builds trust, explainability, and audit readiness.
- Governance and lineage enable scalable, ethical AI.
- Tool selection should follow a structured, strategy-first framework.
- Charter Global’s ROI-driven, tool-agnostic approach ensures adaptability.
- Governance, ethics, and people-centric innovation drive real transformation.
- Leaders must adopt a “build fast, fail fast” mindset to accelerate progress.
Learning Takeaway: Data-driven transformation is powered by people, not tools. The true differentiator lies in cultivating adaptable talent, maintaining data integrity through lineage and governance, and fostering a culture where experimentation leads to innovation.
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