Suhas Nikam

Data architect with over 20 years of experience in data and analytics, specializing in modernizing global data ecosystems. Successfully led initiatives that enhanced data security and scalability, resulting in millions in savings. Proven expertise in corporate governance and cloud platforms, with a strong focus on implementing large-scale data strategies that drive operational efficiency. Results-oriented director with a solid track record in financial oversight and strategic business execution, leveraging market trends to support informed leadership.

• Enterprise data
• Regulatory compliance
• Policy development
• Cloud Strategy
• Analytical Decision Making
• Platforms/Innovation
• Global Data management
• Problem Solving
• Operational Excellence
• Strategic planning
• Data engineering
• Senior level leadership engagement and partnership
• Team Building
• Driving Transformation
• Team mentoring
• Building effective teams/hiring

Suhas Nikam

Director Global Head of Data & Advanced Analytics Enterprise Architecture Plainsboro, NJ x917-856-3292 snikam@its.jnj.com https: www.linkedin.om/in/suhas-nikam-761b706

P R O F E S I O N A L P R O F I L E

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Enterprise data, analytics, and AI architecture executive with 25+ years of experience defining and leading firm‑wide data architecture strategies for large, highly regulated global organizations. Proven track record of architecting modern cloud data platforms, enabling AI/ML and GenAI at scale, and establishing enterprise architecture governance aligned to business outcomes.

Deep expertise in developing 3–5-year data and AI roadmaps, leading senior architecture organizations, and partnering with CDO, CTO, and business executives to translate complex architectural strategies into measurable business value. Known for balancing innovation, risk, cost, and scalability while building AI‑ready, self‑service data foundations across federated enterprises.

I have led enterprise‑wide data modernization and architecture initiatives, transforming complex data landscapes to enable advanced analytics and data‑driven decision‑making. I bring strong expertise in cloud strategy and strategic planning, delivering scalable, future‑ready platforms through close collaboration between business and technology teams.

• Enterprise Data & Analytics Architecture
• Cloud Data Platforms (AWS, GCP, Snowflake, Databricks)
• AI / ML & GenAI Data Readiness
• Data Governance & Architecture Standards data quality
• 3–5 Year Strategy & Roadmap Development
• Executive Stakeholder Influence
• Data Mesh & Data Marketplace Architectures
• Large‑Scale Team & Talent Leadership

E X P E R I E N C E

DIRECTOR DATA AND ADVANCED ANALYTICS ARCHITECTURE | Johnson & Johnson 07/2016 - Current

I was hired to lead define next generation data architecture strategy architecture efforts to define multiyear strategy for global business units in a highly regulated environment.

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I pioneered multi Year strategy next generation data architecture strategy the industry‑first migration of Janssen’s commercial data ecosystem to AWS and modernized analytics by consolidating platforms and insourcing IP—cutting costs by $12M and reducing time‑to‑insight from weeks to a day. Defined and executed a multi‑year enterprise data and analytics architecture strategy, modernizing legacy platforms into cloud‑first, AI‑ready ecosystems.

· Served as senior architectural partner to the Chief tech Officer and technology executives, influencing enterprise investment decisions and platform direction.

Key Achievements: in j Johnson & Johnson

NextGen Data & Analytics (DnA) Strategy Development & Execution:

· Enterprise architect is responsible for defining and executing firm‑wide data, analytics, and AI architecture strategy across global business units in a highly regulated environment.

· Employed modern data architectures such as cloud-first infrastructure, Data Mesh, Secure Data Fabric, AI/GenAI at scale, and Federated

Data Governance. Delivered a significant $10M YOY P&L impact, driving business transformation and data innovation.

· AI‑Driven & Insight Automation

· Enable business users to explore data independently using governed self‑service tools, AI‑assisted insights, and conversational analytics—reducing reliance on IT and analytics teams Augment analytics with AI‑powered recommendations, anomaly detection, and insight automation, moving from dashboards to intelligence platforms.

· Achieved a 30% reduction in total cost of ownership (TCO) and streamlined reporting and KPIs across markets, improving decision-making by transparency.

· Cloud, Data & AI Architecture Strategy / transformation-oriented Data Platform Strategy & Architecture

· · Architected modern cloud data platforms leveraging AWS and GCP, enabling scalable analytics, ML, and GenAI use cases.

· · Introduced Snowflake and Databricks with a use‑case‑driven coexistence strategy to optimize cost, performance, and innovation.

· · Established AI‑ready data foundations enabling MLOps, advanced analytics, and GenAI experimentation to production at scale.

Proactively modernizing capabilities to keep pace with industry and maintain a sustainable competitive advantage.

Ensuring ongoing alignment with industry advancements while preserving market leadership.

· Maintained competitive advantage by introducing Snowflake as the enterprise data backbone, accelerating insights, improving cost efficiency, and enabling governed self‑service analytics technology agnostics MLOps approach.

· Introduced Databricks to modernize data engineering, enabling scalable pipelines, advanced analytics, and faster innovation.

· Introduced Snowflake to sustain an industry edge by modernizing data warehousing and analytics capabilities.

· Defied conventional thinking by enabling Snowflake and Databricks to coexist, leveraging each platform based on distinct data and analytics use cases.

· Maintained competitive advantage by enabling Snowflake and Databricks to coexist, optimizing cost, performance, and innovation through use‑case‑driven platform selection.

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· Technical Delivery of Analytics Products:Directed the delivery of advanced Data and analytics products, enabling key business outcomes such as faster planning and execution of research questions and deeper insights through clinical and real-world data (RWD) pooling. Delivered scalable solutions that accelerated data-driven research and innovation.

Maintained competitive advantage

· Led enterprise data strategy to elevate data governance and data literacy across J&J, advancing data‑maturity outcomes through adoption of FAIR data principles and enterprise data catalog capabilities.

· Championed Gartner’s Bimodal IT model, establishing a fail‑fast, exploratory Mode 2 to accelerate innovation while maintaining operational stability in Mode 1. Applied Gartner’s Bimodal IT framework to balance enterprise stability with innovation, using a fail‑fast, hypothesis‑driven Mode 2 to accelerate experimentation and time to value

· Pioneered a self‑service data access model through the implementation of a data virtualization strategy, improving speed to insight and reducing dependency on IT .significantly reducing wait time for data for analytical team provided real‑time, on‑demand access

· Pioneered an industry‑leading, policy‑based access control model that shifted data ownership from IT to the business—enabling instant, governed access to data in seconds instead of weeks.

Defined and executed a SAAS‑based enterprise data warehouse strategy to deliver a governed, integrated repository supporting enterprise reporting, advanced analytics, and regulatory‑grade data consistency.

· Champion data market place concepts & data mesh strategy

· Led the development of AI/Gen AI MVPs, including automation for promotional content generation, transcription summaries, and response

generation assistants.

Implemented

Pioneering strategy to create data Marketplaces which enable self‑service data consumption with embedded governance, ensuring users can access data quickly without compromising security, privacy, or compliance. Governance is built into the product, not enforced manually

Defined the roadmap for J&J’s first Product 360 and Customer 360 implementation, creating a unified view across customer, product, and supply chain domains.

Implemented JnJ first cross reference data system & supporting ontologies across all domains

Marketplaces enable self‑service data consumption with embedded governance, ensuring users can access data quickly without compromising security, privacy, or compliance. Governance is built into the product, not enforced manually

Data mesh shifts data ownership to business domains (e.g., Marketing, R&D, Finance), reducing bottlenecks caused by centralized data teams and enabling faster delivery of domain‑specific insights

 shorter lead times for data products

 Better alignment with business priorities

 Reduced central team overload

Define & led the creation of an enterprise data marketplace—a one‑stop platform for discovering, accessing, and reusing data products, APIs, and AI models.

Governance & Risk

• Established enterprise data architecture governance, standards, and review forums.
• Strengthened data risk posture while enabling faster, self‑service access to trusted data.
• Advanced FAIR data principles, enterprise data cataloging, and data literacy at scale.

Business & Financial Outcomes

• Delivered $10M+ annual P&L impact through platform modernization, analytics enablement, and cost optimization.
• Reduced data platform TCO by ~30% while accelerating time‑to‑insight from weeks to near real time.
• Led a $13M clinical data platform modernization, enabling cross‑study analytics and improved research efficiency.

· Transformed a team of FTEs and 100+ contingent members, implementing a follow-the-sun operational model across Americas, EU,

and APAC regions Played a pivotal role in driving Transform for Growth (TFG) strategy, enhancing competitive edge through talent

upskilling and adoption of Productization methodology by leveraging SaFE agile frameworks.

E X P E R I E N C E

New York Life. New York, Head of Enterprise Data Architecture 2015-2016

Responsible fo architecture & deliver next generation data platforms modernizingdata landscape and shape Data Science Center of Excellence to enable a data driven organization in strong collaboration with business and IT leaders across various lines of business.

· Architected and delivered global data transformation programs supporting profitability, retention, pricing, and compliance. Defined enterprise information management strategy and established analytics centers of excellence.

· Led large‑scale global programs integrating data across functions and regions.

· Provide though leadership &effectively build partnerships with business and IT leaders to align on digital strategy to drive business transformation for marketing, cross sell, digital marketing, customer segmentation.

· Modernized data and analytical capabilities with innovation and emerging technologies.

· As an interim CDO successfully implemented Data Governance to enable data driven culture.

AIG. New York, chief data Architect 2014– 2015

As Chief Data Architect reporting to the Global Chief Data Officer, I was responsible for integrating enterprise data to address risks arising from high interconnectedness, operational complexity, and potential insolvency, by applying a data‑driven, technology‑forward strategy focused on risk management and advanced data and analytics capabilities.

· Defined enterprise data and analytics architecture strategy to address risk, regulatory, and operational complexity.

· Led governance, compliance, and architecture alignment across global business units.

· Enabled advanced analytics for risk management and business transformation.

- Collaborate with the board of directors and executive leadership to align governance practices with organizational goals.

- Ensure compliance with industry regulations, including FED, , and other relevant bodies.

- Oversee risk management processes and conduct regular audits to identify and mitigate potential risks.

- Develop training programs to promote awareness of governance standards and ethical practices across the company.

· Led enterprise data architecture and governance aligned with U.S. financial regulatory and systemic‑risk frameworks, including Federal Reserve (Fed), FSOC, SEC, and Basel III principles, strengthening data risk posture while enabling scalable analytics and advanced data platforms.

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- Defined best practices for data governance y implementing data council resulting in significant cost saving for data procurement and optimized data life cycle management.

· Defined and implemented business centric Data and Analytics strategies to enable business transformation.

· Pioneered a big‑data implementation to replace legacy data warehouses, delivering global data and analytics capabilities that enabled business transformation across marketing, risk management, and operations—including cross‑selling, marketing optimization, and fraud detection models.

· Successfully articulated value proposition of modern Data Architecture to executive leaders resulting in executive sponsorship and investment.

· Led efforts to transform analytical capabilities with focus on continuous automation, improvement and development including people, process and technology.

· Defined and implemented BIG data strategy and Advanced analytics roadmap to enable analytics and harness enterprise data to support real time analysis, data exploration and predictive analytics.

· Responsible to modernize data landscape and shape Data Science Center of Excellence to enable a data driven organization in strong collaboration with business and IT leaders across various line of business.

· Effectively build partnerships with business and IT leaders to align on digital strategy to drive business transformation for marketing, cross sell, digital marketing, customer segmentation.

· Modernized data and analytical capabilities with innovation and emerging technologies.

· As an interim CDO successfully implemented Data Governance to enable data driven culture.

Marsh & McLennan. New York, Enterprise Information Program Manager, SVP 2006 – 2014

· · Architected and delivered global data transformation programs supporting profitability, retention, pricing, and compliance.

· · Defined enterprise information management strategy and established analytics centers of excellence.

· · Led large‑scale programs integrating data across functions and regions.

· for data driven decision making strategies to drive Client Profitability, Retention, Cross Sell, Client Satisfaction and Pricing of Services (e.g., Compensation Benchmarking), Revenue growth, At-­‐risk clients, Compliance, Operational.

· Defined and Implemented Enterprise Information management strategy, analytics framework, Global data governance across functions to enable straight through processing, implemented data platforms and established DnA CoE.

· · Defined enterprise information management strategy and established analytics centers of excellence.

· · Led large‑scale programs integrating data across functions and regions.

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Citigroup. New York, Global Consumer Bank 2000 – 2006

Following demonstrated success in e‑Consumer after the integration of online banking with brick‑and‑mortar channels, mortgage & credit cards my role expanded to lead Global Consumer data initiatives, integrating large customer demographics, banking and brokerage transactions, clickstream data, and product processor feeds into a common enterprise repository to enable growth across functions cross sell and retention The data volume tripled in size so had to define multiyear strategy to drive data transformation across consumer bank.

· Led enterprise data integration strategies across consumer banking, brokerage, and digital channels

Citibank. New York, E-Consumer 2000 – 2006

· Served as a Data Architect on the data team supporting Citigroup’s first online bank, enabling marketing to run targeted campaigns that drove customer growth, improved retention, and identified at‑risk clients.

· Lead Architecture strategy to integrate 15+ TB data which integrated 50+ data sources such as customer demographics, banking/brokerage transactions, customer click streams from various sources and product processors across the bank into a common repository, fed different functional data

· . · Enabled early online banking analytics, customer segmentation, and cross‑sell capabilities at scale.

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EDUCATION

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BACHELOR OF Electronics &Telecommunication & ENGINEERING, INDIA