Gartner Expert

Sanjeev Mohan

VP Analyst

Sanjeev Mohan researches data management strategies within the Gartner for Technical Professionals (GTP) group. Mr. Mohan covers modern data and analytics architectures, frameworks and related technologies. He provides insights on cloud, IaaS/PaaS/SaaS, data storage including Hadoop, NoSQL, data lakes, data integration, data governance and data transformation strategies. He also covers the intersection of Internet of Things (IoT) and advanced analytics using machine learning (ML) and artificial intelligence (AI). He also covers data management and governance to meet technical needs of compliance and regulatory laws such as GDPR and CCPA/CPRA.

Previous experience

Mr. Mohan has over 30 years of technology industry experience across a variety of industries. Before joining Gartner, he worked in global consulting firms providing strategic advice, leading system integration implementations and building modern data architectures. He specializes in data management technologies, mission-critical systems and application architectures. He also has experience in integrating on-premises systems of global insurance clients with the new cloud-based big data risk management solution with emphasis on APIs, integration technologies and data migration.

Professional background

Risk Management Solutions

Head of Professional Services/Technical Solutions

Booz Allen Hamilton, KPMG Bearing Point, Scient

Data Management and Integration Lead

Oracle, nCUBE

Architect - RDBMS, ERP, UNIX Porting Team

Areas of coverage

Data Management Solutions

Analytics and Artificial Intelligence for Technical Professionals

Cloud Computing for Technical Professionals

Data Management Solutions for Technical Professionals


M.B.A., E-Business, Golden Gate University

B.S., Electrical Engineering, Bangalore University

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Top Issues That I Help Clients Address

1Choosing the optimal architectures and persistence for big data initiatives such as data lakes

2Big data architectures' ingestion, integration, persistence and advanced analytics in cloud and on-premises using Hadoop, Spark

3Data governance in data lakes including metadata, catalog, self-service data preparation and GDPR

4Internet of Things (IoT) information layer

5Data access options - SQL on Hadoop, data virtualization and BI tools