Gartner Information Technology 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
Education
M.B.A., E-Business, Golden Gate University
B.S., Electrical Engineering, Bangalore University
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