Ankush Jain is a Senior Principal Analyst at Gartner, primarily focusing on Data Quality and other Data Management areas (including and not limited to Metadata Management / Data Catalogs, Data Governance Solutions, Cost Optimization), where his key tasks encompass working on high-quality, actionable and consumable written research for IT leaders as well as providing actionable advise to IT Leaders/CxOs. Mr. Jain is responsible for conducting and driving critical research projects, including branded research comprising Magic Quadrants, Critical Capabilities, Market Guides, Toolkits, Best Practices and Survey Analysis.
Prior to joining Gartner, Mr. Jain was with American Express as Assistant Manager, Data Quality and Governance. His work at American Express involved driving key data governance initiatives, stakeholder management, supporting business analytics framework for MIS reporting, and helping clients with data quality issues.
Before joining American Express, he has worked with Pitney Bowes on its Spectrum Technology Platform as an Analyst, where he was a core member of the agile product development team that was entrusted with building and maintaining the Data Quality Products and dealing with clients like Twitter, Dropbox and Facebook. As a domain specialist, his role involved keeping a close eye on multiple other products in the market as well as keeping the management group well informed about the developments in the areas of big data and advanced data quality capabilities. He has also worked as an Informatica Developer with Accenture on two key projects with Morgan Stanley and AT&T, allowing him to traverse the complete data warehousing life cycle.
Manager - Data Quality & Governance
Pitney Bowes Software
Analyst (Spectrum Technology Platform for DQ/DI/DG/MDM and LI)
Data Management Solutions
Data and Analytics Programs and Practices
Bachelor of Technology, Electronics and Electrical Engineering
1Data quality (Solutions, Templates, Roles, Strategy, Practices, Market Trends and Use Cases)
2Metadata Management and Data Catalogs (Solutions, Roles, Practices, Use Cases, Passive and Active Metadata)
3Other Data Management Needs (Cost Optimization, Data Observability, Basic DataOps)
4Data Management Software Proposal Reviews