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Gartner Expert
Carlie Idoine
VP Analyst
Carlie Idoine is a Research Director for Business Analytics and Data Science for Gartner for IT Leaders. Ms. Idoine is an accomplished IT professional with more than 25 years of experience in both business analytics and data science. She provides a unique blend of business and industry knowledge, leading successful efforts to integrate new technologies into effective business solutions. Her experience includes design, implementation and communication of an enterprise analytics strategy and comprehensive analytics program, evaluating and managing the enterprise analytics and data science software portfolio, business needs analysis, analytic tool and process support, custom business analytic and data science solution analysis, design and development, training development and instruction, and application of advanced analytics to complex business problems.
Roles and responsibility
CIO
CTO
Chief Data Officer
Chief Analytics Officer
Data Scientist
Business Analyst
Previous experience
Prior to joining Gartner, Ms. Idoine was the BI consultant and domain expert for the Business Intelligence Development Services Team within the Enterprise Architecture Organization at Progressive Insurance. Her responsibilities included planning and implementing a BI strategy, including BICC development, BI and analytics tool portfolio management, and planning and support of the organization, process and tools related to BI and analytics.
Professional background
Progressive Insurance
IT Systems Analyst Consultant
Roadway Express
Sr. Operations Research Consultant
Areas of coverage
Data and Analytics Leaders
Data Management Solutions
Analytics, BI and Data Science Solutions
Data and Analytics Programs and Practices
Education
B.B.A., Computer Science/Management Science, Kent State University
Top Issues That I Help Clients Address
1Evaluate business analytics and data science/machine learning tools and vendors including strengths, weaknesses, comparisons
2Define business analytics and data science/machine learning trends, including technology, organization and process
3Build and support an organization and architecture for analytics and data science/machine learning
4Leverage and support self-service analytics and citizen data scientists
5Develop and implement an analytics strategy and get started with data science/machine learning