Data Strategy for Analytics and AI



An insurance company wanted to improve fraud detection, claims handling, marketing campaigns, and business decisions through analytics and machine learning.

Their internal data landscape was complex and fragmented with multiple policy systems and single-purpose data marts in almost every department.

Finance and actuarial teams were unable to make informed decisions quickly because they spent a lot of time looking for the right data and doing data cleanup or manipulation.


Worked with executives and key stakeholders to identify strategic goals and create a data strategy that aligned with these goals

Assessed the gap between the as-is and to-be data landscape, software and infrastructure needed to fulfill strategic goals

Developed a 3-year roadmap to build a data governance program and organization and to bridge the data landscape gap using an agile, in a phased approach.


Established a centralized cloud-based data lakehouse for data analytics and reporting and systematically improved overall data quality.

Created curated data sets for finance, claims, underwriting and actuary teams.

Implemented a data governance accountability structure and platform to catalog, track usage and protect PII data.

Implemented machine learning solutions to improve marketing, underwriting, pricing and claims