Data Products sit at the intersection of data science and product management. It's critical to have a roadmap in place which enables delivery of products driven by data science R&D directly to end users. Join us for three examples in three different industries of data products strategy and implementation. We'll discuss the organizational and technological implementations needed to achieve a successful deployment, turning data science investment to increased value for users and profit for the business.
During this webinar, we'll dive into the commonalities and differences between the design and implementation of a delivery optimization product in the logistics industry, and predictive customer 360 product in the insurance industry. We'll detail the delivery schedule for an incremental implementation plan of all three of these products in order to showcase how your organization can effectively invest in data products while gaining quick wins along the path to full product implementation.
Data Products Organizational Strategy: utilizing Expero's data products readiness rubric and our cross-functional teaming best practices, your organization will be prepared to build and deploy highly successful data products your users will love
Data Products Implementation Roadmapping: utilizing Expero's best practices for building data products and our recommended tech stack, your organization will be prepared to start scaling out data science solutions into fully hardened software applications.
Delivery Optimization: utilizing reinforcement learning, this data product enables higher shipping volumes through operational efficiency inside a freight delivery network
Portfolio Optimization: utilizing machine learning and visualization tools, this data product increases visibility across portfolios and assets, revealing missed opportunities and spotting complex risk.
360 Customer Behavior Analysis: customer cohort similarities and relationships drive 360 analysis of customer behavior, product/service cross sells, next conversation, etc.
Data Products sit at the intersection of data science and product management. It's critical to have a roadmap in place which enables delivery of products driven by data science R&D directly to end users. Join us for three examples in three different industries of data products strategy and implementation. We'll discuss the organizational and technological implementations needed to achieve a successful deployment, turning data science investment to increased value for users and profit for the business.
During this webinar, we'll dive into the commonalities and differences between the design and implementation of a delivery optimization product in the logistics industry, and predictive customer 360 product in the insurance industry. We'll detail the delivery schedule for an incremental implementation plan of all three of these products in order to showcase how your organization can effectively invest in data products while gaining quick wins along the path to full product implementation.
Data Products Organizational Strategy: utilizing Expero's data products readiness rubric and our cross-functional teaming best practices, your organization will be prepared to build and deploy highly successful data products your users will love
Data Products Implementation Roadmapping: utilizing Expero's best practices for building data products and our recommended tech stack, your organization will be prepared to start scaling out data science solutions into fully hardened software applications.
Delivery Optimization: utilizing reinforcement learning, this data product enables higher shipping volumes through operational efficiency inside a freight delivery network
Portfolio Optimization: utilizing machine learning and visualization tools, this data product increases visibility across portfolios and assets, revealing missed opportunities and spotting complex risk.
360 Customer Behavior Analysis: customer cohort similarities and relationships drive 360 analysis of customer behavior, product/service cross sells, next conversation, etc.
