Data products are the deployed scalable, reproducible outcomes of a data science workflow that enable delivery of data-driven value to customers. Capitalizing on investment in data science teams starts with the ability to deliver data science insights to customers, whether those customers are end-users or internal business users. The customer delivery pathway consists of a data pipeline from source to end product and includes the functionality for iterative improvement of the data science workflow. Unlike traditional pure software products, data products must adapt to changing content in source data, and thus require additional tooling and process to adapt to changing operational conditions. Join us for a discussion on building data products in your organization to realize data science ROI.
Example Use Cases - Concrete examples of the applications of data products: their intended scope, their lifespan, and their effects on business.
Recouping Data Science Investment - Integration of data science teams with engineering teams and product teams to create a synergistic environment that enables your organization to realize value from your data science investment.
Increasing Visibility into the Data Science Workflow - Enabling org-wide visibility into your data science teams’ work to allow product, engineering, and management to understand how close you are to the next release.
Required Data; Expected Gains - Understanding of expected returns from an investment in data science efforts, including the amount of required time, money, and data.
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Data products are the deployed scalable, reproducible outcomes of a data science workflow that enable delivery of data-driven value to customers. Capitalizing on investment in data science teams starts with the ability to deliver data science insights to customers, whether those customers are end-users or internal business users. The customer delivery pathway consists of a data pipeline from source to end product and includes the functionality for iterative improvement of the data science workflow. Unlike traditional pure software products, data products must adapt to changing content in source data, and thus require additional tooling and process to adapt to changing operational conditions. Join us for a discussion on building data products in your organization to realize data science ROI.
Example Use Cases - Concrete examples of the applications of data products: their intended scope, their lifespan, and their effects on business.
Recouping Data Science Investment - Integration of data science teams with engineering teams and product teams to create a synergistic environment that enables your organization to realize value from your data science investment.
Increasing Visibility into the Data Science Workflow - Enabling org-wide visibility into your data science teams’ work to allow product, engineering, and management to understand how close you are to the next release.
Required Data; Expected Gains - Understanding of expected returns from an investment in data science efforts, including the amount of required time, money, and data.
