{"id":2129,"date":"2020-06-26T00:58:44","date_gmt":"2020-06-26T07:58:44","guid":{"rendered":"https:\/\/www.lightsondata.com\/?post_type=tva_lesson&p=2129"},"modified":"2022-10-09T08:56:57","modified_gmt":"2022-10-09T15:56:57","slug":"oracle-data-science-maturity-model-coming-soon","status":"publish","type":"tva_lesson","link":"https:\/\/www.lightsondata.com\/course\/oracle-data-science-maturity-model-coming-soon\/","title":{"rendered":"Oracle Data Science Maturity Model"},"content":{"rendered":"

Maturity Model Dimensions\/ Domains*<\/h3>\n
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  1. Strategy: What is the enterprise business strategy for data science?<\/li>\n
  2. Roles:What roles are defined and developed within the enterprise to support data science activities?<\/li>\n
  3. Collaboration: How do data scientists collaborate with others in the enterprise, e.g., business analysts, application and dashboard developers, to evolve and hand-off data science work products<\/li>\n
  4. Methodology: What is the enterprise approach or methodology to data science?<\/li>\n
  5. Data Awareness: How easily can data scientists learn about enterprise data resources<\/li>\n
  6. Data Access: How do data analysts and data scientists request and access data? How is data accessed, controlled, managed, and monitored?<\/li>\n
  7. Scalability: Do the tools used for data science scale and perform for data exploration, preparation, modeling, scoring, and deployment?<\/li>\n
  8. Asset Management: How are data science assets managed and controlled?<\/li>\n
  9. Tools: What tools are used within the enterprise for data science objectives? Can data scientists take advantage of open source tools in combination with production quality infrastructure?<\/li>\n
  10. Deployment: How easily can data science work products be placed into production to meet timely business objectives?<\/li>\n<\/ol>\n

    References<\/h3>\n