Empirical discovery concept model
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This document outlines the roles and relationships between data scientists, data engineers, and other roles in analyzing data to generate insights. It shows how data scientists articulate questions and hypotheses to direct experiments and analytical methods. Data engineers implement these methods by determining appropriate data sources and tools. Results and insights are then shared with relevant parties, informing future questions and refinement of models and methods.
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Empirical discovery concept model
- 1. Data Sets Data Sources Models Analytical Tools Empirical Method Experiments Hypotheses Results Questions or beliefs Predictions Conclusions Domain Analytical Methods Insight Consumer Data Scientist Articulates Directs & applies Creates & refines Effected by Lead to Tested by Use / require Motivate Creates & refines Generate Achieves Informed by & shares Inform Understands Defines & evolves Informs Data Engineer Implements Determines Applied to Validates Applied to Development Corpus External Sources Production Corpus Mirrors Applied to Reference Initial Interim New Drawn from Implemented as Implements Informs What is the question? How will we answer the question? What data will we use? What analytical method will we use? What tools will we use? What are the results? What do the results mean? What did we learn / discover? Who should we inform? What is the next question? Manages Manages Published as Empirical Discovery Method Concept Model Joe Lamantia Product Strategist: Big Data and Discovery Oracle Joe.Lamantia@Oracle.com v 4.2 | June 2014 Understands Exploratory Investigative Model Bullding Validation Training Informs Production Models Insights Data Products Measure TestAlgorithm