Best Practices
Data Science Maturity Model - Methodology Dimension (Part 5)
In this next installment of the Data Science Maturity Model (DSMM) dimension discussion, I focus on 'methodology': What is the enterprise approach or methodology to data science? The most often cited methodology for 'data mining' - a key element of data science - is CRISP-DM. However, the breadth and growth of data science may require expanding beyond the traditional phases introduced by CRISP-DM: Business Understanding, Data Understanding, Data Preparation, Modeling,...
Thursday, June 7, 2018 | Best Practices | Read More
Data Science Maturity Model - Collaboration Dimension (Part 4)
In this next installment of the Data Science Maturity Model (DSMM) dimension discussion, I focus on 'collaboration': How do data scientists collaborate among themselves and with others in the enterprise, e.g., business analysts,application and dashboard developers, to evolve and hand-off data science work products? Data science projects often involve significant collaboration, defined as "two or more people or organizations working together to realize or achieve a goal."...
Wednesday, June 6, 2018 | Best Practices | Read More
Data Science Maturity Model - Roles Dimension (Part 3)
In this next installment of the Data Science Maturity Model (DSMM) dimension discussion, I focus on 'roles': What roles are defined and developed in the enterprise to support data science activities? A role can be defined as "a set of connected behaviors, rights, obligations, beliefs, and norms as conceptualized by people in a social situation." As with most any new field, data science within an enterprise can benefit from the addition of new roles. Following the 'strategy'...
Tuesday, June 5, 2018 | Best Practices | Read More
Data Science Maturity Model - Strategy Dimension (Part 2)
In my previous post, I introduced this series on a Data Science Maturity Model and the dimensions we'll be discussing. The first dimension is 'strategy': What is the enterprise business strategy for data science? A strategy can be defined as "a high-level plan to achieve one or more goals under conditions of uncertainty." With respect to data science, goals may include making better business decisions, making new discoveries, improving customer acquisition / retention...
Monday, June 4, 2018 | Best Practices | Read More
A Data Science Maturity Model for Enterprise Assessment (Part 1)
"Maturity models" aid enterprises in understanding their current and target states. Enterprises that already embrace data science as a core competency, as well as those just getting started, often seek a road map for improving that competency. A data science maturity model is one way of assessing an enterprise and guiding the quest for data science nirvana. As an assessment tool, this Data Science Maturity Modelprovides a set of dimensions relevant to data science and...
Wednesday, May 30, 2018 | Best Practices | Read More
Monitoring progress of embedded R functions
When you run R functions in Oracle Database, especially functions involving multiple R engines in parallel, you can monitor their progress using the Oracle R Enterprise datastore as a central location for progress notifications, or any intermediate status or results. In the following example, based on ore.groupApply, we illustrate instrumenting a simple function that builds a linear model to predict flight arrival delay based on a few other variables. In the function modelBuild...