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Best Practices

Data Science Maturity Model - Summary Table for Enterprise Assessment (Part 12)

This installment of the Data Science Maturity Model (DSMM) blog series contains a summary table of the dimensions and levels. Enterprises embracing data science as a core competency may want to evaluate what level they have achieved relative to each dimension - in some cases, an enterprise may straddle more than one level. As a next step, the enterprise may use this maturity model to identify a level in each dimension to which they aspire, or fashion a new Level 6. Data...

Thursday, June 28, 2018 | Best Practices | Read More

Data Science Maturity Model - Deployment (Part 11)

In this next installment of the Data Science Maturity Model (DSMM) dimension discussion, I focus on 'deployment': How easily can data science work products be placed into production to meet timely business objectives? Data science comes with the expectation that amazing insights and predictions will transform the business and take the enterprise to a new level of performance. Too often, however, data science projects fail to "lift-off," resulting is significant...

Wednesday, June 27, 2018 | Best Practices | Read More

Data Science Maturity Model - Tools Dimension (Part 10)

In this next installment of the Data Science Maturity Model (DSMM) dimension discussion, I focus on 'tools': What tools are used within the enterprise for data science? Can data scientists take advantage of open source tools in combination with high performance and scalable production quality infrastructure? A wide range of tools support data science ranging from open source to proprietary, relational database to "big data" platforms, simple analytics to complex machine...

Tuesday, June 26, 2018 | Best Practices | Read More

Data Science Maturity Model - Asset Management Dimension (Part 9)

In this next installment of the Data Science Maturity Model (DSMM) dimension discussion, I focus on 'asset management': How are data science assets managed and controlled? Assets are typically both tangible and intangible things of value. For this discussion, we will consider the array of data science work products as assets and can define 'asset management' at a high level as "any system that monitors and maintains things of value to an entity or group." As we...

Thursday, June 21, 2018 | Best Practices | Read More

Data Science Maturity Model - Scalability Dimension (Part 8)

In this next installment of the Data Science Maturity Model (DSMM) dimension discussion, I focus on 'scalability': Do the tools scale and perform for data exploration, preparation, modeling, scoring, and deployment? As data, data science projects, and the data science team grow, is the enterprise able to support these adequately? The term 'scalability' can be defined as the "capability of a system, network, or process to handle a growing amount of work, or its potential to be...

Tuesday, June 19, 2018 | Best Practices | Read More

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
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