This installment of the Data Science Maturity Model (DSMM) blog series contains a summary table of the dimensions and levels. Enterprises embracing data science...

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. ...

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...

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...

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 opportunity...

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...

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...

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...

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...

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...

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 introduced...

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...

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...

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...

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,...

In this next installment of the Data Science Maturity Model (DSMM) dimension discussion, I focus on 'methodology': What is the enterprise approach or...

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,...

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...

In this next installment of the Data Science Maturity Model (DSMM) dimension discussion, I focus on 'collaboration': How do data scientists collaborate among...

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."...

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...

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...

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 introduction of new roles. Following the...

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...

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...

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 /...

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...