Ever since 2012’s Thomas Davenport and D.J. Patil article on data science , the topic of data
sc... more Ever since 2012’s Thomas Davenport and D.J. Patil article on data science , the topic of data science has been growing in popularity. Organizations are struggling to build data science capabilities, and universities are racing to build programs that produce individuals with data science skills, known as data scientists. Just what is data science, and what are the essential terms involved. This brief introduction to data science will provide an explanation and some context around a few of the essential terms one may encounter when learning the topic.
(NIST) promotes the U.S. economy and public welfare by providing technical leadership for the Nat... more (NIST) promotes the U.S. economy and public welfare by providing technical leadership for the Nation’s measurement and standards infrastructure. ITL develops tests, test methods, reference data, proof of concept implementations, and technical analysis to advance the development and productive use of information technology. ITL’s responsibilities include the development of technical, physical, administrative, and management standards and guidelines for the cost-effective security and privacy of sensitive unclassified information in Federal computer systems. This Interagency Report discusses ITL’s research, guidance, and outreach efforts in computer security, and its collaborative activities with industry, government, and academic organizations. National Institute of Standards and Technology Interagency Report 16 pages (2003) Certain commercial entities, equipment, or materials may be identified in this document in order to describe an experimental procedure or concept adequately. Suc...
Nearly every large organization on Earth is involved in software development at
some level. Some ... more Nearly every large organization on Earth is involved in software development at some level. Some organizations specialize in software development while other organizations only participate in software development out of necessity. In both cases, the performance of the software development matters. Organizations collect vast amounts of data relating to software development. What do the organizations do with that data? That is the problem. Many organizations fail to do anything meaningful with the data.
Another problem is knowing what data to collect. There are many options, but certain data is more important than others. What data should a software development organization collect?
This paper plans to answer that question and present a framework to gather the right information and provide a score for an organization that produces software. The score is not to be comparative between organizations, but to be comparative for a specific organization over time.
The primary goal of this work is to provide a general framework for what a software development organization should measure and how to report on those measurements. The focus is providing a single number to represent the entire organization and not just the development efforts. That single number is considered the Cumulative Result Indicator (CRI) score. The secondary goal of this work is to provide a framework for storing the necessary data.
Ever since 2012’s Thomas Davenport and D.J. Patil article on data science , the topic of data
sc... more Ever since 2012’s Thomas Davenport and D.J. Patil article on data science , the topic of data science has been growing in popularity. Organizations are struggling to build data science capabilities, and universities are racing to build programs that produce individuals with data science skills, known as data scientists. Just what is data science, and what are the essential terms involved. This brief introduction to data science will provide an explanation and some context around a few of the essential terms one may encounter when learning the topic.
(NIST) promotes the U.S. economy and public welfare by providing technical leadership for the Nat... more (NIST) promotes the U.S. economy and public welfare by providing technical leadership for the Nation’s measurement and standards infrastructure. ITL develops tests, test methods, reference data, proof of concept implementations, and technical analysis to advance the development and productive use of information technology. ITL’s responsibilities include the development of technical, physical, administrative, and management standards and guidelines for the cost-effective security and privacy of sensitive unclassified information in Federal computer systems. This Interagency Report discusses ITL’s research, guidance, and outreach efforts in computer security, and its collaborative activities with industry, government, and academic organizations. National Institute of Standards and Technology Interagency Report 16 pages (2003) Certain commercial entities, equipment, or materials may be identified in this document in order to describe an experimental procedure or concept adequately. Suc...
Nearly every large organization on Earth is involved in software development at
some level. Some ... more Nearly every large organization on Earth is involved in software development at some level. Some organizations specialize in software development while other organizations only participate in software development out of necessity. In both cases, the performance of the software development matters. Organizations collect vast amounts of data relating to software development. What do the organizations do with that data? That is the problem. Many organizations fail to do anything meaningful with the data.
Another problem is knowing what data to collect. There are many options, but certain data is more important than others. What data should a software development organization collect?
This paper plans to answer that question and present a framework to gather the right information and provide a score for an organization that produces software. The score is not to be comparative between organizations, but to be comparative for a specific organization over time.
The primary goal of this work is to provide a general framework for what a software development organization should measure and how to report on those measurements. The focus is providing a single number to represent the entire organization and not just the development efforts. That single number is considered the Cumulative Result Indicator (CRI) score. The secondary goal of this work is to provide a framework for storing the necessary data.
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Papers by Ryan Swanstrom
science has been growing in popularity. Organizations are struggling to build data science
capabilities, and universities are racing to build programs that produce individuals with data
science skills, known as data scientists. Just what is data science, and what are the essential
terms involved. This brief introduction to data science will provide an explanation and some
context around a few of the essential terms one may encounter when learning the topic.
some level. Some organizations specialize in software development while other
organizations only participate in software development out of necessity. In both cases, the
performance of the software development matters. Organizations collect vast amounts of
data relating to software development. What do the organizations do with that data? That
is the problem. Many organizations fail to do anything meaningful with the data.
Another problem is knowing what data to collect. There are many options, but
certain data is more important than others. What data should a software development
organization collect?
This paper plans to answer that question and present a framework to gather the
right information and provide a score for an organization that produces software. The
score is not to be comparative between organizations, but to be comparative for a specific
organization over time.
The primary goal of this work is to provide a general framework for what a
software development organization should measure and how to report on those
measurements. The focus is providing a single number to represent the entire organization
and not just the development efforts. That single number is considered the Cumulative
Result Indicator (CRI) score. The secondary goal of this work is to provide a framework
for storing the necessary data.
science has been growing in popularity. Organizations are struggling to build data science
capabilities, and universities are racing to build programs that produce individuals with data
science skills, known as data scientists. Just what is data science, and what are the essential
terms involved. This brief introduction to data science will provide an explanation and some
context around a few of the essential terms one may encounter when learning the topic.
some level. Some organizations specialize in software development while other
organizations only participate in software development out of necessity. In both cases, the
performance of the software development matters. Organizations collect vast amounts of
data relating to software development. What do the organizations do with that data? That
is the problem. Many organizations fail to do anything meaningful with the data.
Another problem is knowing what data to collect. There are many options, but
certain data is more important than others. What data should a software development
organization collect?
This paper plans to answer that question and present a framework to gather the
right information and provide a score for an organization that produces software. The
score is not to be comparative between organizations, but to be comparative for a specific
organization over time.
The primary goal of this work is to provide a general framework for what a
software development organization should measure and how to report on those
measurements. The focus is providing a single number to represent the entire organization
and not just the development efforts. That single number is considered the Cumulative
Result Indicator (CRI) score. The secondary goal of this work is to provide a framework
for storing the necessary data.