The Experts below are selected from a list of 9 Experts worldwide ranked by ideXlab platform

Enda Ridge - One of the best experts on this subject based on the ideXlab platform.

  • Chapter 16 – People
    Guerrilla Analytics, 2015
    Co-Authors: Enda Ridge
    Abstract:

    No Analytics would be possible without analysts. In this chapter, you will learn about the skill sets required in a Guerrilla Analytics Team. This will help you make decisions about interviewing and training Teams so they can perform in Guerrilla Analytics projects.

  • Data Insight Services Risk Consulting
    2014
    Co-Authors: Enda Ridge, Edward Curry
    Abstract:

    Analytics projects come in many forms, from large-scale multi-year projects to projects with small Teams lasting just a few weeks. There is a particular type of Analytics project identified by some unique challenges. A Team is assembled for the purposes of the project and so Team members have not worked together before. The project is short term so there is little opportunity to build capability. Work is often done on client systems requiring the use of limited and perhaps unfamiliar tools. Deadlines are daily or weekly and the requirements can shift repeatedly. Outputs produced in these circumstances will be subject to audit and an expectation of full reproducibility. These are 'Guerrilla Analytics ' projects. They necessitate a versatile and fast moving Analytics Team that can achieve quick Analytics wins against a large data challenge using lightweight processes and tools. The unique challenges of Guerrilla Analytics necessitate a particular type of data Analytics development process. This paper presents research in progress towards identifying a set of development principles for fast paced Guerrilla Analytics project environments. The paper’s principles cover 4 areas. Data Manipulation principles describe the environment and common services needed by a Guerrilla Analytics Team. Data Provenance principles describe how data should be logged, separated and version controlled. Coding and Testing principles describe how code should be structured and outputs tested. All these principles focus on lightweight processes for overcoming the challenges of a Guerrilla Analytics project environment while meeting the Guerrilla Analytics requirement of auditability and reproducibility

  • emerging principles for Guerrilla Analytics development research in progress
    2012
    Co-Authors: Enda Ridge, Edward Curry
    Abstract:

    Analytics projects come in many forms, from large-scale multi-year projects to projects with small Teams lasting just a few weeks. There is a particular type of Analytics project identified by some unique challenges. A Team is assembled for the purposes of the project and so Team members have not worked together before. The project is short term so there is little opportunity to build capability. Work is often done on client systems requiring the use of limited and perhaps unfamiliar tools. Deadlines are daily or weekly and the requirements can shift repeatedly. Outputs produced in these circumstances will be subject to audit and an expectation of full reproducibility. These are 'Guerrilla Analytics' projects. They necessitate a versatile and fast moving Analytics Team that can achieve quick Analytics wins against a large data challenge using lightweight processes and tools. The unique challenges of Guerrilla Analytics necessitate a particular type of data Analytics development process. This paper presents research in progress towards identifying a set of development principles for fast paced Guerrilla Analytics project environments. The paper’s principles cover 4 areas. Data Manipulation principles describe the environment and common services needed by a Guerrilla Analytics Team. Data Provenance principles describe how data should be logged, separated and version controlled. Coding and Testing principles describe how code should be structured and outputs tested. All these principles focus on lightweight processes for overcoming the challenges of a Guerrilla Analytics project environment while meeting the Guerrilla Analytics requirement of auditability and reproducibility.

Edward Curry - One of the best experts on this subject based on the ideXlab platform.

  • Data Insight Services Risk Consulting
    2014
    Co-Authors: Enda Ridge, Edward Curry
    Abstract:

    Analytics projects come in many forms, from large-scale multi-year projects to projects with small Teams lasting just a few weeks. There is a particular type of Analytics project identified by some unique challenges. A Team is assembled for the purposes of the project and so Team members have not worked together before. The project is short term so there is little opportunity to build capability. Work is often done on client systems requiring the use of limited and perhaps unfamiliar tools. Deadlines are daily or weekly and the requirements can shift repeatedly. Outputs produced in these circumstances will be subject to audit and an expectation of full reproducibility. These are 'Guerrilla Analytics ' projects. They necessitate a versatile and fast moving Analytics Team that can achieve quick Analytics wins against a large data challenge using lightweight processes and tools. The unique challenges of Guerrilla Analytics necessitate a particular type of data Analytics development process. This paper presents research in progress towards identifying a set of development principles for fast paced Guerrilla Analytics project environments. The paper’s principles cover 4 areas. Data Manipulation principles describe the environment and common services needed by a Guerrilla Analytics Team. Data Provenance principles describe how data should be logged, separated and version controlled. Coding and Testing principles describe how code should be structured and outputs tested. All these principles focus on lightweight processes for overcoming the challenges of a Guerrilla Analytics project environment while meeting the Guerrilla Analytics requirement of auditability and reproducibility

  • emerging principles for Guerrilla Analytics development research in progress
    2012
    Co-Authors: Enda Ridge, Edward Curry
    Abstract:

    Analytics projects come in many forms, from large-scale multi-year projects to projects with small Teams lasting just a few weeks. There is a particular type of Analytics project identified by some unique challenges. A Team is assembled for the purposes of the project and so Team members have not worked together before. The project is short term so there is little opportunity to build capability. Work is often done on client systems requiring the use of limited and perhaps unfamiliar tools. Deadlines are daily or weekly and the requirements can shift repeatedly. Outputs produced in these circumstances will be subject to audit and an expectation of full reproducibility. These are 'Guerrilla Analytics' projects. They necessitate a versatile and fast moving Analytics Team that can achieve quick Analytics wins against a large data challenge using lightweight processes and tools. The unique challenges of Guerrilla Analytics necessitate a particular type of data Analytics development process. This paper presents research in progress towards identifying a set of development principles for fast paced Guerrilla Analytics project environments. The paper’s principles cover 4 areas. Data Manipulation principles describe the environment and common services needed by a Guerrilla Analytics Team. Data Provenance principles describe how data should be logged, separated and version controlled. Coding and Testing principles describe how code should be structured and outputs tested. All these principles focus on lightweight processes for overcoming the challenges of a Guerrilla Analytics project environment while meeting the Guerrilla Analytics requirement of auditability and reproducibility.