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Overcoming Inefficiencies in Multiple Work Management Systems

By Scott Prugh, CTO, Uturn Data Solutions, Dominica Degrandis, Rosalind Radcliffe, Pat Birkeland, Keanen Wold

Helping Your Enterprise with Their DevOps Transformation

Large enterprises are traditionally organized by function and managed to optimize vertically for specific outcomes.

In IT, this often means organizations specialize in functions such as design, development, QA, and operations.

Many decisions are made in the context of those functional silos as opposed to the end-to-end flow of delivery across those teams.

This mode of decision-making affects work management practices as well as tool selection for each group.

In traditional operating models of siloed teams, this is certainly an issue when creating an environment of task-driven queues.

As enterprises adopt Agile and DevOps, this mismatch between SDLC and ITIL practices, as well as tool silos, impedes both flow efficiency and work understanding within individual team and across teams.

This paper is focused on enterprise practitioners and management leaders who have multiple work management systems that their teams deal with every day and who struggle to provide visibility to the work as well as an improvement model to make the work and systems better.

  • File Size 0.43 mb

Features

  • Overcome Obstacles

    This paper provides clear guidance on how to overcome visibility issues when working in multiple work management systems

  • Expert Authors

    This paper is written by experienced leaders who have led their own successful transformations within large enterprise organizations.

  • Solution-Guiding Principles

    This paper provides a clear model with three solution-guiding principles to help lead your organization to success.

  • For Practitioners and Leaders

    Change Agents can come from anywhere on the org chart. This paper directly provides guidance on how to lead change no matter your role or title.

About the Resource

Large enterprises are traditionally organized by function and managed to optimize vertically for specific outcomes.

In IT, this often means organizations specialize in functions such as design, development, QA, and operations.

Many decisions are made in the context of those functional silos as opposed to the end-to-end flow of delivery across those teams.

This mode of decision-making affects work management practices as well as tool selection for each group.

In traditional operating models of siloed teams, this is certainly an issue when creating an environment of task-driven queues.

As enterprises adopt Agile and DevOps, this mismatch between SDLC and ITIL practices, as well as tool silos, impedes both flow efficiency and work understanding within individual team and across teams.

This paper is focused on enterprise practitioners and management leaders who have multiple work management systems that their teams deal with every day and who struggle to provide visibility to the work as well as an improvement model to make the work and systems better.

Scott Prugh, CTO, Uturn Data Solutions
Dominica Degrandis
Rosalind Radcliffe
Pat Birkeland
Keanen Wold
Scott Prugh, CTO, Uturn Data Solutions

Scott Prugh, CTO, Uturn Data Solutions

Transformational Technology Leader

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Dominica Degrandis

Dominica Degrandis

Dominica DeGrandis is the foremost expert in Kanban Flow within the IT industry today. Her work has shown working IT teams how effectively improve workflow and optimize throughput to produce the best result throughout the value stream. Her passion involves the use of visual cues and transparency across teams and organizations to reveal mutually critical information. As Director of Training & Coaching at LeanKit, Dominica combines experience, practice and theory to help teams level up their capability. She blogs at ddegrandis.com and tweets at @dominicad.

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Rosalind Radcliffe

Rosalind Radcliffe

IBM Fellow, CIO DevSecOps CTO

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Pat Birkeland

Pat Birkeland

Business Analytics Platform Services Operations Lead

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Keanen Wold

Keanen Wold

Engineering Manager - Engineering Effectiveness at Twitter

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