By the end of this course, learners are empowered to implement data-driven process improvement objectives at their organization. The course covers: the business case for IoT (Internet of Things), the strategic importance of aligning operations and performance goals, best practices for collecting data, and facilitating a process mapping activity to visualize and analyze a process’s flow of materials and information. Learners are prepared to focus efforts around business needs, evaluate what the organization should measure, discern between different types of IoT data and collect key performance indicators (KPIs) using IoT technology. Learners have the opportunity to implement process improvement objectives in a mock scenario and consider how the knowledge can be transferred to their own organizational contexts.
Data-Driven Process Improvement
This course is part of Data-Driven Decision Making (DDDM) Specialization
Instructors: Peter Baumgartner
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What you'll learn
Develop a plan to align operational and performance goals
Devise a data collection strategy and validate data integrity
Understand how to create current and future state process maps
Prioritize data gaps for root cause analysis
Skills you'll gain
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There are 4 modules in this course
This module covers the business case for establishing a data strategy, including why it is important, from a strategic level, to align your operations and performance goals before you undertake implementation.
What's included
6 videos7 readings1 assignment1 discussion prompt
This module covers best practices for collecting data.
What's included
5 videos4 readings1 assignment
This module will prepare you to develop current state process maps, prioritize data gaps, and formulate future state process maps.
What's included
5 videos5 readings1 assignment1 discussion prompt
This module provides an opportunity to bridge theory and practice. Learners apply knowledge from this course to solve a business problem.
What's included
2 videos1 peer review
Instructors
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