Most executives and managers say adopting AI and analytics is their top priority, however, only 1 of 3 of these projects succeeds. Further, the lack of analytics costs businesses some $242 billion annually from under optimized planning. Implementing AI has been elusive due to a lack of vision, voice, and clarity on the value of analytics, and how to achieve a culture of data-driven decisions. Module 1 of the Analytics Academy follows the book, “Implementing an Analytics Culture for Data-Driven Decisions”, to crystallize and articulate the Roadmap to implement an analytics culture and to give clarity to the four main components of Mindset, People, Process, and Systems. These components when aligned encompass a successful implementation path.
In Module 1 you’ll learn each of the four components of the analytics culture, then the assembly of these components into a Roadmap for how to implement analytics. You’ll conclude with an exceptionally enlightening use case where a highly successful analytics proof-of-concept project ended without its implementation, and where and why on the analytics Roadmap misalignment can cause a derailment.
Teaches that to enable analytics requires creating bandwidth, as all staff time is consumed in current reporting that is riddled with little used, little value, and unused reports. By eliminating these types of reports and automating others with Business Intelligence, data visualization, and even analytics tools, users gain bandwidth to launch analytics projects and explore their data with analytics tools.
Teaches the components of an analytics project, charting an analytics project, and an analytics project use case. The notion of “boiling the ocean” that the larger the analytics project the better is dispelled to start small then scale. The 5 components of an analytics projects are extensively reviewed of Problem Definition, Data, Analytics Insights, Insights to Action, and Solution Adoption.
In this workshop several students across multiple companies put together their plans to start an analytics project in their companies. Learn how they would approach an analytics project from concept to justification to project plan.
Teaches this innovative method to the essence of an analytics project; i.e. what you’re trying to solve. Since you can’t get the right answer unless you have the right question, Systematic Thinking™ was created to enable users with a discipline to approach the “object” of the project and takes you through the methodology and several examples of the application of Systematic Thinking™.
In this workshop several students across multiple companies put together their plans to do Systematic Thinking™ for a project in their companies. Learn how they would approach Systematic Thinking™ from concept to justification to project plan.
Gives a high-level overview of the concept and difference of Artificial Intelligence (AI) and Machine Learning (ML). You’ll learn AI and ML are related but different along with their definitions as well as the mechanics of ML and several applications. This isn’t a data science class nor a class for ML programming but a class that orients about AI and ML so you’ll know what it is, how it can be used, and the value that can be obtained.
Teaches the difference between biased and unbiased forecasting, with the former dependent on human “guesses” and the latter generated via statistical or AI methods. The class reviews several forecasting methods and provides a use case of statistical budgeting.
Reviews Module 1, Class 6 for those students that didn’t subscribe to Module 1. This important class explores some of the many applications of AI-Enabled analytics across finance, sales, and operations as well as dispels the misconception of data visualization as analytics. Statistical and AI forecasting is extensively explored.
