SAP Machine Learning: Critical Thinking

17061 · ‎09-23-2019

SAP Machine Learning: Critical Thinking

Level 1 – Easy ; 5 minute read

Audience: Project managers, business analysts, subject matter experts

Author: Mark Muir, SAP BTS, S/4HANA RIG Americas

Introduction

“Critical thinking” involves asking appropriate questions, gathering and creatively sorting through relevant information, relating new information to existing knowledge, reexamining beliefs, reasoning logically, and drawing reliable and trustworthy conclusions.

Encyclopedia of the Sciences of Learning I 2012 Edition | Editors: Norbert M. Seel

I introduced what success and acceptance of the end result means to different people and organizations in blog 5.

The Business seeks a successful or useful outcome to the project from the business point of view e.g. the model meets the business goals and objectives, establish trust in the data etc …

The Data Scientist desires a successful outcome to the project in technical terms e.g. comprehension of the business question, choosing the right algorithm, model selection, accuracy and robustness, visualization, ease of maintenance etc…

Revisiting CRISP in blog 6 mapping roles and core competencies to each phase helps understand upskill and reskill requirements at a high level.

CRISP Phase	Role	Core Competencies
Business Understanding	Product owner, Business Stakeholder, Project manager / Scrum Master	Ability to evaluate your challenge, qualify the business need to justify proceeding with machine learning. Business vs latent need in the marketplace Articulate your problem statement to help develop the business case
Data Understanding	Product owner, *SME, stakeholder	Ability to understand business processes and data, identify regular data patterns and if necessary enhance data with other data sources.
Data Preparation	Product owner, *SME	The business is responsible to source data sets (quality and volume) that will return good model output.
Modeling	Data Scientist, technical *SME	Predictive modeling uses statistics to predict outcomes. This requires simulation of the original process with historical data and requirements to meet an agreeable outcome with the business. Core skills and techniques in open source programming languages e.g. Python (general approach to data science) ; R (statistical analysis)
Evaluation	Product owner, Data Scientist, Technical *SME, Business Stakeholder.	The business owner should be involved throughout the entire process, the end result should be a cumulative effort of passing through quality gates before handing over the model to the business.
Deployment	Product owner, Business Stakeholder.	There should be no surprises, define KPI’s and performance indicators to measure quality and success during the process, they may also change after use in a production environment.
Change Management	Chief Innovation Officer, Program management, Business Stakeholders	Institute Innovation Office to plan, schedule and release innovation into the workplace.