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Aurora
Student
Algeria
4
Questions
Asked
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What is it like to be an AI-Driven Diagnostics Innovator?
I heard a lot about this career and I have few pieces of information about it . I want to know in detail what you do , how is work and what did you do to finally get this job?
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1 answer
Jeffrey Wallk
Product Management & Knowledge Engineering
15
Answers
Schaumburg, Illinois
Updated
Jeffrey’s Answer
Hi Aurora,
Not sure if you are focused on a specific business vertical (area): healthcare, manufacturing, financial, engineering, transportation, ...
Diagnostics is an interesting area and has wide applicability (virtually every business, operation, product, service) requires diagnostics (mostly informal, but increasingly formal if digital automation is involved, sufficient complexity, variability, scale, and pace of change). On a more fundamental level, Diagnostics is a discipline to identify and understand the root causes of problems, anomalies, or outcomes within a process and/or system.
Diagnostics involves collecting and analyzing data to pinpoint the factors that led to a specific event or result. The goal is to move beyond simply knowing what happened to understanding why it happened (causal factors) and recommend and/or design proactive improvements to mitigate and hopefully prevent future occurrences (same issue or variation of the same issue where possible).
There are a number of key disciplines to learn and develop competency:
- Root Cause Analysis (RCA) is a structured approach for identifying the fundamental reason for a problem or a failure.
- Risk modeling which uses quantitative methods to assess the likelihood and potential impact of future events. Often uses statistical techniques.
- Scenario analysis is a strategic planning tool that evaluates different pathways for problem resolution and explores the potential future outcomes across a range of hypothetical situations.
- Decision intelligence is an emerging discipline that combines data science, machine learning, and behavioral science to inform and improve decision-making. It goes beyond traditional business intelligence by not just reporting on past data but also providing actionable insights and predicting the likely outcomes of different choices creating a framework for optimizing decisions.
All of the above disciplines require data and in most cases lots of data, so automation is key on the data extraction, data analysis, and applying AI in each of the above mentioned disciplines.
I rely on these disciplines along with systems engineering, knowledge engineering, and AI to support my work. I got this job because of work I had done with Digital Twins, which incorporates all of the above disciplines to create a closed loop system for continuous monitoring, real time decision support, and continuous adaptation and improvement across digital and physical systems.
As for your career options, they are unlimited if you have the above mentioned disciplines since you can apply these in most any type of field.
Reach out if you have more questions or would like additional reference materials.
Jeffrey
Not sure if you are focused on a specific business vertical (area): healthcare, manufacturing, financial, engineering, transportation, ...
Diagnostics is an interesting area and has wide applicability (virtually every business, operation, product, service) requires diagnostics (mostly informal, but increasingly formal if digital automation is involved, sufficient complexity, variability, scale, and pace of change). On a more fundamental level, Diagnostics is a discipline to identify and understand the root causes of problems, anomalies, or outcomes within a process and/or system.
Diagnostics involves collecting and analyzing data to pinpoint the factors that led to a specific event or result. The goal is to move beyond simply knowing what happened to understanding why it happened (causal factors) and recommend and/or design proactive improvements to mitigate and hopefully prevent future occurrences (same issue or variation of the same issue where possible).
There are a number of key disciplines to learn and develop competency:
- Root Cause Analysis (RCA) is a structured approach for identifying the fundamental reason for a problem or a failure.
- Risk modeling which uses quantitative methods to assess the likelihood and potential impact of future events. Often uses statistical techniques.
- Scenario analysis is a strategic planning tool that evaluates different pathways for problem resolution and explores the potential future outcomes across a range of hypothetical situations.
- Decision intelligence is an emerging discipline that combines data science, machine learning, and behavioral science to inform and improve decision-making. It goes beyond traditional business intelligence by not just reporting on past data but also providing actionable insights and predicting the likely outcomes of different choices creating a framework for optimizing decisions.
All of the above disciplines require data and in most cases lots of data, so automation is key on the data extraction, data analysis, and applying AI in each of the above mentioned disciplines.
I rely on these disciplines along with systems engineering, knowledge engineering, and AI to support my work. I got this job because of work I had done with Digital Twins, which incorporates all of the above disciplines to create a closed loop system for continuous monitoring, real time decision support, and continuous adaptation and improvement across digital and physical systems.
As for your career options, they are unlimited if you have the above mentioned disciplines since you can apply these in most any type of field.
Reach out if you have more questions or would like additional reference materials.
Jeffrey
I will use this advice as I prepare for my career . Thank you very much for your answer ,I really appreciate it .
Aurora