5 Pillars for Success
As a Solutions Architect I have lead teams on complex implementations of all sizes. With this knowledge I have provided articles and trainings around 5 key pillars that have supported me through the years.
Platforms: Service Cloud, Fields Service Lightning, AI and IoT implementations, Data Cloud & Asset centric builds.
TCS Projects: Fluence, Wabtec, GE, SoCal Edison, Boston Scientific.
Prior to joining TCS: USA.gov, SF.gov, SF.gov, Siemens Healthcare, Varian Medical Systems, BSC, Red Cross, FDIC, Appfolio, Financial Industries to name a few.
Pillars for Success Below >
Requirements & Discovery
Each new project starts like the new season of summer and sun. Project expectations are high, everyone is excited. Success is evident. But there is a question that needs to be addressed. The question is so simple really, but is actually one of the leading causes of project failure.
“How do we define what a business requirement is?”
For those that have read the book ‘Antifragile’ by Nassim Taleb, you might pick up on how the concept of building robust systems for man-made black swans and other associated misfits mother nature throws at us fits perfectly to cloud system and platform designs. How you might ask?
Setting the proper Planning, Strategies and Mindset are key in creating the stability, trust and focus for the client and teams to drive projects to success. The CDO Mindset reviews my executable list for success for any project.
2. Solution Design & Implementations
3. Data Management & System Design
4. Artificial Intelligence (AI) Roadmaps
I decided to write this series on implementing AI as there are some great articles from many authors but I wanted to read a more thorough view of the lifecycle of the ‘actionable’ process and I couldn’t find a completed process out there. After years of working on data models and systems here is my take on the AI build.
5. Predictive Modeling & Governance
Why is the messaging so complex when Predictive Modeling is discussed. You need to choose from multiple models, algorithms, historical data, current data. In this article I discuss a true passion of mine as to the process of modeling data to address the roadmap to AI. I review how to get started without the complexity using the Exploratory Data Analysis (EDA) process to gain insights and patterns.