RBC Capital Markets

- Lead the requirements gathering and creation of data quality rules for a new regulatory initiative (T+1).
- Manage 2 contract workers in drafting requirements, reviewing data lineage and creating be-spoke data quality rules across multiple quality 
 dimensions.
- Built a Tableau report all data quality controls created with meta-data on schema and other useful data elements to help reduce the time in the drafting of new data controls by 20%
- Built a Python script to Extract Deeply Nested JSON from S3 and Tranform into a CSV format for easy processing and analysis. (Avg size of data set is 600MB across 10 different instances with over 10 different critical data elements)

Senior Data Analyst

• Resolved a data quality issue of missing maturity dates on outstanding loans with a material impact of 122 billion USD.
• Collaborated with finance and regulatory reporting stakeholders to implement business requirements, resulting in a 25% increase in data quality and adherence to compliance guidelines as part of an enhanced enterprise data governance framework.
• Utilized Tableau to create an intuitive schema and data quality report, leading to a 35% enhancement in departmental rule design efficiency at RBC Capital Markets.
• Engineered data pipeline processes using Airflow and Python, reducing data processing times by 30% for faster insights delivery and improved efficiency in strategic decision-making.
• Collaborated with Regulatory Reporting and Finance IT stakeholders to enhance data quality and efficiency using DBT, resulting in a 25% reduction in data-related errors.
• Developed SQL data models on a cloud data warehouse and administered a Dremio data lake, handling over 10TB of data.

Data Analyst

Data Achievers

•  Worked with a variety of clients to support business intelligence reporting and data quality assurance.
•  Monitored product performance and customer satisfaction metrics to identify areas of improvement, resulting in a 40% increase in user engagement and a 25% increase in revenue.  
• Developed and maintained product roadmaps and feature backlogs, resulting in a 30% increase in team efficiency and a 20% increase in project success rate. 
• Created and maintained product dashboards and reports for job seekers, resulting in a 25% reduction in decision-making time and a 15% increase in data accuracy.

Data Scientist

TALENT™ (Ontario Tech Talent)

• Supported a self-paced, self-directed skills development experience (“Self Paced Experience”) for students looking to upskill in data analytics;
• Asynchronously engaged with learners (e.g. discussion forum moderation) via email, chat, or LMS like Canvas
• Provided 1:1 learner support every week; offered as virtual appointments booked through Calendly
• Effectively and efficiently verified customer skill acquisition by way of reviewing all artifacts and other work products presented for verification by learners against approved standards
• Recorded skills verification decisions in TALENT’s system of records

Skill Coach

• Demonstrated exceptional communication skills and exercised diligence in identifying delinquent loans, credit cards, credit lines, or mortgages, and referred clients to Collections.
• Established and maintained relationships with individual or business customers by offering value through every interaction.
• Identify fraudulent transactions at the first point of contact and work with AML and Fraud teams to investigate the transaction to ensure minimal client impact.
• Actioned solutions relevant to client banking needs, with a 30% action rate weekly, to support team goals.

Client Data Analyst

IKEA

- Enhanced order fulfillment Tableau dashboards, improving operational visibility and increasing efficiency by 15%. 
- Analyzed operational data to optimize processes, leading to a 25% improvement in service levels. 
- Collaborated with teams to implement data-driven strategies, reducing inventory errors by 20%

Operations Analyst Intern

Walmart Canada

•	Provided exceptional customer service to clients on the sales floor, ensuring that all their questions are answered promptly and efficiently.
•	Acted as the first point of contact to customers' challenges with finding merchandise on the sales floor.
•	Contributed to team success by supporting colleagues with their workload to effectively deliver on customer promises.
•	Built and maintained a strong relationship with colleagues to improve the quality of work and increase customer satisfaction.
•	Managed safety risk on the sales floor by proactively identifying and removing potential safety issues.

Fulfillment Associate

Fond Regional Pour La Promotion de la Sante du Centre (FRPSC)

• Implemented an analytical framework that reduced transportation costs by 20% by determining the best delivery route for urgent medicine delivery across geographical regions.
• Built dashboards using Tableau to help track Key Performance Indicators (KPIs) thereby increasing visibility on key business metrics.
• Conceptualized a new service delivery design which when implemented improved service levels by 25%.
• Analyzed 6 business processes to collect measurable data.
• Improved the existing demand forecasting software performance by modeling data sources to be compatible with schema.
• Maintained inventory ERP system by ensuring consistency between inventory on-hand and inventory in the system

Logistics Analyst

Cam-Green

• Planned and developed content strategies.
• Built strong relationships with project team members and stakeholders to ensure the achievement
of set goals.
• Assessed business opportunities and develop strategies to attract venture capital.
• Responsible for drafting a business plan for validating the business model.
• Provided consultation on planning and starting new businesses.
• Prepared business proposals and reports for presentation to stakeholders.
• Conceived and implemented a sales development program for the launch of the project which led
to a 110% achievement of the sales target.
• Developed media campaigns across social media outlets and campus bulletins which galvanized
support from key government officials.
• Worked closely with the project manager to ensure that sales and marketing efforts are consistent
with project goals.
• Complaint with established guidelines set by the University Board sponsoring entrepreneurial
initiatives.

Social Media Manager

I help entry-level, non-technical professionals get their first data analyst role | Data Analyst @RBC | Data Career Coach

I love to identify hidden business opportunities that lie in data and bring them to life with compelling visualizations.  I spend time setting clear goals, plans of action, and risk mitigation strategies for data analytics projects before even collecting data. 

I am a data analytics professional with a strong background in supply chain and marketing. I am passionate about data engineering. I love a good journey🚘, be it a journey in an RV truck across country, a learning journey📝 , or a customer journey with multiple touch points, or a data pipeline from source system to end user application. I am always curious to know what I can learn about a customer's journey to ensure it is seamless.😊

At RBC I have developed my customer-focused problem-solving mindset at the awareness stage and consideration stages of the business's marketing funnel. I worked closely with partners across the delivery channels (branch and telephone) to deliver exceptional customer service to clients to meet their banking needs. 

At RBC Capital Market, as a Senior Data Analyst, I own the process of ensuring data sent to report owners are fit for purpose (without data quality issues). Build data quality rules using SQL. Build Tableau dashboards to monitor data quality of critical data elements (CDE's) in production 

My experience working with cross-functional teams and creating a sense of synergy in the achievement of a common goal has proven to be a valuable value add. I helped in the resolution of data quality issues by proposing changes to how data is transformed both upstream and downstream.

I am always on the lookout for technologies that improve my workflow; I will take a week or so and dive deep into a data technology to see if it’s a good fit for a side project I'm working on and put aside if not. 

When I'm not working I am with my family making sure we are living every moment and making it count. You can also find me with my headsets listening to the next big rising artist of our generation and exploring new genres of music - I am a big lover of Afrobeats and Cameroonian cuisine 🇨🇲 

---
Current Status: Helping folks get their First Data Analyst roles - DM me to learn more

Peter Ndiforchu

I’ll be honest: the hardest skill I had to learn in data had nothing to do with data.

It wasn’t SQL.
It wasn’t Excel.
It wasn’t even storytelling with dashboards.

It was learning to ask the right questions.

Early on, I’d rush to analyze data — build visuals, crunch numbers, automate reports — only to realize I didn’t actually understand what problem I was solving.

That’s when it clicked:
👉 Great analysts don’t just answer questions.
They help clarify them.

Now, before I open any tool, I ask myself:

What decision will this data inform?

Who needs this insight, and why?

What does success really look like?


Once I learned that skill, everything changed.

🎯 Your turn:
What was the hardest skill you had to learn early in your data journey?

---
📌 Some of the hardest skills in data are the most basic skills. RSVP for our upcoming event on Saturday 08/10 to learn more about how to stand out with these skills in a competitive job market: https://luma.com/wphk134i

#52DaysDataChallenge

I help mid/senior level, non-technical professionals get their first data analyst role | Senior Data Analyst @Autodesk |Top rated Data Career Coach on topmate.io

You don’t need a data job to start thinking like an analyst.

A few years ago, I wasn’t a “Data Analyst.”
I was working in warehouse operations and customer service — managing shipments, handling client issues, and keeping things running smoothly.

Back then, I didn’t even know “data analytics” was a career path.
But I noticed something:

Every decision —
How much to stock, when to reorder, which customers to prioritize — was driven by data.

And that curiosity changed everything.

I started asking:
🧠 “What story is this data telling us?”
📊 “How can I use it to make smarter decisions?”

That’s when I began my transition — not by quitting overnight, but by learning step by step:
✅ Mastered Excel and dashboards
✅ Took online data courses after work
✅ Practiced by analyzing data from my own job
✅ Built small projects and shared them online

Slowly, I went from tracking numbers to telling stories with data.
And that shift opened doors to a brand-new career.

If you’re in a non-data role right now, remember that you’re not starting from zero.

You’re bringing problem-solving, people skills, and business insight that analysts use every day.

You just need to layer on the tools and mindset.

So tell me!

💬 What did you do before data?

#52DaysDataChallenge

Most data analysts didn’t take the straight road. They built their careers from the detours.

Every week in this challenge, I meet people who stepped into data through the side doors: 

→Retail floors
→Call centers
→Warehouses
→Operations teams
→Customer service desks
→Every job that required GRIT before SQL.

Some of the most resourceful and business-savvy analysts I know didn’t start with a technical background. 

They started with:

→People 
→Problems
→Messy processes
→High-pressure decisions. 

The mindset came first.
The data skills came later. 

Live #3 will spotlight the career journeys that rarely get told:

→The self-taught
→The late starters
→The career shifters
→The “I didn’t study this” crowd


If you have ever wondered whether your background is an advantage waiting to be reframed, this session will reveal exactly how that transformation happens.

I want your input:

👉 Who should I invite next
Tag someone with an unexpected path into data
Or share the story you want to see featured

Let’s turn this Live into the most relatable conversation of the challenge.

The real test of my data career is not SQL or dashboards.

It is explaining my job to my family around the world.

I’ll jump on a call excited to share something meaningful about a new insight or a successful analysis.

Before I get two sentences in, someone immediately says,
“Peter, you are good with computers. My WhatsApp is acting strange.”

I have explained my work so many times.
Analytics. Forecasting. Helping teams make decisions.

Yet the official family definition has settled at:
“Peter works with numbers and computers. Ask him.”

My mom once introduced me to someone at church with pure confidence.
“My son works in technology. If your phone is not behaving, he can fix it.”

My brother once asked if I could check his internet history from Canada.
He really believed I had that level of power.

And whenever the holidays come around, one relative always waits for the right moment.
“Peter, since you understand computers, my WiFi has been slow since 2019. Can you take a look?”

At this point, I save my explanations for work.
With family, I just help where I can and smile while doing it.

Family: “So you are I.T.?”
Me: “Not exactly”
Family: “Great. The remote stopped working.”

Anyone else living this experience? 😅

#52DaysDataChallenge

🎙️ Essential Data Skills for Career Switchers

When I first started learning data, I had no idea where to begin.
I spent months jumping between Excel tutorials, SQL videos, and random courses — but nothing connected.
It wasn’t until I understood which skills actually mattered that things finally started to click.

That’s why I’m hosting Live #2 of the #52DaysDataChallenge:
👉 “Essential Data Skills for Career Switchers”

In this session, I’ll share:
✅ The foundational data skills every career switcher should master first
✅ Real examples of how these skills show up in the workplace
✅ The biggest learning traps that waste time and delay progress
✅ A simple roadmap to go from confused beginner to confident analyst

👥 Tag someone who’s learning data right now!
Let’s make this journey easier — together.

Darshan Patel, Bukola Oyinlola and 89 other attendees

🎙️ Essential Data Skills for Career Switchers

I didn’t start in data.
And most people who end up in analytics… didn’t either.

When someone asks, “How do I transition into Data Analytics from my old job?”
I always think back to the moments that quietly shaped my own journey.

Most of us made the switch long before we realized we were “doing data.”
We were the ones fixing broken processes…
…tracking numbers when nobody asked…
…building little spreadsheets to make life easier for our team…
…spotting patterns in chaos that no one else noticed.

Your old job didn’t hold you back.
It trained you.

→Teachers already understand assessments and learning outcomes.
→Bankers understand risk, trends, and financial behavior.
→Retail workers understand customer patterns.
→Healthcare workers understand process flow and documentation.
→Engineers understand systems thinking.
→Marketers understand experimentation and attribution.

Every role comes with analytical gold—you just need to translate it.

Here’s the part most people miss:

You don’t transition by abandoning who you were.
You transition by repackaging what you already did.

If you’re trying to break in:
• Identify the analytical parts of your old role.
• Show how you improved something with numbers.
• Highlight moments where you solved problems with data, even informally.
• Build 2–3 small portfolio projects that mirror the jobs you’re applying for.
• Tell your story as someone who’s already been using data… just without the title.

The door into analytics isn’t as far as it looks.
You’ve likely been standing in front of it for years.

What did you do before data?
Share below — your story might inspire someone else.

#52DaysDataChallenge

💻 If Excel had feelings…

It would probably file an HR complaint by now. 😂

Because let’s be honest

We’ve all done some questionable things to our poor spreadsheets:

1️⃣ Renamed the same file 8 times: “Final.xlsx” → “Final2.xlsx” → “Final_ThisOne.xlsx” → “Final_FINAL.xlsx”
2️⃣ Used “Merge & Center” like it’s a design tool 🎨
3️⃣ Written a formula so long even Excel gave up: #NAME?
4️⃣ Pretended we knew why the pivot table disappeared 😅
5️⃣ Watched Excel freeze… and prayed for our unsaved work 🙏
6️⃣ Copied data from a PDF like we were performing surgery with a spoon 🥲

Yet through it all, Excel stays loyal.
No subscription drama. No attitude. Just pure spreadsheet magic.

If Excel could talk, what do you think it would say after seeing your last file? 👀

#52DaysDataChallenge

It wasn’t love at first sight.
At first, data looked complicated. 

Rows. 
Columns. 
Chaos.

My journey started far from spreadsheets, on retail floors, in warehouse stores, and on countless customer service calls.

I didn’t have dashboards or SQL then, just conversations.
But every interaction had something in common: patterns.

I began noticing how certain products sold faster at specific times.

How customers’ frustrations often pointed to deeper issues.

How one small change in process could ripple across an entire team.

Back then, I didn’t call it data.
I called it paying attention to detail.

Over time, I realized those observations were data.
And once I learned how to analyze them, everything changed.

Data stopped being numbers.
It became stories, truths, insights waiting to be uncovered.

That’s what made me fall in love with data.
Its power to give meaning to everyday moments.
To show patterns in chaos.
To help people see what they couldn’t before.

💬 Now I’m curious.
What made you fall in love with data?
Was it a project, a person, or that one “aha” moment when it all clicked?

Someone needs to hear your story today.

Day 17 #52DaysDataChallenge

The most underrated data skill isn’t SQL, Python, or dashboards.

It’s empathy.

I’ve spent years across retail floors, warehouses, and corporate boardrooms… and one pattern always stands out:

The analysts who rise the fastest aren’t just technically strong.

They care enough to understand the human on the other side of the data.

Empathy helps you:
• Ask sharper questions because you understand the real pain behind the request
• Build dashboards people actually use
• Translate complexity without making stakeholders feel small
• Design solutions that fit the workflow, not fight it
• Notice when a number feels “off” because you understand the story of the process

People think empathy is “soft.”
In reality, it’s a strategic advantage.

Every insight we deliver impacts someone’s job, decision, or customer.

When you remember that, your work becomes sharper, more relevant, and significantly more trusted.

The most underrated data skill: empathy.
Agree?

If you took away these 5 tools, my entire week would fall apart.

Here are the core platforms I rely on as a Senior Data Analyst — and why they matter for real business impact:

1. SQL
My decision engine.
This is where I validate narratives, challenge assumptions, and uncover the signals leaders don’t see yet. When the story is unclear, SQL brings the truth to the surface.

2. dbt Labs
Where chaos becomes structure.
Testing, documentation, lineage, and version control — this is how analytics scales beyond a single analyst. dbt turns insights into reliable systems the whole business can trust.

3. Power BI / Looker 
The boardroom translator.
Dashboards aren’t about charts. They’re about influence.
This is where I refine the message, highlight the “why it matters,” and drive decisions that move revenue, cost, or risk.

4. Cursor
The accelerator I didn’t know I needed.
Reusable SQL templates, automated documentation, workflow shortcuts — it’s helped me reclaim hours and reinvest them into deeper, strategic analysis.

5. Jira/Confluence by Atlassian
The operational glue.
Intake, alignment, prioritization, cross-functional visibility — without these, even the best insights get lost in the shuffle. These two keep the work moving and the team aligned.

These tools cover the full lifecycle:

Question → Data → Transformation → Story → Delivery.

Which one should I demo next?

#52DaysDataChallenge

5 Essential Data Analytics Workflow Tools 

Some days, being a Data Analyst feels like being a translator…between English and ‘Data.’

Stakeholder: “I want a dashboard to show user satisfaction — so we can see how pleased our users are with our product.”

Me: “Absolutely! I can build that. Are there any specific KPIs that interest you?”

Stakeholder: “You mean… metrics?”

Me: “Kind of! Metrics are the raw numbers like number of promoters or rate of positive feedback. KPIs are the key ones that tell us if we’re actually meeting our goals like NPS or CSAT.”

Stakeholder: “Got it. Then I’d like to see our NPS and CSAT.”

Me: “Perfect! Those are great KPIs for user satisfaction. What grain would you like to see on the dashboard visuals?”

Stakeholder: “Grain? Like... rice? We’re not an agricultural SaaS, you know.”

Me (internally): Deep breath. Smile. You’re the data whisperer, remember? 😅

Stakeholder: “Not sure what you mean, but I trust your expertise.”

Me: “Fantastic. I’ll take it from here.”

That conversation reminded me that Data Analysts are part translator, part therapist, part magician.

You’re not just building dashboards.
You’re bridging two worlds: business and data.

The best dashboards aren’t the flashiest,  they’re the ones your stakeholders actually understand.

So the next time someone says:

“We need a dashboard!”

Ask them:

→ What decision will this dashboard help you make?

→ Which KPIs matter most?

→ What level of detail (not rice 😄) do you want?

That’s how you turn confusion into clarity — and earn the words every analyst loves to hear:

 “I trust your expertise.” 🙌

#52DaysDataChallenge

Every data pro you admire once stood where you are — at the bottom of the same ladder.

When I first stepped into data analytics, I thought there were only two levels:
✅ “Beginner”
✅ “Expert”

Turns out… there’s an entire ladder in between.
And understanding it changed how I learned, applied, and grew.

Here’s how I now see the Data Career Ladder 👇

1️⃣ Data Curious — You’re exploring what data even is. Watching YouTube videos, reading posts, trying to understand what analysts do.

2️⃣ Data Learner — You’ve picked your tools (Excel, SQL, maybe Python). You’re following tutorials, taking courses, and building your foundation.

3️⃣ Data Doer — You’re applying skills on small projects. You clean messy spreadsheets, build dashboards, or analyze a dataset from Kaggle.

4️⃣ Data Storyteller — You connect insights to impact. You can explain “what the data means” in a way business people understand.

5️⃣ Data Strategist — You go beyond reports. You influence decisions, set up frameworks, and help define what to measure in the first place.

💡 Every step builds on the last — and you can’t skip steps.

When I recognized where I was on this ladder, I stopped comparing myself to others…
and started focusing on the next rung.

---
Your turn:
👉 Where are you on this ladder right now?
👉 What’s one skill helping you climb to the next level?

#52DaysDataChallenge

The Data Career Ladder

Sharing with my network! I've once interviewed for this role and they are a solid team I must say!

Célia Bru is a great data leader!

📈 We're hiring a Senior Data Analyst at KOHO! 📈 

My team needs someone rare: someone who lives at the intersection of deep business thinking and hands-on technical mastery.

You'll own data models in DBT, and create dashboards, but that's just the baseline. What really matters? You understand what drives our business forward and can draw clear connections between technical work and strategic business outcomes. 

The cherry on top: You would get to work with the most caring and fun team I know! ❤️ 

#hiring #dbt #dataanalytics

View the open to hiring details of Célia

The Power of Storytelling in Data Careers. 

This one skill has helped me grow my data analytics career in so many unexpected ways.

What's that one unexpected skill you didn't know would serve you well in your career?

#52DaysDataChallenge

Dashboards behave perfectly… until they sense a stakeholder within 10 feet.

Have you ever tried to convince a toddler to sit still for ONE photo?

That's my relationship with my dashboards before a stakeholder demo

You line up the filters.
You fix the labels.
You triple-check the KPIs.

And for a brief, glorious moment… everything behaves.
Until—
You hit Publish and the dashboard decides to express its true personality.

Suddenly:
→A number you didn’t touch is different.
→A filter you tested 12 times stops working.
→A chart decides today is the day it wants to be “creative.”

And then a stakeholder pops in like:
“Is the data… supposed to do that?”

If you’ve ever had this love–hate relationship, you already know the feeling.

Describe this with ONE emoji.
(I’m ready for the chaos.)

Who can relate?

#52DaysDataChallenge

Unconventional Paths Into Data Analytics 

Most analysts didn’t start in data.
They came from retail, banking, customer service, logistics, hospitality, and so many other places that taught them how to solve real problems long before they touched SQL.

This Live breaks down how people with unconventional backgrounds successfully pivot into analytics — and how your past experience might already give you an edge.

In this session, you’ll learn:
• How to spot “data moments” in your past roles
• Why non-traditional paths often create stronger analysts
• Real stories of pivots that worked
• Practical steps to position yourself for a data role

Date: November 22nd, 2025
Time: 12:15 PM EST

If you’re transitioning into data or guiding others on the journey, this one is for you.

Join us and bring your questions.

Rinu Francis, Bukola Oyinlola and 30 other attendees

Unconventional Paths Into Data Analytics 

If your insight doesn’t change a decision, it’s not an insight. It’s a recap.

How do you write insights that make execs listen?

Most analysts write insights to explain data.
The best analysts write insights to move decisions.

Here’s the pattern I’ve seen across product, finance, and capital markets:

1. Lead with the headline, not the homework.
Your first sentence should answer: “Why does this matter right now?”

2. Translate metrics into money, risk, or time.
Executives think in trade-offs and outcomes—speak their language.

3. Create tension before you reveal the punchline.
Show the gap between the current state and the opportunity.
Tension fuels attention.

4. Strip out every detail that doesn’t change the recommendation.
If it doesn’t move the decision, it doesn’t belong in the slide.

5. End with a clear call: decide, approve, prioritize, or pivot.
Your insight should point to an action—not another meeting.

This simple narrative shift consistently takes conversations from:
“Nice dashboard.”
to
“Okay—what do we do next?”

What’s your storytelling trick when presenting insights?

#52DaysDataChallenge

How Communicate Actionable Insights to Execs

Some days the hardest part of being an analyst isn’t the data… it’s the tools.

We all have that one platform that tests our patience, derails our flow, or sends us straight to Google with a desperate “why isn’t this working?”

So let’s find out:

Which tool frustrates you the most?

I’m running this poll because every frustration has a story behind it — the refresh that never finishes, the merge conflict that ruins your morning, the notebook that suddenly refuses to run the same cell it ran an hour ago.

Vote — then share the specific tool and one thing that really gets under your skin.

Your comment might help someone realize they’re not alone… or point them to a solution you wish you had sooner.

What’s the one tool that tests your patience the most?

#52DaysDataChallenge

Data without context is noise.
Context without curiosity is wasted.

Lately I’ve noticed something across teams I support:

People aren’t short on dashboards.
They’re short on situational awareness.

You can have perfect SQL, clean models, and beautifully designed visuals—
but if you don’t understand the “why behind the numbers,” your insights never move the room.

Executives don’t act because you showed a metric.
They act because you showed meaning.

The strongest analysts I know do three things exceptionally well:

1. Anchor every number to a business moment
A spike doesn’t matter until you explain what triggered it.

2. Pull in what’s not in the data
Market dynamics, customer sentiment, operational constraints, timing.

3. Translate insights into consequences
“If we don’t fix this, here’s what happens.”

That’s when noise becomes narrative.
And narrative becomes action.

What context are you missing lately?

Read any ERD and join tables like a pro — SQL fundamentals

Greg Coquillo

Product Leader @AWS | Startup Investor | 2X Linkedin Top Voice for AI, Data Science, Tech, and Innovation | Quantum Computing & Web 3.0 | I build software that scales AI/ML Network infrastructure

Product leader with a passion for solving business use cases such as Growth, Go-to-Market, and Price Optimization. I owned P&L for commodity-based products, and leveraged AI to increase the average customer contract values across business industries. As a Product Leader at Amazon, I own a roadmap that drives the adoption of AI products through the Media Automation charter, which supports Private Brands’ YOY growth. I work with Data Scientists, Software Engineers and Business Intelligence Engineers to leverage Machine Learning and Big Data Analytics to design predictive models that enhance business decision-making and performance to accelerate growth, reduce Time to Market of products, monitor cross-geo compliance and import requirements, and ensure customer safety.

I am always curious and eager to learn how new technology affects our daily lives, and I love to support communities on their quest to adopt Artificial Intelligence across industries. Feel free to contact me if you have Data Science projects that you’d like to be showcased on this platform for visibility.

🌐I am a Global Business Leader with 12 years of experience and skills including Product Management, P&L management, Global Networking, EBITDA Optimization, Pricing Management, Margin Management, Product Management, Program Management, Operations and Supply Chain Management, Lean Manufacturing, Six Sigma, and Continuous Improvement.

🧘🏽‍♂️Soft Skills include: Communication, Collaboration, Lateral Thinking, Adaptability, Inspiration, Innovation, Empathy, Listening, Diplomacy, Organization, Responsibility, Leadership...

🎬🎥🎞 I really enjoy watching movies, and if I could redo it all over, I’d be a Sci-Fi script writer so I can be bounded by the limits of imagination.

(Opinions are my own)

Product Management, Media Automation

Owner of the AI vision and roadmap for Private Brand’s Compliance Division. Driving adoption of new technology, allowing my customers to scale their processes and increase productivity. Core responsibilities include:

-Building cross-functional teams consisting of Data Scientists, Software Engineers, Data Engineers, Product Managers and Program Managers, to plan and implement sophisticated AI projects
-Manage the New Initiative Intake Process
-Gather Business requirement artifacts
-Communicate roadmap definition and prioritization
-Coordinate User Acceptance Testing
-Manage Launch Announcements
-Prepare and communicate annual planning artifacts

Building Agentic AI systems beyond connecting APIs or LLMs is complicated, but not impossible.

This architecture lays the foundation for how AI agents think, communicate, and improve, covering everything from testing and observability to deployment and memory management.

Here’s a breakdown of the key layers and components that make up a scalable Agentic AI Architecture :

1.🔸Decomposition
Break down complex systems by domain (e.g., Coding Agent, Data Agent), by cognitive capability (Reasoning, Planning, Execution), or by agent role (Planner, Executor, Memory Manager, Communicator).

2.🔸Communication
Enable message passing between agents using inter-agent protocols or A2A (Agent-to-Agent) orchestration.
Support both single-agent and multi-agent setups for small or distributed workflows.

3.🔸Deployment
Deploy agents in containerized or serverless environments using Docker or Modal.
Support orchestrators like CrewAI or AutoGen for collective intelligence in multi-agent workflows.

4.🔸Data & Discovery
Integrate knowledge bases (like vector databases for RAG), memory stores (FAISS, Redis, Pinecone), and APIs for dynamic data access.
Context is passed using Model Context Protocol (MCP) for structured and real-time reasoning.

5.🔸Testing & Observability
Validate workflows end-to-end, test reasoning logic, and evaluate performance under real conditions.
Monitor using Weights & Biases, LangFuse, and track metrics like latency and task success rate.

6.🔸UI & Style
Provide intuitive feedback loops through visualization layers, dashboards, and self-reflective modes.
Enable collaborative, proactive, and goal-driven reasoning among multiple agents.

7.🔸Security
Protect access with token-based authorization and data encryption.
Include Trust Layers for human-in-the-loop validation and Policy Enforcement for safe execution.

8.🔸Cross-Cutting Concerns
Handle configuration, secrets, and environment management.
Support flexible frameworks like LangChain, AutoGen, or CrewAI for runtime execution and modular design.

Agentic AI is the future of automation - where AI doesn’t just assist but collaborates and learns.

Save this post to understand the architecture that powers the next generation of AI systems

#AgenticAI

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AI agents are great at working alone. However, they can also be effective when operating within networks of collaboration.

The real power of AI lies in how these agents communicate, coordinate, and complement each other’s abilities.

Orchestration patterns define this collaboration which turns isolated agents into intelligent, goal-driven systems capable of solving complex tasks together.

Here are 6 orchestration patterns that power the next generation of AI systems :

1.🔸Sequential Orchestration
Agents work one after another - each passing its result to the next.
Perfect for structured, step-by-step tasks like data pipelines or analysis chains.

2.🔸Concurrent Orchestration
Multiple agents work in parallel, handling different parts of a problem simultaneously.
This boosts speed, scalability, and overall task efficiency.

3.🔸Group Chat Orchestration
Agents communicate in a shared conversation, refining ideas together.
Useful for brainstorming, collaborative decision-making, or generating consensus.

4.🔸Maker–Checker Loops
One agent creates or proposes; another verifies, corrects, or approves.
Ideal for maintaining accuracy, compliance, and quality assurance in workflows.

5.🔸Handoff Orchestration
Agents transfer control dynamically based on context or expertise.
Best suited for adaptive workflows like support systems or multi-step automation.

6.🔸Magnet Orchestration
A central “manager” agent dynamically assigns tasks to the right specialists.
Enables fluid, context-aware collaboration across multiple intelligent agents.

AI Agents are evolving fast and orchestration is what makes them powerful.

Learning how these patterns can help you build smarter, more collaborative AI systems can be a huge advantage.

#AIAgent

AI Agent Orchestration Patterns

📚LLMs in the Enterprise are finally getting the playbook they deserve… LLMs in Enterprise by Ahmed Menshawy and Mahmoud Fahmy provides practical guides for building real AI systems that operate at scale.

Most teams talk about LLMs in theory. This book focuses on execution. It bridges foundational concepts with the hands-on design patterns that matter when you are integrating models into production environments.

Here are the insights that stood out👇

1.🔸Enterprise LLM integration is a data architecture problem
The book breaks down how to design pipelines, tune retrieval, and structure data so models operate with consistency and low latency in real workloads.

2.🔸Scaling LLMs requires pattern-level thinking
They go deep on architectural patterns that reduce complexity, improve efficiency, and streamline deployment. This includes RAG frameworks, fine-tuning strategies, segmentation techniques, and evaluation patterns that teams often overlook.

3.🔸Performance is not just about bigger models
The authors show how to optimize model behavior with advanced inferencing engines, contextual model customization, and monitoring systems that keep applications predictable.

4.🔸Real enterprise value comes from operational rigor
Security, fairness, transparency, and accountability are not afterthoughts. They are part of the design process, especially when LLMs touch business workflows and customer data.

5.🔸AI teams win by mastering both concepts and impact
The flow of the book reflects the real enterprise lifecycle: Concept → Customization → Impact
A clear, structured way to think about turning LLM capabilities into business outcomes.

If you are building production AI systems, leading an LLM program, or preparing for the next wave of enterprise adoption, you should definitely get a copy of this book.

Enterprise AI is evolving fast. Understanding these design patterns early puts at a career advantage which allows you to shape the next generation of intelligent applications.

#LLM

Agentic AI vs Traditional AI: The Shift from Supervision to Autonomy

For years, Traditional AI has been like a skilled worker. It has been efficient, but dependent on human supervision.

Agentic AI, however, takes a different approach. It is a decision-maker that thinks, plans, and adapts without constant direction.

Let’s dive into what truly separates them 👇

Agentic AI: Smarter. Adaptive. Independent.

- Dynamic Learning: Perceives its environment, plans actions, and evaluates outcomes continuously.

🔸Goal-Oriented Thinking: Sets goals, creates sub-goals, and adjusts strategies like a human problem solver.

🔸Reasoning & Reflection: Learns from mistakes, refines decisions, and improves performance over time.

🔸Collaborative Agents: Multiple agents share context, delegate tasks, and synchronize for collective intelligence.

Traditional AI: Powerful but Limited.

🔹Static Learning Models: Train once, deploy, and manually retrain, with no real-time adaptability.

🔹Human-Dependent Execution: Waits for prompts or tasks, performs inference, and stops.

🔹Narrow Focus: Optimized for specific use cases without flexibility.

🔹No Self-Improvement: Requires human supervision for updates and tuning.

In Short:

- Traditional AI executes.

- Agentic AI thinks, plans, and evolves.

It’s the difference between automation and autonomy, a shift that’s redefining how AI systems operate, learn, and collaborate in the real world.

What other diffenreces have you noticed?

#AI

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