Jovita Tam

Kyndryl

Kyndryl (NYSE: KD) is the world’s largest IT infrastructure services provider, a 90,000-person 'start-up' with more than 3,000 patents. The company provides applications, data, AI, cloud, core enterprise, digital workplace, security, and other related services. The company has a global base of customers — including 75 of the Fortune 100. 

Kyndryl’s Chief Privacy and Regulatory Office (CP&RO) directs the company’s privacy and regulatory vision, compliance strategy, and oversees the organization’s privacy and regulatory programs.

Chief Privacy and Regulatory Office Global Program Leader

Big Data LDN

Member of the steering group responsible for the curation of the biggest data and analytics conference (15K+ attendees in 2023) in the United Kingdom. https://bigdataldn.com/steering-group/

Big Data LDN (London) is the UK’s leading free to attend data & analytics conference and exhibition, hosting leading data and analytics experts, ready to arm you with the tools to deliver your most effective data-driven strategy.

💻Over 180 leading technology vendors and consultants. 

🗣️ 300 expert speakers in 15 technical and business-led conference theatres 🎭,with real-world use-cases and panel debates.

Network with your peers and view the latest product launches & demos.

The event is held annually at the London Olympia with over 13k attend

All about the conference & exhibition https://bigdataldn.com

Steering Committee

FinTech Connector 

FinTech Connector is a global online and real-world professional community that connects fintech and financial services experts, executives, and entrepreneurs to collaborate and cultivate fintech innovation.

To learn more and join this exclusive, invite-only community, please DM.

Community Partner UK

illumour Limited

illumor, a boutique consulting firm specializing in building better companies. Illumour partners with transformative leaders to create business value, build better companies, and a better world.

Founder, Principal Consultant

Mercy University

Customer Experience Executive Education Advisory Board Member

Advisory Board Member

Followed my curiosity, rediscovered alignment and balanced between effort & ease.

Recharge: Self-Imposed Sabbatical

Exiger

Advisory - Data Governance / Data Analytics Engagements 

Topside Advisors, LLC

Managing Director, General Counsel - M&A

Theseus Advisory Solutions

Theseus Advisory was a private company providing strategic advice to C-suite executives.

Founder, Strategy Consultant / C(x)O Advisor

Centerboard Group

VP, Investment Banking

Ready to simplify, scale, and innovate responsibly? 🙌🏻

I’m a curious business-focused data and AI advisor with a unique blend of expertise in law and data, I’m passionate about leveraging Data Strategy, Data Governance, and responsible AI and AI governance to drive ethical, data-driven decision-making. With a strong foundation in regulatory compliance, data privacy, and AI ethics, I empower organizations to harness the transformative power of data while navigating regulatory landscapes.

I thrive on translating complex challenges into strategic opportunities that fuel digital transformation and promote cross-functional collaboration. Examples include building global processes and leading globally diverse teams (20+) of data scientists, architects, attorneys, and management consultants.

Whether advising /delivering on technology implementation or crafting a comprehensive data strategy, I bring a business-centric and customer-centric approach, helping companies make smarter, more informed decisions that align with their values and objectives.

Global Experience 🌎 : 
📍North America: United States (New York, San Francisco), Canada (Toronto) 
📍Europe: United Kingdom (London) 
📍Asia: Hong Kong, PRC, Macau 
📍Middle East: Turkey (Istanbul) 
 
"The best way to predict the future is to invent it." – Alan Kay

Let’s chat. ☕️

Email: jovita@jovitatam.com; jovita@illumour.com

Hashtag: #MusingsWithJovitaTam

Chief Privacy and Regulatory Office Customer Experience Program Lead

Do the Data

Founder

Deloitte

Data Analytics Manager

Novartis

Lead a team of data analysts and oversee the design and implementation of healthcare data analytics projects

Develop and maintain healthcare data management systems, ensuring data accuracy, completeness, and security

Develop and implement data analysis plans to support clinical and operational decision-making processes

Collaborate with stakeholders across the organization to identify and prioritize business intelligence needs, and ensure that data analytics projects align with the overall strategic goals of the organization
Analyze large datasets using statistical and data visualization tools to identify trends, patterns, and insights that can be used to drive business performance

Ensure compliance with data privacy and security regulations such as HIPAA, and work with legal and compliance teams to address any data-related issues

Develop and deliver presentations to communicate complex data analysis to non-technical stakeholders, including executive leadership, and make recommendations for process improvements based on data-driven insights

Stay up-to-date with the latest trends and technologies in healthcare data analytics and provide guidance on how the organization can leverage them to improve performance and patient outcomes

Data Analytics - Turning Coffee into Insights, One Caffeine-Fueled Query at a Time! | Healthcare Data | Financial Expert | Driving Business Growth | Data Science Consultant | Data Strategy

As a data analytics professional, I have a passion for turning raw data into meaningful insights that drive business decisions. With my experience in data visualization, statistical analysis, and data modeling, I can help your organization harness the power of data to optimize operations, improve customer experiences, and achieve your strategic goals.

🔍 Discovering Insights

My expertise lies in exploring complex data sets, uncovering hidden trends and patterns, and providing actionable insights that support informed decision-making. I can help you answer questions like "What are our customer's buying habits?" or "What factors are impacting our sales performance?", and use data to guide your business strategy.

🎨 Data Storytelling

I believe that data storytelling is an art form, and I love using creative and engaging data visualizations to communicate complex information in a way that's easy to understand. Whether it's through interactive dashboards or custom reports, I can help you see your data in a new light, and use it to tell the story of your business.

🧠 Continuous Learning

I'm a lifelong learner, and I'm always exploring new data tools and techniques to stay at the forefront of the industry. Whether it's machine learning, artificial intelligence, or predictive analytics, I'm excited to apply the latest advancements to help you gain a competitive edge.

🤝 Let's Connect

If you're looking for a data analytics professional who can help you unlock the power of your data, let's connect! Whether you're a startup or a large corporation, I'm excited to collaborate with you and help you achieve your business goals. Feel free to reach out at Ravenaravs@gmail.com

Ravena O

Python for Data Professionals: Why It’s the Preferred Choice

📍 Why is Python the go-to language for data roles?

Python isn’t just a programming language—it’s an essential tool for data professionals. Its simplicity, versatility, and vast ecosystem of libraries make it a top choice for handling complex data challenges with ease.

📍 Where does Python fit in the data ecosystem?

🔴 Data ingestion, cleaning, and transformation
🔴 Storage and orchestration
🔴 Visualization and reporting
🔴 Machine learning and model deployment

From building ETL pipelines to creating interactive dashboards, Python empowers data teams at every level.

📍 Why choose Python over Scala/Java?

While Scala and Java are excellent for large-scale data processing and system robustness, Python stands out for its flexibility. It’s a universal tool, adaptable across various data domains, making it the preferred choice for many professionals.
📍 Key data roles where Python is invaluable:
🔴 Data Analytics – Analyze data using Pandas, NumPy
🔴 Data Visualization – Create stunning visuals with Seaborn, Matplotlib, Plotly
🔴 Machine Learning – Build models with Scikit-learn, TensorFlow, Keras
🔴 Web Scraping – Extract data using Scrapy, BeautifulSoup, Selenium
🔴 Data Engineering – Implement pipelines with Spark, Kafka, Flink
🔴 Databases – Work with SQLite, PostgreSQL, MongoDB, MySQL

📍 Top free platforms to upskill in Python:
 - realpython.com
- pythonprogramming.net
- datacamp.com (free basic courses)
- w3schools.com/python
- https://lnkd.in/gCDhJvA2
- https://lnkd.in/grqYWfGW

 📍 Follow these Python experts for insights: 🐍

Matt Harrison, Khuyen Tran, Guido van Rossum, Dan Bader

Python remains an essential skill for data professionals—whether you're analyzing, engineering, or visualizing data, it has the tools to get the job done!

CC:Brij Kishore Pandey

Ravena  O

Ever wondered how companies actually build AI systems powered by LLMs?

It’s not just about calling an API and hoping for magic.

There’s an entire tech stack behind the scenes — and once you understand it, you’ll see how everything connects.

Let’s break it down step by step 👇

🟡 Step 1: The Brain (LLMs)
 Think of GPT, Gemini, or Claude as the intelligence layer.
 They understand, reason, and respond — but they need context, data, and direction.

🟡 Step 2: The Connectors (Frameworks)
 Frameworks like LangChain or LlamaIndex act as plumbing between your model and your tools.
 They let you combine the LLM with data sources, APIs, and logic — no need to reinvent the wheel.

🟡 Step 3: The Memory (Vector Databases)
 LLMs forget fast.
 That’s where databases like Pinecone, Weaviate, and Chroma come in — they store “embeddings” (a fancy word for text turned into numbers).
 It’s like giving your AI Google Search for your own data.

🟡 Step 4: The Fuel (Data Extraction)
 No data, no intelligence.
 Tools like Crawl4AI and FireCrawl pull information from PDFs, websites, and documents — then clean it so your AI can actually use it.

🟡 Step 5: The Open Source Edge
 Don’t want to rely on expensive APIs?
 Use open models from Hugging Face or run local ones via Ollama.
 Freedom + privacy + control = ✅

🟡 Step 6: The Language of Numbers (Embeddings)
 Before your text hits the vector DB, it must be converted into numerical form.
 That’s where OpenAI Embeddings or SBERT come in — enabling true semantic understanding.

🟡 Step 7: The Quality Gate (Evaluation)
Now test it.
Use tools like Giskard or Trulens to check for accuracy, hallucinations, and relevance.
Because every smart system needs smarter feedback loops.

💥 In short:
 1️⃣ Pick your LLM
 2️⃣ Connect it via a framework
 3️⃣ Feed it structured data
 4️⃣ Store it in a vector DB
 5️⃣ Add embeddings for meaning
 6️⃣ Use open tools for flexibility
 7️⃣ Evaluate relentlessly

This blueprint can help your team go from idea to intelligent system.

 🔁 Repost if you want your network to build smarter with AI.

CC: Rahul Agarwal

Ever wondered how Agentic AI actually works behind the scenes?

Here’s a simple breakdown of how an AI agent transforms raw input → into intelligent output.

 Perfect for anyone curious about how autonomous AI systems really think. 

🟠 Input Sources
 Agentic AI starts by collecting data from multiple channels — knowledge bases, user queries, APIs, logs, and web data.

🟠  AI Processing
Once data is gathered, the agent gets to work — analyzing the query, reasoning through context, retrieving memory, planning tasks, selecting tools, and managing the entire flow.

🟠 3. Action Layer
 With a clear understanding of context, the agent performs real-world actions — making decisions, executing tasks, fixing errors, collaborating with other agents, and even scheduling future steps.

🟠 4. Output
 Finally, it delivers a response that’s accurate, relevant, and context-aware — the hallmark of truly autonomous intelligence.
That’s what sets Agentic AI apart from traditional chatbots or rule-based systems.

 💾 Save this post for your next deep dive into AI systems!

CC: Prem Natarajan

freeCodeCamp  recently released a comprehensive AI Engineering Roadmap — and it’s surprising how many people missed it.

It’s not just another tutorial list.
It’s a reality check for anyone serious about building a career in AI.

Here are some key takeaways worth reflecting on 👇


🔴 1. Math is non-negotiable
Linear algebra, calculus, and probability form the backbone of every AI system.
If you can’t understand gradients, optimization, and loss functions — you’ll struggle to build anything meaningful.

🔴 2. Python remains the foundation
From TensorFlow and PyTorch to NumPy and Pandas — this ecosystem drives modern AI.
Fluency in Python isn’t optional; it’s expected.

🔴 3. LLMs aren’t everything
While everyone is chasing ChatGPT replicas, real impact (and opportunity) lies in building domain-specific models that solve practical business problems.

🔴 4. Depth beats credentials
Building and deploying CNNs, RNNs, and LSTMs will teach you far more than any certificate ever could.

🔴 5. Data work is underrated
Data cleaning, transformation, and validation consume most of an AI engineer’s time — and mastering these makes you truly valuable.

🔴 6. Prompt engineering isn’t the destination
It’s a useful skill — but true AI engineers design systems, not just prompts.

🔴 7. Deployment is the differentiator
A model sitting in a Jupyter notebook has no value.
Productionization is where engineering begins.

🔴 8. Generative AI goes beyond LLMs
Understanding GANs, VAEs, and foundational architectures separates professionals from hobbyists.

🔴 9. System design matters
AI solutions must scale. Knowing Kubernetes, Docker, and distributed systems is critical for real-world impact.

🔴 10. Ethics and bias awareness are essential
As AI adoption accelerates, responsible design and fairness aren’t optional — they’re requirements for trust and compliance.



In short:
The roadmap reminds us that AI engineering isn’t just about chasing trends — it’s about mastering fundamentals, building production-ready systems, and thinking responsibly about the technology create

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Jovita Tam

Ravena O

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The Battle

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Ravena O

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Engagements

Likes

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