SCOTT TAYLOR - The Data Whisperer

MetaMeta Consulting

MetaMeta: "It's about what it's about"

We are a strategic marketing consultancy focused on partnering with innovative Tech Brands that seek to change the nature of enterprise data management - and want help to tell that story.  Our experience is rooted in helping countless business executives understand the strategic value of proper data management.  Data has never been hotter but Tech Brands can't always break through the clutter.  We are proponents of compelling messaging that is also technically accurate.

The Data Whisperer & Principal Consultant

DataVengers

Lead member of the DataVengers, a coordinated group of independent content creators amplifying and enhancing the data event experience

DataVenger

Technics Publications

NEW BOOK: TELLING YOUR DATA STORY - Data Storytelling for Data Management 

The Data Whisperer's practical guide to explaining and understanding the strategic value of data management.

Available directly from Technics Publications and also on Amazon

Author - TELLING YOUR DATA STORY - Data Storytelling for Data Management

Thinkers360

Top 10 Ranking Thought Leader & Influencer
Big Data 
Business Strategy
Digital Transformation

Top 10 Ranking Thought Leader & Influencer - Big Data | Digital Transformation | Business Strategy

DATAVERSITY

Very excited to be chosen for the Opening Keynote at Enterprise Data World in San Diego March. 
"Data Management as the Foundation of Digital Transformation"
For a discount on registrations use promo code DATAWHISPERER

Keynote Speaker - DGIQ

InfoCepts

Helping to Bridge the Story Gap Between the Business and Data - Events, blogs, videos and all sort of content.

InfoCepts Partner

ThinkLinkers

ONE WORLD. ONE PEOPLE. ONE DATA.

A collaboration between ThinkLinkers and Scott Taylor - The Data Whisperer, born out of love for #Data, #Storytelling and knowledge sharing, we’re excited to present:
M D M - Master Data Marathon

Host for the Data Marathon 5.0

Cloudera

Podcast Host

Dun & Bradstreet

Reimagined, redefined and repositioned Dun & Bradstreet’s global market position for data management solutions supporting Master Data resulting in a common set of vocabulary, a clear consistent voice in the marketplace and a visionary business accessible narrative.

D&B Master Data Evangelist : Market Development & Innovation

InfoCommerce Group Inc.

Specialized Consultant - Master Data Productization Opportunities

Let me help you TELL YOUR DATA STORY 

I can be your voice for Data Management 

Partnering with Enterprises and innovative Data Brands that want to change the nature of data management - and help them tell their story!

As **The Data Whisperer** I help "calm data down" 

I am a firm believer in “making your data do the work,” and have enlightened countless business executives to the value of proper data management by focusing on the strategic rationale and business alignment rather than technical implementation and system integration. 

I'm more of the strategic WHY than the technical HOW. 

I have spent over two decades solving master data challenges with large global enterprises as well as guiding Tech Brand owners to leverage their reference data and taxonomy assets.  

In a variety of strategic marketing, GTM, innovation and consulting roles, I have worked with some of the world’s most iconic business data brands including Dun & Bradstreet, Nielsen, Microsoft, Kantar, NPD as well as start-ups such as Qoints and Spiceworks.  

I'm an avid business evangelist and original thinker, and continually share my passion for the strategic value of foundational data through public speaking opportunities, blogs, videos, whitepapers, podcasts and all forms of marketing content. 

Highly effective strategic Subject Matter Expert/SME working directly with sales and alliances to reinforce the value of data management.

If you are an innovative Tech Brand looking to change the very nature of data management let me help you tell a better story!

MetaMeta: "It's about what it's about"

We are a strategic marketing consultancy focused on partnering with innovative Tech Brands that seek to change the nature of enterprise data management - and want help to tell that story.  Our experience is rooted in helping countless business executives understand the strategic value of proper data management.  Data has never been hotter but Tech Brands can't always break through the clutter.  We are proponents of compelling messaging that is also technically accurate. 

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

No alternative text description for this image

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

taplio

The Battle

SCOTT TAYLOR - The Data Whisperer

Greg Coquillo

Followers

Posts

Engagements

Likes

Performance ScoreGreg Coquillo's LinkedIn Performance

Ready to grow your LinkedIn presence?

taplio

The Battle

SCOTT TAYLOR - The Data Whisperer

Greg Coquillo

Followers

Posts

Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Greg Coquillo's LinkedIn Performance

Ready to grow your LinkedIn presence?

Performance ScoreGreg Coquillo's LinkedIn Performance