SCOTT TAYLOR - The Data Whisperer

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!

The Data Whisperer & Principal Consultant

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. 

Honestly have not been thinking too much about data recently 😎  


Princess Di Beach, Barbuda 🏝️

SCOTT TAYLOR

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Due to…ahem…local weather, yeah local weather 😎 I won’t be able to make it to Data Day Texas and will miss seeing a bunch of my data friends. 

BTW When is Data Day Antigua? 🇦🇬

Joe Reis Jordan Morrow Matthew Housley Jean-Georges Perrin Ole Olesen-Bagneux Sanjeev Mohan  Dylan Anderson  Ryan Dolley 🎯 Mark Freeman II  Tim Gasper Bill Inmon Winfried Adalbert Etzel  Alex Merced  enjoy the snow ❄️ 




#datastorytelling 



Jolly Harbour, Antigua 🇦🇬

If your hair is on fire every time you upgrade Apache Spark… 🔥

You’re not alone. For many teams, Spark upgrades set off a full on fire drill: chasing deprecated APIs, fixing dependencies, and validating data, all while keeping production running.

At AWS re:Invent they announced an AI-powered Spark upgrade agent for Amazon EMR that changes everything. It works inside your IDE to:

📈Analyze and modernize code
🛠️Fix build and runtime issues
✅Validate tests and data outputs

What used to take months of engineering effort can now be done in hours, or even minutes.

Less fire. 🔥 More warmth. ☺️

If you’re running Spark on Amazon Web Services (AWS) EMR, this is worth checking out.

Read all about it here : https://lnkd.in/gePZcTck

Malcolm Hawker

Thought Leader | CDO | Data Strategy | Master Data Management | Data Governance | Host of the CDO Matters Podcast

With over 25 years of experience in data and analytics, I am a recognized thought leader and advisor in the fields of Master Data Management (MDM) and Data Governance. I have helped some of the world's largest businesses to improve their enterprise information management strategies, and I enjoy sharing practical and actionable insights on how to leverage data as a strategic asset.

As the CDO for Profisee, I lead the global field MDM and Data Governance practice, and I am the host of the CDO Matters podcast, where I interview senior data leaders from various industries and domains. My mission is to raise the awareness and value of MDM and Data Governance to companies across the globe, and to empower them with the best practices and solutions to overcome their data quality challenges and achieve their business goals.

Head of Data Strategy

The concepts of control and ownership are foundational to data governance, but they don't work.  They never have.  

Why? 

Because control is the 𝐰𝐫𝐨𝐧𝐠 𝐦𝐞𝐧𝐭𝐚𝐥 𝐦𝐨𝐝𝐞𝐥.  

Control is inherently one-dimensional.  Data is not. 

The exact same data can be created in one context, consumed in another, reused for entirely different purposes, and introduce wildly different risks depending on who is using it and why. 

No single owner (outside the CEO), or centralized authority, can reasonably “control” all of those dimensions at once.

Data doesn’t behave like an asset you can lock in a vault. 

It behaves more like a shared language, where its meaning, value, and risk profile shift based on context.

That means effective governance isn’t about controlling data. It’s about 𝐨𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐧𝐠 𝐚𝐜𝐜𝐨𝐮𝐧𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐚𝐜𝐫𝐨𝐬𝐬 𝐜𝐨𝐧𝐭𝐞𝐱𝐭𝐬.

Any meaningful governance policy should consider multiple dimensions at once, including:

✅ the context in which data is created
✅ the context in which its being consumed (operational, analytical)
✅ the person or role responsible for creating it (the sovereign of the data)
✅ the person or process/system consuming it 
✅ the person accountable for the enforcement of its policies, by context

Governance programs that only focus their policies on only one of these dimensions will struggle.  

And sadly, most do. 

I understand why.  

It's hard enough to define, implement, and manage a robust set of governance policies for your data when you're only considering a single dimension.  

Doing the same for 5 dimensions becomes drastically more complex.  

Things get even more difficult when we don't quantify the business value of governance - and almost nobody does.  

But all is not lost.  

I'm extremely optimistic that AI will provide incredible value here. 

I'm confident that within the next two years, many of us will rely on it heavily to start augmenting (and yes, even automating) our governance policies so they honor all of these various contexts or dimensions.  

The software systems that define and implement these policies, most especially data catalogs and MDM platforms - must start to become more context aware, and context-driven.

There is much work do be done here, but we can figure this out. 

In the interim, we desperately need to 𝐜𝐡𝐚𝐧𝐠𝐞 𝐨𝐮𝐫 𝐦𝐞𝐧𝐭𝐚𝐥 𝐦𝐨𝐝𝐞𝐥, and all of our 'best practices'.   

A great starting point here would be to read my book, the Data Hero Playbook - which is focused entirely on the mindset data professionals need to succeed in an era of AI.  

Else, you can stay tuned to this feed, where I'll be talking more about this topic throughout 2026.  

#datagovernance #dataquality #datamanagement

CDO | Author | Keynote Speaker | Podcast Host

Most data governance programs don’t fail because of bad frameworks.

They fail because the business never experiences value.

If your governance function is struggling to gain traction, here are 6 actions data leaders can take now to change the narrative:

1️⃣ Stop governing data. Start governing decisions.  

Anchor governance to specific business decisions, not abstract data domains.  For example - asking the question "what policies are needed to support the onboarding of new suppliers in a way that optimally supports your business process?"  is fundamentally different than asking   "what does 'good' supplier data look like?" 

2️⃣ Name real customers, and fire the imaginary ones.  

Who is the actual individual, who is responsible for a business KPI, who benefits from this policy?  If you can’t identify who benefits from a given policy, don’t create it.

3️⃣ Tie every policy to an business KPI that executives already care about.

 Risk reduction, speed to market, cost avoidance, revenue protection.

4️⃣ Design governance like a product, not a control system.  

What customer problem are you trying to solve, and how does a specific governance policy help to solve that problem?   If adoption of your program isn’t happening, the problem is a lack of customer focus, not compliance.

5️⃣ Measure success by behavior change, not artifacts produced.
 
Policies documented ≠ value delivered.

6️⃣ Hold the data team accountable for adoption.  Yes, the data team is accountable, not the business.  

Governance that isn’t used is a data team failure, not a business one.  We exist to make our customers successful, and if they arent supporting a governance effort - that's because WE havent adequately articulated its value. 

Governance doesn’t earn trust by being “right.”

It earns trust, and gains adoption, by being useful.

#ai #datagovernance #datamanagement

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Malcolm Hawker

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Malcolm Hawker

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Engagements

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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);}SCOTT TAYLOR - The Data Whisperer's LinkedIn Performance

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Performance ScoreSCOTT TAYLOR - The Data Whisperer's LinkedIn Performance