Kate Strachnyi

DATAcated™ | Branding | DataIQ100 | Marketing | Media | ColorWise | DataVenger

YAY - I'm so excited that you saw my profile and wanted to get to know me better!As the founder of DATAcated, I help companies amplify their brand and expertise in artificial intelligence, machine learning, and data science.  I'm a content creator with over 300k followers across LinkedIn, YouTube, Instagram, and other platforms. I also run a DATAcated Plus program with 35+ influencers that can be hired to 'make a splash' on social media.  Oh yea, I also manage the DATAcated Expert / Speaker Hub where we have over 50 engaging speakers and experts that you can hire for conferences, webinars, books, blogs, and more.I have been recognized as a LinkedIn Top Voice in Data Science and Analytics and as a DataIQ USA100. I am also the author of ColorWise: A Data Storyteller's Guide to the Intentional Use of Color (published with O'Reilly). Connect with me on social media and explore the DATAcated website for more resources and opportunities. datacated.com

Founder

DATAcated® to helping clients share their stories through content creation and amplification. Providing custom 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 for companies focused on innovating in the data science, machine learning, and artificial intelligence space. 

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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Kate Strachnyi

Malcolm Hawker

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The Battle

Kate Strachnyi

Malcolm Hawker

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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);}Malcolm Hawker's LinkedIn Performance

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