Bridging the gap between data and strategy ✦ Lead Data Strategy Consultant @ Redkite ✦ R Programmer ✦ Policy Nerd

Okay I probably have your attention here for about 10 seconds, so I’m going to be quick:

My niche is navigating the complex data world for organisations to help them deliver on their strategy

Basically, I bridge the gap between data and strategy

Want to know more? Here is how I do it:

💡 Learned as a strategy consultant what matters and what doesn’t
🎤 Storytell to help clients implement effectively (how do you properly engage the audience and infuse that story with the key takeaways/ implications)
💻 Use my R coding and strategy consulting experience to bridge the data scientist's "get lost in the data" approach with the "tell me what I need to know on one page" approach of the business executive
★ Live off my passion to see through positive outcomes, be that at work, on my blog or even when helping a friend

So why should you care?

1) I post my best tips 3-5x per week on LinkedIn
2) I make it relevant to you, as a student, business professional, executive, or whomever. My goal is to share relevant stuff with my audience
3) I try to be unique in what I share and deliver, as few people have the combination of experience I boast
4) I value feedback and input from all others out there. I pride myself on learning from others' perspectives so please add it to my content!

Also some of my other Outputs:
Medium - https://dylansjanderson.medium.com/
Blog Website - https://www.policyinnumbers.com/
Election App - https://danderson.shinyapps.io/Canada-Electoral-Model/
Academic Journal Article - https://www.cambridge.org/core/journals/canadian-journal-of-political-science-revue-canadienne-de-science-politique/article/tipping-point-of-a-strategic-vote-when-does-an-individual-vote-strategically/7FEDD21C59DF03AC15311CD13080881B

Dylan Anderson

Data teams are losing their audiences due to its increasing complexity

Unlimited tools, rising costs, increasing complexity, poor ROI. People want data/ AI, but how can you keep up?

As advanced as our tools have become, there is a growing gap in understanding what data really means for business

Data warehouses, lakes, and AI algorithms are complex, and the people expected to use them often lack the necessary guidance or expertise. After the hype wears off, senior leaders go from the excitement of possibility to the overwhelming reality of implementation.

For data leaders, this lost understanding is a huge problem:
🚫 Hinders potential investment
📉 Reduces organisational data literacy
🛑 Makes it harder to deliver new projects
💸 Complicates the ROI calculation of data initiatives
🧱 Gets in the way of creating a more data-centric culture

There is no silver bullet to solve this, but companies need to recognize why they are in this position, and steer the ship back in the right direction. Instead of thinking with a short-term mindset, they need to plan for the long-term:
🎯 Approach data strategically (including new trends, tools, and fads)
🧭 Understand the actions to operationalise the data strategy
🏗️ Hire and build internal capabilities for the long-term
⚙️ Shore up the foundations (e.g., data modelling, governance, platform, etc.)
🧹 Declutter the tooling repertoire and be smart about new technology choices
🔍 Constantly reassess existing issues and uncover root causes

Data is going to continue to get more complicated.

Are you ready for it?

Bridging the gap between data and strategy ✦ The Data Ecosystem Author ✦ Data & AI Leader ✦ Speaker ✦ R Programmer ✦ Policy Nerd

Your organisational structure underpins everything you do

But companies actively ignore it, and data people don't seem to mind

Here are three organisational structures to understand as a data person

🎯 𝐂𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐬𝐞𝐝 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 – The centralized structure is the most basic view of data where all data functions are consolidated within a single, central team or department. They are responsible for all data activities, from storage to processing & transformations to distributing insights. Usually companies start with this type of structure given its simplicity and the need to have all the data operations in one place.

⚙️ 𝐃𝐞𝐜𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐬𝐞𝐝/ 𝐌𝐞𝐬𝐡 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 – A decentralised data structure distributes data ownership, management, and activities across various domain-specific teams within the organisation. Each team is responsible for building its own data products and solutions, and managing all other data capabilities within that. Companies take a decentralised approach to develop functional domain experience (e.g., marketing, finance) within data teams and to build business-relevant products in a more agile way without the bureaucracy that centralised teams can bring.

🛠️ 𝐇𝐲𝐛𝐫𝐢𝐝/𝐇𝐮𝐛 & 𝐒𝐩𝐨𝐤𝐞 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 – The hybrid approach (or hub & spoke model) is the most common organisational structure. It combines elements of both centralized and decentralized structures, usually having a central hub for infrastructure and governance with spokes (individual business units) managing domain-specific data activities like analytics.

Check out my article that digs into this in a lot more detail, including the pros, cons, best approaches, and considerations for each of these structures

AI is the first time we are seeing data become truly democratised
 
And that is a competitive advantage, if you are doing it correctly
 
This was the topic I addressed in the latest episode of DQE, Data Quality Everywhere's podcast "Let's Talk Data”: making data quality a competitive advantage with AI
 
Here’s why data quality matters and why you should listen to this episode if you have any of these goals:
 
𝐂𝐫𝐞𝐚𝐭𝐞𝐬 𝐚 𝐧𝐞𝐜𝐞𝐬𝐬𝐚𝐫𝐲 𝐟𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐀𝐈 𝐚𝐧𝐝 𝐚𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐚𝐥 𝐭𝐨𝐨𝐥𝐬 🏗️
- Reduces the cost of model development and maintenance
- Makes it easier to train, fine-tune, and deploy models effectively
 
𝐁𝐮𝐢𝐥𝐝𝐬 𝐭𝐫𝐮𝐬𝐭 𝐚𝐧𝐝 𝐚𝐜𝐜𝐞𝐬𝐬 𝐚𝐜𝐫𝐨𝐬𝐬 𝐭𝐡𝐞 𝐨𝐫𝐠𝐚𝐧𝐢𝐬𝐚𝐭𝐢𝐨𝐧 🔐
- Increases user adoption (think about how/ why ChatGPT took off)
- Reduces manual fixes and furthers automation
 
𝐃𝐫𝐢𝐯𝐞𝐬 𝐜𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐜𝐲 𝐚𝐜𝐫𝐨𝐬𝐬 𝐭𝐞𝐚𝐦𝐬 🎯
- Aligns your KPIs and definitions so everyone speaks the same language
 
How does AI do this?
🔍 Deduplicate data to eliminate redundant records
👁️ Provides better observability that captures business logic and context
📖 Clear data documentation and explanation (because people are lazy and don’t do this)
📐 Standardisation across sources for KPIs, metrics and processes
🔧 Ongoing data maintenance and compliance
 
Before AI, being data-driven meant coding. Even BI tools and dashboards require some level of technical skills to extract real insight
 
With a high barrier to entry, data stays siloed with the people who could query it (or stuck in Excel spreadsheets)
 
With AI, that's changing and natural language interfaces are opening up data to everyone.
 
But that only works if you set the right foundation. AI doesn't fix bad data, it amplifies it.
 
🎧 Listen to the full episode for more on how to make data quality your competitive advantage in the age of AI. Great to partner with DQE, Data Quality Everywhere on this one and check out their continuing podcast series on Data Quality, AI and what is next in data!

#ad

If you build it, they will come…user feedback that is, not users 😯

In the end your dashboard isn't for you, it's for the user! 

And their feedback isn't just advice; it's actionable intelligence. So here are some tips:

👂 𝐋𝐢𝐬𝐭𝐞𝐧 𝐛𝐞𝐟𝐨𝐫𝐞 𝐝𝐞𝐟𝐞𝐧𝐝𝐢𝐧𝐠 – Your dashboard may be your baby, but your baby needs to grow up. And when feedback comes in, absorb it before formulating a counter-argument

🔎 𝐒𝐞𝐞𝐤 𝐨𝐮𝐭 𝐭𝐡𝐞 𝐬𝐢𝐥𝐞𝐧𝐭 𝐦𝐚𝐣𝐨𝐫𝐢𝐭𝐲 – Only a fraction of users voice their opinions so find ways to capture input from others through surveys or one-on-one chats.

💡 𝐏𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐳𝐞, 𝐝𝐨𝐧’𝐭 𝐩𝐚𝐧𝐢𝐜 – Not all feedback needs immediate action. Categorize suggestions into 'Now,' 'Soon,' and 'Later' buckets based on urgency and impact.

🔄 𝐈𝐭𝐞𝐫𝐚𝐭𝐞, 𝐝𝐨𝐧'𝐭 𝐨𝐯𝐞𝐫𝐡𝐚𝐮𝐥 – Small, frequent updates are better than rare, seismic shifts. Your users will thank you for not changing everything at once.

📏 𝐌𝐞𝐚𝐬𝐮𝐫𝐞 𝐭𝐡𝐞 𝐢𝐦𝐩𝐚𝐜𝐭 – Before and after making changes, look at metrics like user engagement and task completion times. Did your tweaks lead to improvements?

⚙️ 𝐒𝐢𝐦𝐩𝐥𝐢𝐟𝐲 𝐭𝐡𝐞 𝐂𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐨𝐧𝐬 – Explaining the connections between databases and dashboard tools goes a long way in creating trust. Using tools like Orchestra (which are drag and drop, low-code) to do this make it that more relatable for stakeholders

🗂️ 𝐃𝐨𝐜𝐮𝐦𝐞𝐧𝐭, 𝐝𝐨𝐜𝐮𝐦𝐞𝐧𝐭, 𝐝𝐨𝐜𝐮𝐦𝐞𝐧𝐭 – Keep a log of all feedback and changes to track your progress and provide insights for future projects.

🙏 𝐀𝐜𝐤𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐚𝐧𝐝 𝐭𝐡𝐚𝐧𝐤 – Remember to thank the feedback-givers. Even a simple "Thank you for your input!" goes a long way in building a positive feedback culture.

So, how do you handle feedback for your dashboards? Any other tips?

The best thing about data is the conversations everybody wants to have to make things better

One of my favourite topics to discuss is data quality and its recent relevance in this new AI world

Teams need to take the time to think about this intersection in more detail because they are missing out

…And trust me, 80-90% of the organisations I work with aren’t doing this. There are so many data quality quick wins they can invest in to get them to the next level, if only they dug into it further and spent some time really thinking about what works!

 DQE, Data Quality Everywhere released their latest “Let’s Talk Data” podcast, where I had these great conversations with Philippe BOULANGER, PhD. It was a ton of fun and I recommend a listen for anybody wanting to learn more about the intersection of data quality and AI!

Open article: Ep2 | Data quality: How AI can turn data quality from a challenge into a competitive advantage by smartlink.ausha.co

Ep2 | Data quality: How AI can turn data quality from a challenge into a competitive advantage

I love data science. But, my story is a bit different. 

I began as an Excel-Guru / Mechanical Engineer. Not a traditional background for a data scientist by any means. 

I used Excel until I reached its limits. 

I found R, and the rest is history:

- I began learning R, which led to applying data science to business 

- By applying data science, my team went from $3M/year to $15,000,000 per year

- I began consulting, which led to trainings all over the world

- I found my passion for teaching, and made a company out of it (Business Science!)

Since finding data science, I haven't worked a day in my life. It's been amazing. 

Let me know if you'd like to learn R, Python, or just want to chat data science.

Matt Dancho

This is what a data science team looks like in 2026. Not a team. A folder.

8 departments. 39 agents. Built in Python.

Every role is a markdown file.

Every department is a folder.

Every agent has memory, tools, and a job to do.

But here's what nobody is saying:

Agents don't own results. People do.

You still need one person who is accountable for what the agents produce.

That person is not a prompt engineer.

That person is a AI data scientist who knows how to build the system, ship it, monitor it, and tell the business what it means.

A year ago this required a team of 20.

Now it requires one Track 2 data scientist with the right portfolio.

The question is whether that person is you.

🚨 I'm running a free live workshop this week showing exactly how to build and ship AI / DS systems like this in Python.

Inside, you'll get:

• My Generative AI + Data Science Process
• Applied to a Business Problem (not "toy" data)
• Actual Python code + AI Agents

Used across teams tied to MRM McCann / S&P Global / Apple

👉Registration Link (500 seats): Comment “PORTFOLIO” and I'll share a registration link in the 1st comment below

Generative AI, Data Science, Python, and Business (ROI). Join my next live AI workshop (free).👇

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80% of data scientists say they want to build AI agents.

Almost none of them can answer this question:

Which agentic pattern should you actually use?

There are 7. And picking the wrong one breaks your entire workflow.

Here's the quick breakdown:

1. Parallel — multiple agents run at the same time. Use when tasks are independent. Faster output.

2. Sequential — agents run one after another. Use when each step depends on the last. More reliable.

3. Loop — agent repeats until a condition is met. Use for evaluation, retry logic, quality checks.

4. Router — one agent decides which agent handles the task. Use when inputs vary widely.

5. Aggregator — multiple inputs feed into one agent. Use when you need a single synthesized output.

6. Network — agents talk to each other freely. Use for complex reasoning tasks.

7. Hierarchical — a planner agent coordinates worker agents. Use when you need to scale.

That last one — hierarchical — is what Cursor just used to write 1 million lines of code autonomously in one week.

It's also the pattern that separates Track 1 from Track 2 data scientists.

Track 1 knows what an AI agent is.

Track 2 knows which pattern to deploy — and can ship it in Python before the meeting ends.

🚨 If you’re worried about data science career death in 2026… 

I have a live workshop that will help you shift to Track 2 (AI/DS Systems Builder). 

Inside my live workshop, you'll get:

• My Generative AI + Data Science Process
• Applied to a Business Problem (not "toy" data)
• Actual Python code + AI Agents

👉Registration Link (500 seats): Comment “PORTFOLIO” and I'll share a registration link in the 1st comment below

My 2026 AI data science stack:

1. Python ($0)
2. Pandas ($0)
3. Scikit Learn ($0)
4. LangChain ($0)
5. LangGraph ($0)
6. OpenAI API ($1/month)

You literally can grow into a $200,000 career for less than $12/year.

I’m teaching it live (free):
👉 Comment “PORTFOLIO” and I'll share a registration link in the 1st comment below ￼

Harvard just open-sourced its entire ML Systems curriculum.
Free. Public. 6 pillars. Hundreds of pages.

And it won't get most data scientists any closer to a $150K AI role.

Here's why.

The book covers:

1. System Design
2. Data Engineering
3. Model Deployment
4. MLOps and Monitoring
5. Edge AI
6. Responsible AI

That's genuinely excellent material. Prof. Vijay Janapa Reddi built something worth bookmarking.

But here's what I've watched happen with 7,500 students over 8 years:

The ones who read everything and built nothing stayed stuck.

Lannon didn't get his $150K AI Engineer role because he read more.

He built a RAG Copilot in Python. Walked into the interview. Opened his laptop. Showed them the system running.

They hired him on the spot.

Heinrich didn't get Lead GenAI Engineer with a 20% raise because he studied deployment theory.

He went through 8 live clinics and walked in with a portfolio of working systems no other candidate had.

Knowledge is not the bottleneck.

Shipping is.

The gap between Track 1 and Track 2 data scientists is not what they know.

It's what they've built and can prove.

Bookmark the Harvard book. Read it. It's good.

And then ask yourself: what have I actually shipped this month?

🚨 If you want to go from reading about AI to building systems companies actually use —

I have a free live workshop this week.

Inside my live workshop, you'll get:

• My Generative AI + Data Science Process
• Applied to a Business Problem (not "toy" data)
• Actual Python code + AI Agents

Used across teams tied to MRM McCann / S&P Global / Apple

👉Registration Link (500 seats): Comment “PORTFOLIO” and I'll share a registration link in the 1st comment below

In 2020, AI couldn't do your job for 30 seconds.

In 2026, it can do it for 7 hours straight.

You have one year before that number doubles. 

Here's how to prepare:

This isn't a prediction. There's a chart from Epoch AI that proves it.

From 2020 to 2024 — flatline. AI couldn't complete meaningful engineering or data tasks.

Then 2025 hits. The curve goes vertical.

And the tasks that crossed the line first?

- Train a classifier
- Fix bugs in Python libraries
- Run data analysis
- Build basic models

That's not "AI might take over someday."

That's your Tuesday.

Here's what makes this chart terrifying for Track 1 data scientists:

The curve doesn't plateau. It accelerates.

GPT-5 handles 3.5 hours of human work. GPT-5.2 handles 7. The next model won't stop at 14.

At some point — soon — the entire Track 1 data science workflow fits inside what AI completes before lunch.

Clean data. Build model. Write report. Done. Automated.

So the only question that matters is:

Are you building things AI replaces — or are you building the systems that deploy AI?

That's the Track 1 vs Track 2 split in one sentence.

Track 1 does the work AI is eating.

Track 2 builds the workflows that use AI as the engine.

Same Python skills. Completely different career trajectory.

On Wednesday, March 18th I'm showing exactly what Track 2 looks like — live, in Python, no prep needed.

Raw data → AI workflow → decision-ready output. 15 minutes.

The same pattern running inside teams at S&P Global, Apple, and MRM McCann.

Free. 500 seats. 2PM EST.

👉Registration Link (500 seats): Comment “PORTFOLIO” and I'll share a registration link in the 1st comment below

RIP data analysis and modelling jobs.

Don't take my word for it.

Take Nature's.

"Data-analysis and modelling positions are already becoming obsolete."

Nature. February 2026.

150 years of scientific credibility just described what's happening to Track 1 data scientists right now.

Track 1: Clean data. Build model. Write report. Pray someone reads it.

Track 2: Ship AI workflow. Automate the decision. System runs without you.

One of these is what Nature is talking about.

One of these is getting paid $200K.

You already know which is which.

The uncomfortable truth is most data scientists built their entire career on Track 1 skills. EDA. sklearn. Jupyter. Handoffs.

Those skills aren't worthless. But they're no longer enough.

What companies are paying for now is someone who can take raw data and ship a workflow a team actually runs — not a notebook that lives on a drive nobody opens.

That's the gap. And it's widening.

On Wednesday March 18th I'm showing exactly how to close it.

Live Python demo. 15-minute workflow pattern. Raw data to decision-ready output.

The same pattern running inside teams at S&P Global, Apple, and MRM McCann.

Free. 500 seats. 2PM EST.

👉Registration Link: Comment “PORTFOLIO” and I'll share a registration link in the 1st comment below

OpenAI, Google, and Anthropic just published guides on:

• Prompt engineering
• Building agents
• AI in business
• 601 AI use cases

9 of the best guides you can't miss:

1. AI in the Enterprise by OpenAI

Grab the PDF: https://lnkd.in/e4AcEV3S

2. A practical guide to building agents by OpenAI

Download here: https://lnkd.in/eMD-sMxp

3. Prompting Guide 101 by Google

Grab the PDF: https://lnkd.in/eMzuTs-h

4. Identifying and scaling AI use cases

Grab the PDF: https://lnkd.in/eHunEntp

5. Building Effective Agents by Anthropic

Grab the PDF: https://lnkd.in/eiSKayJ8

6. Prompt Engineering by Anthropic

Grab the PDF: https://lnkd.in/eCUmmcRj

7. Kaggle Agents Companion

Download here: https://lnkd.in/e_bVJbt4

8. Google: 601 real-world gen AI use cases from the world's leading organizations

Read here: https://lnkd.in/eJm_E4Rm

9. Prompt engineering by Google

Grab it here: https://lnkd.in/eDsvdXi8

===

🚨 What’s the next step (if you want to build an AI/DS portfolio for real)?

I have a live workshop that will help (free). Inside my live workshop, you'll get:

• My Generative AI + Data Science Process
• Applied to a Business Problem (not "toy" data)
• Actual Python code + AI Agents

👉Registration Link (500 seats): Comment “PORTFOLIO” and I'll share a registration link in the 1st comment below

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