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

Founder @ Drilldown | Custom-designed business dashboards for B2B SaaS that deliver insights in one click, eliminating the need for complex spreadsheets in just 60 days.

We help B2B SaaS teams build an owned data system - so metrics align across the organization and growth decisions are based on evidence.

This might resonate:

As B2B SaaS companies scale, it becomes harder to understand on where growth comes from and what to do next.

Most teams already have dashboards, data integrations, and the technical ability to build new ones. 

Yet they still struggle to agree on what metrics mean, explain drivers of revenue, or produce reliable forecasts.

What you need is one set of accurate, explainable numbers that your team can rely on.

Our work focuses on the data system underneath dashboards: the definitions, structure, and logic that determine whether analytics can support decision-making at scale.

If you’re trying to make decisions based on data but the numbers don’t line up, this explains why: https://thomasvarekamp.substack.com/p/you-have-dashboards-why-are-decisions

Thomas Varekamp

I used to love Power BI. 

Now I hate it.

Not because it’s a bad tool. But because of how most teams use it.

Power BI lets you build dashboards.

But to build dashboards, you need to model the data first. And that’s the trap.

Most people do datamodelling inside the BI tool. That’s a huge mistake.

Why?

Because as your business grows, your data grows too.

What starts as a simple report becomes a spiderweb of logic.

- impossible to trace.
- brutal to maintain.
- easy to break.

Here’s what to do instead:

1. build a proper data warehouse.
2. manage your single source of truth there.
3. use Power BI only as a presentation layer.

A good warehouse gives you:

- version control → roll back mistakes.
- traceability → find and fix issues fast.
- quality checks → know the data is right. 

When you separate logic from visuals:

- people trust the numbers.
- teams make faster, better decisions.
- you don’t have to rebuild everything later.

It’s cleaner, smarter, cheaper.

Don’t duct-tape your data stack.

Build it right from the start.

Drilldown | a data science team in your pocket for B2B SaaS.

I saw a horror story on Reddit that highlights the biggest AI risk in 2026 (and beyond)

A company let an AI data agent answer leadership's business questions for three months.

The output was complete fabrication:

- Sales targetted territories based on fake revenue.
- CFO presented decks filled with incorrect metrics.
- Managers made decisions based on hallucinations.

If you plug AI directly into your data without providing sufficient context, it will simply guess the answer. 

It will sound confident, but it will likely be completely wrong.

The missing piece here is the foundational work; data modeling + metadata + governance that explicitly tells the AI:

"This is how we calculate Net Revenue."
"This is the definition of a qualified lead."
"This is how we measure churn."

It might not be the most exciting part of data management, but as we rely on AI to do more of the heavy lifting, it is absolutely essential.

Business intelligence is being commoditized way faster than anyone realizes.

Workflows that used to require a team of 5 are now being handled by 1 marketer using Claude Code.

Here is exactly where BI is heading in the next 12 months:

1. 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗮𝗻𝗱 𝗱𝗿𝗮𝗴-𝗮𝗻𝗱-𝗱𝗿𝗼𝗽 𝘁𝗼𝗼𝗹𝘀 𝗮𝗿𝗲 𝗮 𝗱𝗲𝗮𝗱 𝗲𝗻𝗱.

→ using code and letting AI handle it is much faster, cheaper, and robust. 

Learning Power BI now? You're in for a big surprise.

2. 𝗦𝗽𝗿𝗲𝗮𝗱𝘀𝗵𝗲𝗲𝘁𝘀 𝗮𝗿𝗲 𝗼𝗻 𝘁𝗵𝗲𝗶𝗿 𝘄𝗮𝘆 𝗼𝘂𝘁 

→ no one will manually update cells and write formulas in Excel. Just hook your data to AI and ask for the insights.

3. 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝘄𝗶𝗹𝗹 𝗯𝗲 𝟵𝟵% 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 

→ AI will do the heavy lifting. Humans will transition from "analysis" to "validation", only checking outputs.

4. 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗴𝗲𝘁𝘀 𝗼𝘂𝘁𝘀𝗼𝘂𝗿𝗰𝗲𝗱 𝘁𝗼 𝗔𝗜 

→ machine learning is code-heavy. AI writes code perfectly. Humans will just set the framework. AI does the rest.

5. 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝘀 𝗯𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝗮𝗻 "𝗲𝗮𝘀𝘆" 𝗷𝗼𝗯.

→ AI can now auto-generate connectors, map schemas across sources, and self-fix broken pipelines in real-time. 

So, where is this going?

I think we will see companies either:

1. hire a single data person & upskill employees.
2. have external agencies handle it.

(or both)

Just as computers replaced most mathematicians, AI is doing the exact same thing to BI.

Only this time 1000% quicker.

If you disagree, you simply aren't paying attention to what's happening right now. 

I'm shocked how fast things are progressing.

Data teams will look completely different in 2 years.

The standard data analysis workflow used to work like this:

1. you have a question.
2. you ask the analyst.
3. you wait a week.
4. you get a report.

And then you figure out if you actually got what you wanted.

Now?
1. you ask the question.
2. you get the answer in seconds.

Think of this:

Marketing can ask “what is our best channel?”
…without opening 20 dashboards.

The CEO can ask “where to focus next quarter?”
…without going through slide decks.

Sales can ask “which deals need attention?”
…without digging through the CRM.

Everyone will be able to use business insights, instantly. 

So what will data teams do? 

Their primary value no longer comes from structuring and analyzing data. 

It will come from:
→ building foundations AI can understand.
→ helping others formulate questions.
→ translating insights into actions.

The titles will have to catch up with the work.

New €5,000 deal. 3 deals in pipeline. 6 calls. 

Here’s my 7-day update:

I spent most of the week doing outreach, no automation. 

I put quite some time writing personal messages. Response rate is fairly high (10-20%).

Had some good chats. Definitely works if you care.

𝗢𝗙𝗙𝗘𝗥 𝗨𝗣𝗗𝗔𝗧𝗘:

Based on these conversations, we now include a 1-day on-site build in our offer.

Meaning, we come to your office & build your data system together.

I’ve done this in the past. 

High-energy, fun, and speeds up the results.

𝗪𝗘𝗘𝗞𝗟𝗬 𝗦𝗧𝗔𝗧𝗦:

Deals:
- finalizing new €5,000 deal.
- 3 open deals for new offer.
 
Calls (7 days):
- 5 calls with partners.
- 1 follow-up call with lead.

Substack (7 days):
- 97 new views.
- 8 new subs.

Linkedin (7 days):
- 28,887 new impressions.
- 101 new followers.

We're heading into the right direction I think.

Ready for the week ✌️

Drilldown | Helping B2B SaaS leaders regain control by turning data into direction.

Expandi hit $10M ARR bootstrapped. Here’s the data system we built to help them grow faster.

From the outside, Expandi was an unstoppable force:

→ 0 to $7M ARR in under 3 years.
→ fully bootstrapped.

But as they scaled, internal problems were growing.

- marketing couldn't measure LTV. 
- finance struggled to reconcile $ vs. € ARR. 
- teams burning hours updating spreadsheets.

We partnered with them to install The SaaS Intelligence System.

The execution plan:

𝟭. 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝘇𝗲

We identified growth blockers and defined clear goals for every department. 

No vanity metrics.

𝟮. 𝗕𝘂𝗶𝗹𝗱

We implemented complex ARR logic (New, Expansion, Churn) into a centralized warehouse they own.

𝟯. 𝗔𝗱𝗼𝗽𝘁

We put the whole team (Marketing, Customer Success and Finance) on shared metrics. 

Before: no realtime insights.
After: trusted metrics for decision-making.

Swipe to see how we built their data system.

How we built Expandi's data system

We are entering the era of the 1-person data team.

I spent the past week testing AI data agents. It made me to rethink everything:

- data science.
- data analysis.
- data engineering.
- analytics engineering.

AI can now execute all of it.

Which makes one thing very clear: most data tools with buttons are going to fizzle out.

Why pay for a rigid analytics platform when an AI can write, refactor, and maintain the underlying code directly?

My predictions:

1. 𝗖𝗼𝗱𝗲-𝗵𝗲𝗮𝘃𝘆 𝘀𝘁𝗮𝗰𝗸𝘀 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗱𝗼𝗺𝗶𝗻𝗮𝗻𝘁.

AI makes raw code the fastest, cheapest, and most robust way to build data systems. 

Writing and maintaining SQL, Python, and infrastructure is no longer the bottleneck.

2. 𝗗𝗮𝘁𝗮 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗰𝗼𝗺𝗺𝗼𝗱𝗶𝘁𝘆.

Moving data from A → B is a solved problem. 

Just give the API docs to an AI agent and ask to build the pipeline. 99% of the time it will one-shot it.

(try Claude Code if you don't believe me)

3. 𝗝𝘂𝗱𝗴𝗺𝗲𝗻𝘁 𝘄𝗶𝗹𝗹 (𝘀𝘁𝗶𝗹𝗹) 𝗯𝗲 𝗸𝗶𝗻𝗴.

AI can write the code, but it doesn't know your complete business context. 

Deciding what to model, why it exists, and how it should be interpreted remains a human responsibility.

The next 12 months are going to be VERY interesting.

4 deals in pipeline. 2 calls. 

A 7-day update:

I spent a chunk of the week experimenting with Claude Code, and holy shit I'm impressed.

A single person can now easily fulfill all the roles of a data team.

Data work will look very different in 12 months. 

𝗢𝗙𝗙𝗘𝗥 𝗨𝗣𝗗𝗔𝗧𝗘:

Due to these new insights, I'm testing out new things we can do for our clients.

Technical obstacles are pretty much a thing of the past.

My idea is to put more focus on advanced data science work.

Meaning: building custom prediction models (churn prediction, ARR forecasting, etc.)

This used to take ages to build & validate, but not anymore. 

𝗪𝗘𝗘𝗞𝗟𝗬 𝗦𝗧𝗔𝗧𝗦:

Deals:

- 4 potential deals in pipeline.

Calls (7 days):

- 1 call with a previous client.
- 1 call with a new lead.

Substack (7 days):

- 24 new views.
- 1 new sub.

(will publish new article soon)

Linkedin (7 days):

- 40,259 new impressions.
- 74 new followers.

Let's see if we can improve this week 👊

Founder: "I want an AI agent to do my ARR forecasting."

Me: "Show me your data infrastructure."

The reality check:

If your data is scattered across Stripe, HubSpot, and Google Sheets...

You don't need more AI. You need a Data Warehouse.

To build a "AI-Native" business, you have to fix your messy data first.

The old way:

❌ marketing calculates spend in spreadsheets.
❌ product counts users in Mixpanel.
❌ revops tracks ARR in Stripe.

→ Problem: when data is disconnected, AI lacks context.

Here's why that matters:

1. Conflicting definitions.
  
Marketing defines a "Lead" differently than Sales. The AI doesn't know which logic to trust.

2. Hallucinations.

When an agent can't find a single source of truth, it guesses. Guessing = hallucination.

3. Token bloat.

Your AI has to burn tokens trying to understand three conflicting data sources instead of answering the question.

The new way:

✅ centralize data in a Data Warehouse.
✅ add context + definitions.
✅ deploy AI agents on top.

→ Result: consistent, accurate, and cheap answers.

An AI agent is only as smart as the data it feeds on.

If you can't trace a customer from ad click to renewal, neither can your AI.

Fix the data first.

The agents come second.

Most dashboards & reports fail for one specific reason.

It rarely has to do with the tool or the data.

The problem is much simpler: metric definitions.

A dashboard full of metrics means nothing if no one agrees on what they mean.

I see this pattern constantly:

❌ sales trusts a spreadsheet over the dashboard.
❌ finance / Product report conflicting churn %.
❌ "active user" has 3 different definitions.

Before you build the dashboard, build the Metrics Dictionary.

It can be a simple Google Sheet that lists out your metrics + descriptions.

Here are the columns we include in it:

1. unit → is it €, $, %
2. filters → how to slice the data
3. calculation → how it's calculated
4. department → who cares / uses it
5. metric owner →who is accountable
6. refresh rate → how often it updates
7. description → what the metric means
8. data source → where the data comes from

I attached the template we use in the comments.

Copy it, tweak it, use it. ✌️

The data analyst role we know won’t exist in 3 years.

And that’s a good thing.

For years, analysts were valued for one output: dashboards.

Pretty charts, nice filters and providing a surface-level view of the business.

That is ending.

Agentic AI is changing how analysts work completely. 

They’re not just dashboard builders anymore. They’re end-to-end solution developers.

That means:

1. defining business logic up front.
2. modeling data to drive strategy.
3. delivering insights without 5 handoffs.

And yes, way fewer data people will be needed.

But the ones who remain?

They’ll have more trust, and more impact.

The analyst isn’t just the “report person” anymore.

They’re becoming orchestrators behind the insights.

In 2-3 years, all data teams will become AI teams. 

Here's why:

Data teams today spend countless hours in meetings explaining dashboards and writing SQL. 

Most of this work doesn't add real value.

AI can do this faster, better, and without human bias.

In the next few years, analysts will stop spending time on dashboards and answering questions.

They will be maintaining infrastructure for AI:

- designing data models.
- adding context to data.

Here's how that works:

1. business software generates data.
2. data warehouse ensures high quality data. 
3. AI pulls insights and executes tasks.

Data will be the most valuable resource in every organization. 

This will be the source of truth for AI.

taplio

The Battle

Dylan Anderson

Thomas Varekamp

Followers

Posts

Engagements

Likes

Performance ScoreDylan Anderson's LinkedIn Performance

Performance ScoreThomas Varekamp's LinkedIn Performance

Ready to grow your LinkedIn presence?

taplio

The Battle

Dylan Anderson

Thomas Varekamp

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);}Dylan Anderson's LinkedIn Performance

Performance ScoreThomas Varekamp's LinkedIn Performance

Ready to grow your LinkedIn presence?

Performance ScoreDylan Anderson's LinkedIn Performance