Yo, I'm Matt Mike 👋

(yes, Mike is my actual last name).

My career path has been far from linear. Here are the cliff notes (that's still a thing, right?)

2015: graduated with an English degree
2016: became a teacher
2017: became a dean of students
2019: left education for B2B sales
2019: hated sales. took a pay cut to become an operations analyst
2021: got promoted to operations supervisor

2022 and onward: became a business analyst (FIRST DATA JOB). Started documenting the journey on social media where I now have a combined following of 100,000+ tech professionals. I've been through a few different data roles with progressive complexity and now do BI consulting while continuing to help others progress their tech careers through content.

Because of my background, I have a passion for helping people with non-technical backgrounds break into tech and advance their careers. My technical passion is business intelligence analytics.

🤝🏻 Brands: mjomike@gmail.com
🎥 YouTube: @mattmike
📩 Newsletter: https://thdatapoint.substack.com/

Matt Mike

I changed careers at 31.

I was a teacher, then a Dean of Students first.

From there, I spent a few years in a non-technical corporate role. But that was my introduction to technical tools.

I decided I wanted to get into data analytics and went for it. The process start to finish took about 6 months.

People often think that you should be settled into a career in your 30s, but it's never too late to pivot from one area to another.

We put a lot of pressure on ourselves to "figure it out" but life isn't always so linear.

If you want a change. Make it happen.

BI Developer & Analytics Consultant 🤘 | YouTube @ Matt Mike | Former Educator & Dean of Students

SQL is a fundamental analytics skill.

It's the language of databases and how we query those databases for the information we need.

My latest read, "𝑆𝑄𝐿 𝑓𝑜𝑟 𝐷𝑎𝑡𝑎 𝐴𝑛𝑎𝑙𝑦𝑡𝑖𝑐𝑠" shows you how to do just that.

But it goes far beyond the basics.

Into things like:

↳ Manipulating data with Python
↳ Presenting, transforming, and updating data
↳ Defining datasets from existing datasets
↳ Window functions
↳ Performant SQL
↳ Processing JSONS and Arrays
↳ Advanced data types
↳ Inferential statistics

And best of all, it's practical.

Big thanks to the authors:

Jun Shan​
Haibin Li
​Matt Goldwasser​
Upom Malik​
Benjamin Johnston

Looking forward to learning some cool stuff :)

Grab the book here if you're interested: https://packt.link/0oLy1

#packtpartner

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

What is the technical architecture for each platform philosophy?

Often infographics or diagrams focus on the data team structure, not how the technologies are set up and how the data flows

Here are the four approaches and associated infographics:

𝐂𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐬𝐞𝐝 𝐃𝐚𝐭𝐚 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 – Stores and manages all data in one place, typically in a cloud warehouse or data lake. It reduces data silos and creates a single source of truth, simplifying governance and security. However, it can be challenging to scale globally and limits data access and democratisation for business users

𝐃𝐞𝐜𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐬𝐞𝐝/ 𝐃𝐚𝐭𝐚 𝐌𝐞𝐬𝐡 – Decentralises data ownership, placing it with domain-specific teams, treating data as a product. This approach allows for faster, tailored solutions but risks data silos, inconsistent tooling, and high costs. Governance and communication can be tricky, with varying processes within each domain

𝐇𝐲𝐛𝐫𝐢𝐝/ 𝐇𝐮𝐛 & 𝐒𝐩𝐨𝐤𝐞 – The hybrid model centralises core functions (like data storage and governance) while allowing domain-specific customisation for analytics. It strikes a balance between scalability and flexibility but requires alignment across teams. Tool duplication and disputes over resources can still arise without a clear strategy

𝐃𝐚𝐭𝐚 𝐅𝐚𝐛𝐫𝐢𝐜 – Builds on other models but introduces a unified data layer through metadata. It enables easy data discovery across the organisation. However, it doesn’t eliminate operational complexity and might not fully address issues like data duplication or bottlenecks in access. Still evolving, it offers promise but isn't a silver bullet

Check out the article for more detail on each and let me know your thoughts!

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

To do analytics, you need to start from a common base with a common language

That is the reason KPIs exist; yet companies don't know how to use them properly

And if companies don’t know how to use them, neither will your new AI tools…

Check out a past Data Ecosystem article where Mark de Jong shares his experience helping clients standardise their KPIs across their business and data teams/ products. This covers: 
👀 Real world consequences of misaligned KPIs
🧠 The process and approach to standardise them effectively
🛠️ The supporting functions that should help your KPI standardisation
🔑 How KPI Trees can help you along this journey and make sense of things

Link is in the comments!

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?

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

There are a lot of different tools and technologies in data

And it can be easy to get lost

So here are the high-level categories and tooling types within each (note not everything is in here, and I welcome feedback and input):

1) Data Sources
- Operational Systems
- Customer Data Platform
- IoT Sensors
- External Databases
- Web Data & Social Media Services

2) Data Ingest
- Real-Time Streaming
- Batch Ingestion
- Integration Middleware

3) Data Storage
- Relational Databases
- Cloud Storage
- Data Warehouses

4) Data Processing & Transformation
- Data Processing Engine/ Platform
- Data Transformation Tools

5) Orchestration
- Pipeline Workflow Management
- Job Scheduling
- Platform Integration Orchestration

6) Data Governance, Management & Security
- Data Catalogues & Lineage
- Data Observability/ Quality Tooling
- MDM Tooling
- Data Security Tooling
- Data Modelling & Architecture

7)Serve & Access
- Query Engines
- Access Management
- Reverse ETL

8) Analytics (including Data Science)
- Coding Languages
- Machine Learning Platforms
- Analytical Tooling
- Knowledge Graphs

9) Consumption
- BI Platforms
- Data Visualisation Tooling
- Semantic Layers

Check out my newsletter articles on building a data tech strategy and my other one on these tooling types and why they matter. Because understanding the tech ecosystem/ landscape is a difficult area (so you may as well use this leg up)!

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

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