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

Data Analyst

Helsinki Data Week

Save the date: Oct 28 to 31, 2025
www.helsinkidataweek.fi

Founder

Teosto

Data Analytics Lead

Business Development Manager

Customer Relations Manager

Customer Service Manager

Customer Service Advisor

Joensuun Popmuusikot ry

Producer

Levy-Eskot (Maailman Ääni ja Valo Oy)

Record Store Clerk

Ilosaarirock (Joensuun Popmuusikot ry / Joensuun Nuori Rytmi Oy)

Merchandise Manager

Kerubin Kuppila (Joensuun Nuori Rytmi Oy)

Promoter

Data Analytics | Data Visualization | Business Intelligence | Data & AI Strategy and Product Management | Music | Writing a book on ggplot2 extensions (R packages)

I've always been curious about systems and how they work. Especially how they can be improved. And I don't only mean technological systems. My curiosity has led me to learn a bunch of technical skills on the way, but everything starts (and ends) with people!

If I have a superpower, it’s the ability to translate between the business and IT. This is something I've been doing for more than 10 years now. Regardless of the role I've had and way before I started learning about data. Also to be able to take a BI project from a business pain point to a finished product. Mostly by myself (one good data engineer to work with is all I need to be happy). Not counting the business stakeholders. I like to have them in the loop to make sure what is being built a) solves the problem it was meant to AND b) gets used.

I am skilled in data analytics, BI, data & AI product management and/or strategy. I have experience managing a team of 10. And I'm comfortable leading without formal authority.

I like to write (currently writing my first technical book). My blog posts have garnered attention. They even ended up on the pages of the biggest newspaper in Finland, Helsingin Sanomat. My domain expertise lies in the touchpoint of music, copyrights, and (generative) AI.

Here are some of the highlights from the last two roles (business development and data analytics lead) I had at Teosto:

2020
💠 Designed a dashboard and related taxonomy for the customers (music creators). Before this, they could only see part of their distribution information online. The rest of it was on PDFs containing thousands of pages.

2022
💠 I set up a data function from scratch (inside the business development organization). Led a virtual data team that laid down the groundwork for future data work at Teosto.
💠 I initiated an information request process for the whole organization. It was easy for the people (email service desk) and the data team (JIRA tickets). Less overhead, better requests, more communication between business and IT. Thousands of hours saved.
💠 I built Teosto's first production BI dashboard. To quote a former Teosto executive, it "[b]uilt from scratch Teosto’s capability to track foreign distributions" and helped to get to "all-time high foreign royalty distributions to Teosto members in 2022 with a 25% year-on-year increase".

2023
💠 I helped build Teosto's first-ever data strategy (we started with a data maturity analysis in 2022). Completely business-driven.

ps. When I'm not doing data, I'm probably somewhere, singing!

Antti Rask 🦜

So, what did I do yesterday? Among other things, I added ”find nearest open library” and ”get directions” to my BiblioStatus app.

This is what I dreamed about when I first had the idea for the app a year or so ago.

Back then, I just didn’t know how and/or have enough time to learn to implement it.

And yesterday it was just one of the improvements to this and another app.

Crazy. Now there really aren’t any excuses left…

——

As Mark Freeman II mentioned yesterday, and what I’ve also found is that building the thing will make you realize what else is missing. In this case it was the ”get directions” button. I didn’t originally have that on my to-do list for the app. But it came to me naturally after adding the ”find nearest open library” button.

Are you doing/feeling/seeing this?

What are you building now that you can?

I’m genuinely curious. Because too much of what I see is posts about using AI to be more productive… in building more tools. I want to know what it is that you’re actually building with those tools!

The tools aren’t the thing. It’s what they allow you to do is the thing. Or at least they should be. Otherwise, why do we even bother?

——

And yes, if you’re interested, the app is called BiblioStatus and you can find it from my website: youcanbeapirate (you know how the domain most likely ends). It works only in Finland (that’s the library API I’m using) and that’s why the next improvement is to add language versions (Finnish (default) and Swedish).

BI | R: RandomWalker package (co-author), col2hex2col package (author), TuneTeller app (author), BiblioStatus app (author) and the author of the upcoming book ggplot2 extended l Helsinki Data Week (founder)

graphical user interface, application, website

Q: What captures all the colors and keeps them separated?
A: Maximum security prism.
Jokes aside, a new version of my col2hexcol R package, has landed on CRAN!

This is the second version to hit CRAN, but there have been multiple updates between 0.3.1 and 0.5.3. Here are the highlights (full Changelog can be found on the col2hex2col page on my site, youcanbeapirate):

⭐ hex_to_color(): Added nearest color fallback functionality
- When no exact match exists, returns the closest named color using LAB color distance
- Requires optional farver package for color distance calculations
- New fallback_nearest_color parameter (default: TRUE) to enable/disable fallback
- New fallback_distance parameter to get the LAB distance to the matched color
- Added Yann Cohen as contributor for the fallback functionality

⭐ create_color_table(): First added LAB columns and then enhanced column labeling
- LAB columns now display as “Lab L”, “Lab a”, “Lab b” instead of raw column names
- First column is automatically labeled “Color Name” regardless of input column name

⭐ Data: Updated color database with 29 new colors from the color-names repository

To try out the package, just run install.packages("col2hex2col") in R!

There have already been 249 downloads / month and I'm happy about each of them!

-----

🦜 Want more content like this in your LinkedIn feed? Click 'follow' and the 'notification bell' on my profile! 🔔 Or go to Substack (youcanbeapiRate and/or ggplot2 extended) 📚

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