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

It’s impossible to underscore how dependent companies have become on data engineers

An issue arises and who do you think they call?

I looked at the many definitions of the domain, and here is what engineers are responsible for: 
1️⃣ "The development, implementation, and maintenance of systems and processes that take in raw data"
2️⃣ "Producing high-quality data, consistent information"
3️⃣ "Supporting downstream use cases"
4️⃣ "Creating core data infrastructure"
5️⃣ Understanding "the intersection of security, data management, DataOps, data architecture, orchestration, and software engineering"

I’m going to be honest, that is A LOT, especially for one position.

Overall, I think some people just hear the word engineer and think: “Oh, they can solve that problem”

I see the breaking apart of the Data Engineering role a key theme in 2026 (and I’m going to write about how to do that at some point).

In the meantime, check out my article on the Data Engineering domain, what it is and what issues I see within it (including this reliance on one role to be all things to all people)

Subscribe to the newsletter and check out the article (in the comments)!

Dylan Anderson

The Data Ecosystem Author ✦ Head of Data Strategy @ Profusion/ Atombit ✦ Bridging the gap between data and strategy ✦ Speaker ✦ R Programmer ✦ Policy Nerd

I'm a consultant, but I often hate how some consultancies sell

We’ve all seen them sell clients on AI, cutting edge ML, cool data science projects

But don’t talk about all the other stuff that needs to be put in place first 

And clients often buy it because:
🤷♂️ They don’t know better
🤖 They think everybody is doing AI 
🛣️ They believe a POC can get them there
😒 They don’t get excited about data foundations
✨ They want their name attached to the shiny new thing

Yet we constantly see companies disappointed by the results of one-off AI projects by big consultancies

Or stuck in a never-ending system of dependency because they can’t do things on their own

So, although I’m a consultant, I always do my best to approach projects from the data foundations up:
1️⃣  You start with the data/ business strategy
2️⃣ Assess and recommend the right technology
3️⃣ Help build the required data assets and pipelines
4️⃣ Attach governance and ensure data quality is captured
5️⃣ Turn the data into BI/ BA insight tools that users know how to use
6️⃣ Scope out how to scale your analytics (like AI) on top of those solutions 

There is room for Data Science and AI, but getting there is about approaching it strategically, and focusing on really matters for an organisation’s data needs

If you, or your business are ever interested in experiencing a more long-term, holistic approach, feel free to reach out! 

And if you are just an individual, take note of this methodology because that is how organisations should approach their data journey

Leadership want a silver bullet to improving data quality, but that doesn’t exist

Below, I list out how you can start to think about data quality with a holistic and logical approach

First, 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 the lay of the land, including the technology landscape, pain points, and what data is there to fix. This includes:
💻 Data Technology & Tooling Audit/Strategy – Outlining what different tools do within the data journey and align that with data quality needs
🛠️ Root Cause Analysis – A systematic process helps teams understand why data issues occur and enable targeted interventions that address more than just the symptom
🏆 Critical & Master Data Assets – Help focus efforts and resources on the most impactful data
📊 Initial Data Quality Assessment – Getting the baseline data quality of important assets

Next, 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐢𝐬𝐞 what data quality means within the organization and have a strategy to tackle these fixes in a proactive (not reactive) way. This includes:
🎯 Data Governance Strategy – Understand how the organisation works with and governs data (including who owns it)
📝 Setting Data Quality Standards – Establish those clear and measurable criteria for data quality to serve as a benchmark for all people across the organisation
📑 Data Contracts – Set clear expectations and responsibilities between the downstream and upstream groups of data users

Finally, 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭 tools, technologies and approaches to combat data quality issues (but don’t skip to this step without doing the others)
⚙️ Data Catalogue & Lineage Tooling – Allow users to search datasets, understand its content, provide access, define ownership, and construct the flow of data assets from source to consumption
🛑 Data Quality Gates – Define checkpoints at various stages of the data platform (usually contained within pipelines) that validate data against predefined criteria before it proceeds further
👀 Data Observability Tooling – Monitor data health metrics to detect, diagnose, and resolve data quality issues in real-time to reduce data downtime and improve visibility into issues

There are other things you can do here as well, but the point is to think about these things holistically and in order of implementation. 

Check out my article on solving data quality issues, it is not one to miss!

Whenever ideas pop into stakeholders' heads, they share them

So changes to dashboards or tools will always exist

Instead of being reactive to that addiction, anticipate and deal with it

🧠 𝗧𝗵𝗶𝗻𝗸 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰𝗮𝗹𝗹𝘆 𝗔𝗯𝗼𝘂𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀 – Start with the big picture. What’s the long-term vision for your analytics? What dashboards exist, what business questions do they solve, and which ones are unused or redundant?

📋 𝗢𝗿𝗴𝗮𝗻𝗶𝘀𝗲 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝗲𝘀 𝗼𝗳 𝗖𝗵𝗮𝗻𝗴𝗲𝘀 – Create a framework to assess new requests: Does this change align with business goals? Is it urgent or a “nice to have”? Prioritise based on value and feasibility, and make sure everyone understands the trade-offs of adding complexity.

🗣️ 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗲 𝗣𝗿𝗼𝗮𝗰𝘁𝗶𝘃𝗲𝗹𝘆 – Keep stakeholders informed about what’s being worked on, the timelines, and what’s next in the pipeline. Proactive communication reduces noise and gives stakeholders confidence that their needs are being heard

🚧 𝗣𝘂𝘀𝗵𝗯𝗮𝗰𝗸 – Saying no is a powerful move. You should be the expert! If a change won’t deliver real value or is too niche, explain why it won’t move forward. Frame pushback in terms of business priorities to maintain trust.

📊 𝗠𝗮𝗻𝗮𝗴𝗲 𝗟𝗶𝗸𝗲 𝗮 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 – Dashboards are not individual, disconnected projects—they’re part of a broader ecosystem. Treat them like a portfolio: balance maintenance, improvements, and new builds. Regularly review what’s working, what’s outdated, and where new opportunities exist. 

In the end, your stakeholders will always want new things, you just need to manage them. Any other ways you’ve done this well?

I wrote a few articles on managing your stakeholders, thinking strategically about dashboards, and designing them more effectively. Check out the links in the first comment!

The situation is the same at every company, whether they are on the AI path or not
 
There is a lot of data, but very little insight
 
And while companies are investing in AI Agents and tools, their teams are still using Excel sheets and distrust the data made available to them
 
Suffice to say, structured, reliable data is still the foundation of progress in the world of data & AI (not a new tool peddled by some AI-focused SaaS vendor). I was extremely excited to discuss this with Philippe Boulanger on the Let’s Talk Data Podcast by DQE, Data Quality Everywhere, where we covered:
- Why, after all this time Data Quality is still a thing!
- The importance of, and how to build, structured and reliable data
- How to embed this in your organisation
 
Listen to this episode, as it reveals a lot of what I’ve been thinking/ saying for years about data quality and getting insight from the abundance of data that exists. In it I go through this best practice approach:
 
💸 First, explore the 𝐮𝐬𝐞 𝐜𝐚𝐬𝐞𝐬 𝐰𝐢𝐭𝐡 𝐭𝐡𝐞 𝐛𝐢𝐠𝐠𝐞𝐬𝐭 𝐯𝐚𝐥𝐮𝐞 𝐚𝐝𝐝
- What will actually make a positive impact?
- Ensure this comes from the business and not the data team
 
🔑 Second, 𝐢𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝐭𝐡𝐞 𝐫𝐞𝐥𝐞𝐯𝐚𝐧𝐭 𝐝𝐚𝐭𝐚 𝐝𝐨𝐦𝐚𝐢𝐧𝐬 that can drive those use cases
- Don’t focus on all the data, find that 20% that delivers 80% of the value
 
🛠️ Third, determine how to 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐭𝐡𝐞 𝐝𝐚𝐭𝐚 𝐚𝐧𝐝 𝐦𝐢𝐠𝐫𝐚𝐭𝐞 𝐢𝐭 into the right systems
- How do we connect the data sources/ systems?
- Clean and structure the data in a way that is scalable, accessible and maintains quality (if this can be automated with a tool, all the better)
 
🚀 Finally, and in a nod to AI, 𝐬𝐞𝐭 𝐮𝐩 𝐭𝐡𝐞 𝐝𝐚𝐭𝐚 𝐚𝐧𝐝 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐭𝐨 𝐚𝐥𝐥𝐨𝐰 𝐟𝐨𝐫 𝐬𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲
- If you are putting in all that work, always think with the future in mind…
- …But don’t try to skip stages, because scaling means setting up a proper foundation!
 
In the end, people are obsessed with AI making sense of unstructured data, but structuring it is still the best way to get insight out of your data! As your business generates more data, ensuring the right sources are of high quality will become increasingly important to get the insights you need.
 
Check out the episode, and it was great to partner with DQE, Data Quality Everywhere, on the Let’s Talk Data podcast and this post! Check them out and I've linked the podcast in the comments!

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About to take off from London to Austin for Data Day Texas!

If you are attending I’ll be speaking on Saturday at 9:40am  in 2nd Floor - Classroom 203 about how to Navigate the Data Ecosystem

Consider this 45-minute session an expansion on my newsletter with some hard hitting facts, implications and action steps to think more holistically about your data ecosystem. Within we will break down:
1) How to embed business strategy and org structure into your data plan
2) How to think more holistically about the data & analytics lifecycle so you don’t have siloed domains
3) The importance of data management and how should slot in (because let’s be honest, this always falls to the back of people’s minds)
4) How to make the business actually consume data and get value from those products you’ve built
5) How to think about other strategic considerations as a data leader (hello change management and tech debt)
6) How to approach the never-ending list of surrounding influences/ distractions (like new tools) so you don’t blow your budget or get too stressed out

Sound like an interesting session? Then come check it out. 

Not at the conference? Feel free to reach out because I consult on this type of thing every day with huge organisations, backed up by some amazing talent at Atombit who implement these technical bits!

Also kudos to Atombit for helping me with this presentation and speaking slot, goes to show how much they want to invest in building out thought leadership in the data space!

Frameworks help you organise your thoughts, bringing quality insights to your work

When it comes to your analytics, KPI trees can be that cheat code 

Here’s why it’s so effective: a KPI tree shows relationships between metrics. 

For example, if EBITDA is down, the tree might reveal the root cause—lower revenue, fewer paid orders, or even customer churn. 

By linking KPIs, you can quickly diagnose problems and focus on the right solutions.

But it’s not just about diagnosis. The KPI tree can serve as the foundation for a KPI dictionary, outlining calculation methods, sources, and ownership for each metric. 

It also becomes the backbone of your dashboarding environment, ensuring everyone in your organisation is using the same language and working from the metrics

Key to success? 
🔗 Make the tree accessible across teams
🔍 Build click-through dashboards for detailed views
🎯 Ensure departmental metrics always ladder up to the organisational view

Check the past Data Ecosystem article by Mark de Jong and learn more about this effective tool and what it can deliver for your business!

Super excited to be presenting at Data Day Texas at the end of January! Sign up and hope to see you there!

Bibliophile / Impresario (and Data Day Texas)

Straight from London, Dylan Anderson will be joining us at Data Day Texas to share the latest on his Data Ecosystem project. 
https://lnkd.in/efYwzXcH

It seems like half of London is coming to ddtx this year. 
Do you have your ticket yet?

Follow Dylan to bring yourself up to speed - and see you in Austin!

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