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 | Tableau Consultant | Author of How to Become a Data Analyst

If you're reading this you probably either:
1. Want a job in data. 
2. Want a friend in data.
3. Need a data analyst. 

If you're #1, I was you, just one year ago! I've shared how I went from beginner to experienced data professional since the beginning.

Its my goal to create the kind of content a younger version of me would have wanted to consume!

#2, thanks for being here! Feel free to give me a wave. Lets be social!

#3's, hello 👋. If you haven't yet, check out my portfolio site, or my Annie's Analytics company page on here where I offer consulting services.

Heres my story. 

In February of 2022, I decided to learn data analytics for fun. I created a Tik Tok account, and started documenting my journey to learn this new hobby (@anniesanalytics). 

Very soon, I realized that every day I was waking up and just wanting to write SQL, build visualizations, learn Python.... Work with data! So, I decided that once I finished graduate school (in Healthcare) in July, I was going to become a data analyst. 

Along the way, I have shared here and on Tik Tok everything I can about what it's like to learn data analytics, and how you can do it for free or at least very cheaply. I've shared about the ups and downs of job searching. I've even shared about how I have built a portfolio, and set myself up as a real analyst. 

Now I also do YouTube videos, especially my series where I invite other data analysts to have a coffee chat with me, but we live stream it! We talk about our journeys into data and what our jobs look like now. https://youtube.com/playlist?list=PL3wIL0Blsv2vf8aAjTQXo017YntAXqrZc


Want to see my Portfolio as it was when I got my first job in analytics? I left it here in exactly the same state that it was when I got my job in July of 2022 to serve as an example to you, if you are a job seeker now! https://anniesanalytics.wixsite.com/annie-nelson/projects-6 

Want to see my current portfolio I am using now that I have gained some more skills? https://anniesanalytics.carrd.co/

Or the LinkedIn group here: https://www.linkedin.com/groups/12635532

Discord here! https://discord.com/invite/m9vZKtReFM

Annie Nelson

Why have a big profile if you can't use it for fun sometimes? Eveyrbody, Meet Harper! 

Harper is a goofy, loving, playful, and beautiful 1yr 7mo old girl and she is available for adoption with the San Diego Humane Society! (pet ID #977211) 

We are fostering her and spend a lot of our day going "look at Harper!!!!" because she's constantly doing something adorable. 

She's so easy to love. I hope she finds her forever home soon 🫶

If anyone's interested in learning more, you can find her profile by searching the adoptable pets list on the SDHS website. She's a gem!

I made a rookie error the other day. I asked AI to help me with some code that generally it is pretty well suited to handle.

But it got it wrong the first time. And rather than resetting the conversation, I tried to reason with it to change it's approach.

The code was really just dealing with sequential logic that at the time of writing I felt too lazy to do myself.

I spent over an hour going back and forth with it when I could hhlave probably just done it from scratch in 15 mins.

Lesson learned: once AI makes a decision, it sticks to it. If it's way off base with its first answer, stop. Reset. Start fresh.

Me, receiving praise for doing an amazing job on something that was actually really easy to do

Have we hit and are passing a golden 'easy' age for being a data analyst?

In the past, getting access to data was messier and required a higher technical skill set.

Today, a combination of advances in technology and a well saturated market (aka, having coworkers who handle the data /analytics engineering) means that there are many people with data analyst titles who get by exclusively building dashboards or even only using Excel.

But technology is still progressing. And it's taking away the edge that these people have enjoyed by making it easier for * everyone * to do those things. 

Although I think the "AI is going to take your job" argument is lazy and overblown, it likely is true that standing out from the competition is going to take more than the ability to build a sleek dashboard or write a complicated formula.

Not to mention, the way people access and use data is continuing to change. Why rely on a dashboard to load that takes 40 seconds and has a limited number of cuts when you could ask [AI tool] instead? 

Again, I think there is still a lot of value to the data analyst role and am not saying AI is replacing it, but I am saying I think that the role is changing and it seems that all of the "easiest" parts are the first to go.

Luckily for me, the parts of my job that I enjoy the most are the parts that are not going to be automated away any time soon 🤠

How many interviews do YOU think are necessary to fill a data analyst role? (or similar)

As everyone knows, tech companies put people through 5,6,7,8 rounds - some of which take hours!!

Someone mentioned to me the other day that while the current market is really tough, it's also hot. Some companies are moving candidates through their pipelines quickly.

In my opinion, this puts the companies with a long and arduous process at a competitive disadvantage.

But competition aside - how many do you actually NEED?

I've never hired someone, and can empathize with the stress (especially for less experienced hiring managers) that you'll make a bad choice. I've seen the impact that can have on teams, and I know it can be hard to get rid of someone.

But as a candidate.... There's nothing new you're getting out of me after a hiring manager interview, technical, and exec/stakeholder interview.

And the technical doesn't need to be some long rocket science experiment. A good interviewer (which I have experienced more than once) knows how to ask questions that help them understand if you actually understand the concepts, and how you approach problem solving.

I'd be really curious to see if you could put together a study correlating interview length & candidate success. At what point do you hit "diminishing returns" territory?

If we were ready for AI to take over all of our work then we also would have already nailed self - serve analytics.

In order to be ready for true self serve analytics we typically need a huge budget for a great, large team, or a business that moves slowly and has time to pay down all tech debt.

In order to pay down tech debt we need the people making the reports to change their behavior to stop creating so much in the first place.

Welcome to the real world.

We talk about self serve analytics and we dream about zero technical debt. But then we get back to work in the messy middle, doing our best and moving as fast as we can.

The view out of the train window on my commute to a coworking spot. My life is so hard! 😉

Petition to replace the sycophantic AI slop comments with the good, old fashioned "Interested".

The only people who use that anymore are creators who remember when their posts/feeds were flooded with a wall of "Interested" 😕

You don't know how good you have it until it's gone!

Everyone, meet Terk! He is available for adoption with the San Diego Humane Society and just looking for a soft place to spend his Golden years 💛

Terk is 16lbs, and enjoys your company and having a good sniff while out on a walk. We normally foster larger dogs, but our last sweetie was a handful so we asked for a mellow, easy dog to balance it out! 

As you can tell Terk is an old man, so he walks with a bit of a wide gait but if you look closely you can still see a little tail wiggle as he sets off on walks.

So far it seems like he is fairly indifferent to other dogs (not reactive!) and will sometimes solicit attention from passerbys out on walks.

He prefers to hang out at our side while we work from home, and enjoys a good head scratch every now and then.

If you know someone in SoCal looking for a mellow companion who they can spoil with love during their golden years, Terk might be your guy!

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The Battle

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