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

Vin Vashishta

AI Strategist | Monetizing Data & AI For The Global 2K Since 2012 | 3X Founder | Best-Selling Author

I have helped over 9,000 senior++ ICs and executive leaders advance and stay relevant with high-end AI capabilities. Courses & Certifications: https://datascience.vin

Upskill your workforce to be AI-ready in 3 months: seminars@v2ds.com

Conference talks, brand partnerships, and thought leadership: partnerships@vinvashishta.com

Over 85% of AI projects fail to deliver a return on investment because they start with technology, not a business problem. I’m fixing that by empowering companies and training people to stop experimenting with AI and start monetizing it.

Define, align, and manage the business and technical transformations that turn C-level AI visions into products, productivity, and revenue. Since 2012, V Squared AI has worked with large enterprises to deliver over $4 billion in value.

My frameworks are built from working in the trenches. My practitioner-to-strategist journey allows me to bridge the critical gap between technical teams and the boardroom, ensuring that AI initiatives are not only technically sound but also commercially viable.

Partners & Disclosures: Dell Pro Precision Ambassador, NVIDIA Partner, & Gartner Ambassador.

Speaker’s Bio:

Vin Vashishta is an award-winning AI thought leader, best-selling author of ‘From Data to Profit,’ 3X founder with a successful exit, and CEO of V Squared AI, one of the first pure play data and AI consulting firms. The Wall Street Journal, Fast Company, and The Information have showcased Vin’s AI expertise.

He brings 30 years of experience in leadership, technical strategy, and hands-on technical product delivery. He has trained, productized, and monetized information and AI for 15 years and previously worked in software engineering and organizational leadership roles.

Founder & AI Advisor

Go with experience and a track record of success. V Squared is one of the oldest data and AI consulting companies in the world. Since 2012, that singular focus has built deep expertise in monetizing data, analytics, and AI.

V Squared's AI advisory services give clients clarity into the complete data and AI monetization journey. They trust us to implement frameworks that facilitate and streamline the journey. We provide expertise to remove roadblocks and avoid setbacks.

Our past and present clients include Airbus, Siemens, Walmart, JPMC, and over 20 SMEs.

Reach out for help with:
AI Product Strategy and Delivery
AI Strategy
Data & AI Organization Building and Scaling
Product Evangelization and Positioning

email: info@v2ds.com or DM me for more information.

Block laid off 40% of its workforce because Jack Dorsey finally realized it's a legacy middleware SaaS company, and he does not know how to survive the next 3 years. Here's the real AI story.

Block had 3,800 employees in 2019. It hired like a pandemic-era SaaS company chasing growth metrics. Headcount ballooned to 10,000. The stock dropped 75% over 5 years, and Dorsey only sees one way forward.

He is cutting 4,000 people and calling it an AI transformation, but Wall Street knows the real story. Dorsey has given up and is preparing the company to be acquired. The stock jumped 24% overnight.

Block processes payments and takes a cut. It is outdated middleware facing emerging AI platform business models. To survive, Block must use AI to grow the ecosystem or make it more efficient.

Dorsey does not know how to do either one, so he is backing out altogether and begging for a buyout.

Square Capital is a merchant cash advance that looks at transaction volume and fronts a percentage. Any bank looking at deposit flows has the same data. The opportunity used to be too small to be worth the engineering cost.

But AI tools change that and eliminate the cost barriers. What is Block's moat now? Distribution and habit will not be enough, and Dorsey knows it. Businesses, especially small businesses, optimize for price.

Block is a payments processor competing on price against Stripe and Adyen. It is a lender competing against bigger banks that realize they can underwrite small loans from their own data whenever they need a growth spurt.

Block is a legacy neobank that will soon compete with a wave of AI-first neobanks and incumbents who are transforming with a better vision of how AI platforms are monetized.

Block is a SaaS company in a race to the bottom that will soon be seen as bloatware. That's why Dorsey didn't cut gradually. He cut 40% in one shot and raised the 2026 outlook.

He made the company look lean and profitable overnight and is dressing up his company for a sale before the rest of the market figures out the obvious.

The AI age rewards two kinds of ecosystem or platform business models:

Those that grow the pie or ecosystem and monetize the growth.
Those that make the ecosystem more efficient or reduce risk and take a cut of the optimization.

Block does neither. It sits in the middle and takes a cut, relying on complexity and cost to maintain its position. What Block is about to realize is that AI also reduces the value of acquiring vs building.

The value proposition in acquiring used to be a 2-3 year head start. Now it is more like 6-12 months. I wish Dorsey and the remaining staff at Block all the best, but the future does not look bright.

And this narrative about AI replacing people is overblown. Growing businesses need people more than ever. Shrinking businesses try to paint over a lack of vision with AI hype.

Vendors spent years selling the data cloud built for analytics, and they think slapping “AI-Ready” on the same old architecture will do the trick. AI layers and semantic layers are mostly rebranded SQL engines and data catalogs.

SELECT *
FROM dataPlatforms
WHERE marketing = 'Hype'
AND reality <> 'Revenue';

The tape has tape on it at this point. Most businesses just finished ‘modernizing’ their data infrastructure, only to find out that in the time it took to modernize, that infrastructure became obsolete.

Executive leaders are realizing that they spent all that money and went through all that pain to upgrade to a platform that was modern 5 years ago. It does not support AI or agents, even though it has the ‘AI-Ready’ stamp.

C-level leaders just opened their wallets, and the business just finished a year-long migration. The paint is barely dry, and no one wants to talk about doing it all over again.

But the sweet, sweet AI ROI will not flow until they do.

I talk about Continuous Transformation because that is the new business reality. Technology is never done, so every architectural component must transform to keep pace. It is not enough to look at what the platform does today.

Will the vendor’s roadmap keep it current for the next 5 years so the business can generate positive returns?

Answering that question requires the business to define what it plans to build for the next 3-5 years. However, the business is waiting on the technology side to explain what comes next, so they know what is possible.

It is yet another example of what happens in businesses that have a Missing Middle. Technical strategy and platform roadmaps must be forward-looking and prescriptive.

Both must be built from opportunity discovery that is driven by pragmatic futurism, not hype cycles. Missing Middle roles (AI Strategist, AI Product Managers, and Strategic Technical Leaders) fill the gap.

Hire them or enjoy your tape salad.

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