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

I'm Ravit Jain — the Founder & Host of The Ravit Show, a platform dedicated to meaningful conversations with the people shaping the future of Data and AI.

What started as a passion for learning from industry leaders has grown into a global media platform that reaches millions. Today, I spend my time interviewing experts, covering major tech events, and working closely with companies to help them share their stories in a way that truly connects with the community.

Over the past few years, I’ve had the chance to:

Host 550+ interviews with data and AI leaders, founders, engineers, and product thinkers

Collaborate with 200+ companies — from fast-moving startups to nearly every major tech giant

Cover 100+ conferences across the world, sharing real-time insights and backstage conversations

Support 200+ partner campaigns to amplify their product launches, webinars, reports, and community efforts

My focus is always on creating content that is useful, grounded, and easy to understand — no hype, just real value. Whether it's discussing GenAI, data quality, modern architecture, or what it takes to scale AI responsibly, my goal is to keep the conversation practical and community-first.

If you’re building in this space or want to share what you’re working on, I’d love to connect and learn more.

You can reach out to me at Ravitjain0909@gmail.com for The Ravit Show queries :)

Ravit Jain

Most AI analytics platforms assume one thing. Your data lives in one place. Kapil Chhabra, Co-Founder and CPO at WisdomAI on The Ravit Show challenged that assumption immediately. Enterprises are distributed. Their data is fragmented across clouds, warehouses, operational systems, and business units. Forcing everything into a single layer before AI can work is slow and expensive.

That is why WisdomAI introduced Federated Agentic Intelligence.

Instead of centralizing first and analyzing later, the system works across distributed sources. It assembles context at runtime. We spent time on what they call the Enterprise Context Layer. Without context, AI gives generic answers. With context, AI understands how metrics connect, what definitions mean, and how governance rules apply.

Kapil was clear that federation is not a feature. It is a design principle for the modern enterprise.

We also talked about what is next on their roadmap and what capabilities they are most excited about over the coming year. The focus is less on flashy features and more on depth, reliability, and scale.

If your organization is struggling to move from AI experiments to AI that executives actually trust, this conversation goes deep into architecture decisions that matter.

#data #ai #gartnerda #wisdomai #theravitshow

We have been building dashboards for 20 years. Now everyone is adding AI on top of them. But what if the real issue is the stack itself? That is where my conversation with Soham Mazumdar, Co-Founder and CEO, WisdomAI went at Gartner D&A on The Ravit Show!!!!

WisdomAI calls what they are building “Agentic Analytics.” Not a chatbot on top of BI. Not a copilot that still depends on humans to interpret everything. We talked about what is fundamentally broken in today’s analytics world:

- Dashboards answer questions you already thought of
- Executives need answers to questions they did not know to ask

Soham shared how enterprises are moving from static reporting to agents that reason across metrics, detect issues, and explain why something happened. The trust problem came up quickly. Most AI analytics tools look impressive in a demo. Very few hold up under real enterprise scrutiny.

We also discussed a real customer story with Cisco and what changed after deploying WisdomAI. The shift was not just faster answers. It was decision confidence.

Looking ahead, Soham believes analytics teams will not disappear. They will evolve into designers and supervisors of intelligent systems that operate continuously across the business.

For enterprise leaders rethinking the future of BI, this was a forward-looking and very practical discussion.

#data #ai #gartnerda #wisdomai #theravitshow

Your AI has a context problem. It’s not the model. It’s context!!!! That was the mic drop from Prukalpa ⚡, Co-Founder & Co-CEO, Atlan on The Ravit Show when we kicked off this conversation. And honestly, it set the tone for everything that followed.

We spoke about why so many AI projects stall. Not because the model is weak. Not because the team is not smart. But because the data lacks shared meaning. No common definitions. No clear ownership. No business context.

Here is what we unpacked:

• What a “context layer” actually means in simple terms
• Why this idea is suddenly everywhere at Gartner this year
• Where the context layer fits in the modern data stack
• What Atlan is building to make context usable, not theoretical
• The one demo every data leader should see before leaving Orlando

One big takeaway:
If your AI does not understand your business context, it will confidently give you the wrong answer.

If you are at Gartner this week, stop by Booth 313. See how context is being turned into something real, usable, and operational.

#data #ai #gartnerda #atlan #theravitshow

BREAKING announcements from the front row at New Relic Advance 2K26. Today’s announcements from Ashan Willy (CEO, New Relic) and Brian Emerson (CPO, New Relic) made one thing clear: This is not just a product update. It is a shift toward intelligent, agent-driven operations. Here are the biggest announcements from the stage 👇

1. The Agentic Platform for operational AI
New Relic introduced a platform designed to let teams build and orchestrate AI agents that can investigate incidents, automate workflows, and reduce manual toil across production environments. The idea is to move from passive monitoring to systems that actively execute tasks

2. SRE Agent to automate investigation and response
A new AI-driven SRE agent uses telemetry signals across logs, metrics, traces, and dependencies to prioritize alerts, identify root causes, and guide remediation workflows

3. Intelligent Root Cause Analysis and smarter alerting
The platform now focuses on reducing noise and narrowing down the most probable failure paths quickly so teams can respond with clarity instead of chasing alerts

4. Intelligent Workloads linking performance to business impact
New capabilities aim to connect technical telemetry directly to user experience and revenue impact so organizations can understand not just system health, but business risk

5. Stronger OpenTelemetry and telemetry pipeline simplification
New Relic is also investing heavily in making telemetry ingestion and pipeline health more transparent, helping customers scale observability without increasing operational overhead

But what stood out beyond the announcements was hearing customers talk about this shift.

I joined the session “Customer Panel: Intelligent Observability in Action” with leaders from Nordstrom, Turo, and New York Life alongside New Relic’s team.

The conversation was less about dashboards and more about outcomes.

Customers talked about:

• Managing the growing complexity of AI-driven systems
• Moving faster without increasing operational risk
• And using observability as a way to connect engineering work to business impact

It was clear that for them, observability is becoming a strategic capability, not just a monitoring tool.

Why this matters

Engineering teams today lose a huge amount of time to firefighting, alert noise, and manual investigation. The promise of these announcements is simple:

- Reduce operational toil
- Speed up decisions
- And let teams focus on innovation instead of maintenance

If New Relic can execute on this vision, observability platforms will stop being dashboards and start becoming operational decision systems

From what I am hearing in sessions and hallway conversations, this shift toward agent-assisted operations is arriving faster than most organizations expected.

More updates coming live from the floor.

#Data #AI #NewRelic #Observability #AI #SRE #DevOps #PlatformEngineering #TheRavitShow

At New Relic Advance 2026 in San Francisco, one theme was impossible to miss. Observability is no longer just about seeing problems. It’s about fixing them. I caught up with Ashan Willy, CEO of New Relic, to talk about what this shift really means for customers.

The biggest highlight was the launch of their SRE Agent and the broader move toward an agentic platform. What stood out in our conversation was not just the technology, but the reason behind it.

Customers are overwhelmed by signals, alerts, and dashboards. They don’t need more data. They need faster action.

That is exactly what New Relic is aiming to solve. Closing the gap between insight and execution so engineers spend less time diagnosing and more time delivering.

Compared to last year, this feels like a real strategic inflection point. The conversation is no longer about adding AI features. It is about redesigning workflows around AI-native operations.

The big question now is not whether agents will assist engineers. It is how much responsibility they will take on as trust grows. If this launch works, by next year we should see fewer alerts, faster resolutions, and teams spending more time building instead of firefighting.

#Data #AI #NewRelic #Observability #AI #SRE #AgenticAI #TheRavitShow

AI will fail without strong data foundations. At the DataDriven Conference 2026 by Reltio, I sat down with Bronwen Schumacher from ZS to talk about what it really takes to make enterprise data ready for AI.

Bronwen leads the Reltio partnership at ZS, a global consulting firm deeply rooted in life sciences and healthcare. ZS has been one of Reltio’s earliest partners, working together for 13 years to build modern Master Data Management solutions for global pharma companies.

This year, they won Innovation Partner of the Year for their work integrating ZS’s platform, ZAIDYN, with Reltio. The goal is clear. Deliver an out-of-the-box data and analytics solution that helps organizations, including smaller pharmaceutical companies, master their data faster.

We also spoke about what 2026 really demands from enterprises. The focus is shifting toward Agentic MDM, where strong data quality, governance, and change enablement become the backbone for AI. Without organizational buy-in and clean, trusted data, large-scale AI programs simply do not scale.

Beyond pharma, ZS is also expanding into travel and hospitality, bringing similar data discipline to customer loyalty and experience use cases.

This was a grounded conversation about how real enterprises are preparing their data before chasing AI.

Sharing the full interview here.

#data #ai #datadriven #reltio #theravitshow

Some events are about trends. Others signal where the industry is actually going next. I am in San Francisco today, getting ready for New Relic’s Advance tomorrow, and this one feels like the latter.

Over the past year, almost every conversation with engineering and platform teams has circled back to the same theme. AI is helping teams move faster, but it is also making systems harder to manage. More signals, more dependencies, more pressure to react quickly.

Register here, you can join virtually too:
https://lnkd.in/dsCfKaFC

That is why this event matters. We are expecting one of the biggest waves of product releases New Relic has announced so far, all focused on helping teams move from simply seeing issues to actually resolving them faster and with more confidence.

I will be there with Aditi Jain, meeting teams, attending sessions, and sharing what actually matters once the announcements land.

If you have been thinking about joining, this is your last chance to register. And if you cannot make it in person, the virtual option makes it easy to follow everything as it happens.

Register here, you can join virtually too:
https://lnkd.in/dsCfKaFC

Should be an interesting one. Stay tuned.

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I am in Orlando for the Gartner Data & Analytics Summit 2026. And one company I am excited to cover is WisdomAI. I will be meeting their team on the show floor, interviewing their leadership, and going deep into how they are helping enterprises move beyond dashboards into agentic analytics. Find more details here – https://lnkd.in/eEAfAqjr

From what I am hearing, their booth is going to be one to watch. Live demos. Strategic 1:1 conversations. Giveaways. Raffles. Swag. And most importantly, real conversations about how enterprises can scale AI with context and clarity.

They are also hosting a fireside session featuring CEO Soham Mazumdar alongside Raj Kamra an enterprise AI leader from Cisco, focused on what it truly takes to operationalize AI across large organizations.

The energy around AI at this year’s summit feels different. It is less about experimentation and more about execution.

If you are heading to Gartner next week, make sure WisdomAI is on your list.

I will be sharing interview highlights and behind-the-scenes insights directly from the show floor.

Orlando, see you soon.

#data #ai #gartnerda #wisdomai #theravitshow

Bangalore always brings great energy when it comes to data, cloud, and AI conversations. Excited to be heading there the second edition of the Commvault Cloud Summit 2026 on March 5th. More details here
https://lnkd.in/daHXC4KT

I will be hosting a special panel with Tim Zonca, VP Product and Portfolio Marketing, and Woon Ho Jung, CTO Cloud Native at Commvault, where we will focus on something many teams are actively dealing with today protecting AI workloads, data pipelines, and building true cloud native resilience.

As AI adoption grows, Cloud, Platform, DevOps, and SRE teams are under pressure to ensure systems are not just fast but reliable, recoverable, and secure. This session will unpack what AI ready infrastructure actually requires and how teams can design resilience into their platforms from day one.

If you are working on modern cloud platforms or data systems, this summit will be worth your time.

More details here
https://lnkd.in/daHXC4KT

#data #ai #cloud #shift2026 #aiprotection #devops #sre #theravitshow

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