DataFramed, Distilled. The Best Moments of 2025 with Richie Cotton

at's a relieving answer that data analysts aren't going to disappear anytime soon. Many jobs are certainly going to change though, and one of the most intriguing ideas is that of having AI colleagues. I spoke to Karen Ing, the Head of Product at HubSpot, about the idea of humans and AIs working together.

#319 Building & Managing Human+Agent Hybrid Teams with Karen Ng, Head of Product at HubSpot

"I do think a lot of the ways that we work together as a team is going to be really important in our agent world. We believe a lot in hybrid teams, and I believe that's the way that things are going to grow. And hybrid teams are both humans, supercharged with AIs, and agents that need to work together with humans and other agents.

I love working with people, but being a boss of some bots does seem like an easier path into management."

One role that is absolutely scorching hot right now is data engineer. Without the right data in the right format being delivered to the right people and applications, all your analytics and AI efforts are meaningless.

Our next guest is Deepak Goyal, CEO and founder of Azurelib Academy. Deepak has taught over a hundred thousand professionals in data engineering.

In his episode, Deepak provide in depth technical advice on what skills you need to get hired as a data engineer. Here though, I'd like to highlight a soft skill he mentioned.

#295 How To Get Hired As A Data Or AI Engineer with Deepak Goyal, CEO & Founder at Azurelib Academy

"As a Data Engineer, you should be able to communicate, right? Because I mean, maybe compared to the software engineer, data engineer has to move to communicate because probably they have to talk to the lot of stakeholders to understand what kind of a you know, the output or the results they are looking for."

It feels like whatever role I discuss, whenever I ask an expert what the most important soft skill is, communication comes top. So your homework for the holiday season is to practice chatting to people. Remember to listen to them too.

Zooming out, I have a nebulous sense that the shape of careers is changing. I asked Abhijit Bhaduri about this. He's one of the leading voices on talent development and the former General Manager of Global Learning & Development at Microsoft.

#294 Six Skills Data Professionals Need To Succeed with Abhijit Bhaduri, Brand Evangelist & Former General Manager of Global L&D at Microsoft

"You can either choose to do specialization in different aspects of data, or you can choose the kind of field that you can work in, whether it's healthcare or analytics or retail, or you can then choose whether you want to work solo and become a contractor, or do you want to get employed? Do you want to do part-time? Do you want to go into an office? Do you like to work remote? Each option that you create creates a set of possibilities and takes away a set of possibilities."

It's a benefit and a challenge that career paths are getting more fluid. You have more choices about your career, but that means you need to make good decisions.

Career challenges also exist for managers. I chatted to Bilal Zia, the Senior Director and Head of Data Science and Analytics at Duolingo. 

DuoLingo is currently a highly successful, data-driven company. However, when he joined, the data team was struggling and performance, and wasn't trusted by the rest of the organization. He has a great story about how he turned around his team. Here's a highlight.

#333 Creating an AI-First Data Team with Bilal Zia, Head of Data Science & Analytics at DuoLingo

"The biggest success I had as a leader was investing in the people, not in ideas. That order is really important. Ideas don't become successful if you don't have good people in the team."

That wraps up our career highlights. 

Once you have your talent sorted, the next question is: how do you track what’s happening in your business? The answer is, of course, business intelligence.

Howard Dresner — often called the godfather of business intelligence — coined the term BI back in the 1980s. These days he runs Dresner Advisory Services, which publishes some of the most influential industry reports.

Here’s Howard on BI in 2025.

#310 The State of BI in 2025 with Howard Dresner, Godfather of BI

"We have so many external forces operating our organizations, data is your friend. You want to know when things are changing and how they're changing as quickly as possible so that you can react. mean, at the end of the day, that's the whole point of business intelligence. How do we respond to change and execute as quickly as possible?"

One quirk of BI is that the tooling has become bifurcated. There is one set of tools for analysts, another for business users.

This issue annoyed Colin Zima so much that he decided to build a new BI platform. Colin is CEO at Omni, and his episode of DataFramed discussed what the next generation of BI tools needs to deliver.

#306 The Next Generation of Business Intelligence with Colin Zima, CEO at Omni

"The only reason that Omni exists is because I sort of experienced this frustration as a data person over, like the last 20 years, where I had touched a bunch of different tools and sort of felt the good and the bad of them, and I wanted them all to exist in one place. So the very simple story is I started in finance, grew up in Excel, sort of learned everything data from an Excel context, and ended up later in BI. Got exposed to looker data modeling like very heavy SQL concepts. And I was always frustrated that tools couldn't do both of those things at the same time. Like there's a time and a place to cut corners and go really fast and be a little messy. And there's also a time and a place to operate like a software engineer and have governance and centralization, and we just kind of never felt like it was getting executed."

Of course, the secret to analytics impact isn't just creating the perfect dashboard, it's communicating the results well. Data Storytelling has long been one of the most important forms of artistry for data practitioners, so I'm a sucker for any new ideas on how to tell better data stories.

I chatted with Kat Greenbrook, the author of The Data Storyteller’s Handbook and founder of Rogue Penguin, about her framework for data storytelling.

She had some great advice on achieving influence through data storytelling.

#298 Data Storytelling Skills to Increase Your Impact with Kat Greenbrook, Author of The Data Storyteller's Handbook

"Shaving this understanding of the problem, the goal, the action, the impact, the hierarchy in which that we are existing, it can help when we share the work that we do. we are able to influence better. We are able to share more of the context around the work that we do. So we're not just sharing, I'm building this model. We're sharing, this is a problem that the business have that this model is going to solve. And the flow on effect from that is that I'm going to create this positive impact that's going to be good for the business."

Data storytelling is such a broadly applicable skill that it can be considered part of fundamental data literacy. That is, core skills around data that everyone needs.

Achieving data literacy for everyone has been an important goal for at least the last decade. In recent years, there is also a need for everyone to have AI literacy.

I spoke to Jordan Morrow, who is often called the godfather of data literacy. He’s Senior VP of Data and AI Transformation at AgileOne, and he’s writing his fifth book on the topic.

Here’s Jordan on why literacy isn’t about training courses — it’s about behaviour change.

#323 The Evolution of Data Literacy & AI Literacy with Jordan Morrow, Godfather of Data Literacy

"Some of the first steps of a change management within data, AI, any of that is number one, understanding what the true objective is. And the objective is not to teach data literacy course. The objective is to change people's behavior. That is hard, right? That is very hard. So the key besides the right objective is what is your communication strategy around this? You can't just send people an email that says you have mandatory training. The moment you do that, you've lost. They there needs to be messaging around it. They need to understand what's in it for them. They need to have ensure that leaders truly buy in. They need to understand how much time this is going to take. All these things add up."

I was rather pleased that Jordan's message sounds suspiciously similar to the advice DataCamp's Customer Success team gives to enterprise customers running training initiatives.

I've been incredibly restrained so far in not talking about the biggest technical topic of the year. I can't delay any longer though; it's time to talk about AI agents.

During his time at Meta, our next guest, Douwe Kiela, helped invent the popular AI knowledge lookup technique, "retrieval-augmented generation", a.k.a. RAG. He’s now the CEO at Contextual AI, where he’s building a modular agent creation platform.

Here’s Douwe on what an AI agent actually is.

#305 RAG 2.0 and The New Era of RAG Agents with Douwe Kiela, CEO at Contextual AI, Adjunct Professor at Stanford University, Inventor of RAG

"I think a much more useful definition of an agent is just something that actively reasons. So something that thinks about what it's doing, formulates a plan, executes on the plan, and then can revise that plan based on the information that came in. So that's active reasoning. And so the really exciting technology that has enabled all of this is just test time reasoning and the insight that shifting the compute from the training side through the test time inference side actually has very, very nice properties."

The last point about generative AI being able to reason at inference time, that is, after you give it a task, is key to enabling agents.

One of the big challenges of enterprise AI systems is keeping them fed with up to date data sources. I spoke to Jun Qian, VP of Generative AI Services at Oracle, about these maintenance issues, and he suggested that RAG systems, combined with your existing data infrastructure, are key to success.

#316 Enterprise AI Agents with Jun Qian, VP of Generative AI Services at Oracle

"If you already have an existing knowledge base, you can easily add another layer of RAG on top of it. Build a decent enough RAG system and however your data is refreshing, your data management system can be as the same as what you have today. You don't have to redo everything. So this is one possible way to move this RAG system forward quickly."

Perhaps the biggest problem of all for enterprises building with AI is AI governance. Manasi Vartak, the Chief AI Architect at Cloudera, had a great take on the relationship between data governance and AI governance.

#328 The Challenges of Enterprise Agentic AI with Manasi Vartak, Chief AI Architect at Cloudera

"Data is a part of AI governance, but AI governance is a much bigger umbrella, but it does start with data. Where did the data come from? What was the lineage of that data? If you think about the lawsuits that currently happening, that Anthropic and OpenAI are facing, the core of them is, what data did you use to train the model? That's a data governance and lineage issue. If you could trace it down and say this model was trained on these 10 data sources. It's an easy question. The challenge is the data goes through so many steps of pre-processing and post-processing, that by the time the model gets to it, it's hard to figure out where it came from. For AI governance, we need to solve those kinds of fundamental issues around data governance."

One of the biggest questions around AI Is what should you build with it? I asked Jerry Liu, the CEO and co-founder of LlamaIndex.

#308 A Framework for GenAI App and Agent Development with Jerry Liu, CEO at LlamaIndex

Everybody is using coding assistance these days. You know, if you ask any engineer at a tech company, they're all using like cursor, windsurf, chat, bt, et cetera. And then of course, like, you know, you're seeing AI agents deliver real ROI in certain, uh, enterprise verticals. So this includes like customer service. Uh, it includes like it help desk stuff. This also includes, you know, areas that we're actually very specifically excited about, which is. Document workflow automation, you know, whether you're a finance team or procurement, legal, being able to sort through massive volumes of unstructured data and getting insights from it."

That's a great high-level answer, but I'm also curious about some of the industry-specific applications. Let's start with healthcare.

Aldo Faisal is a Professor of AI and Neuroscience at Imperial College London and runs the Nightingale AI project focused on medical decision-support systems. That is, AI advice for doctors.

#312 Can we Create an AI Doctor? with Aldo Faisal, Professor in AI & Neuroscience at Imperial College

"I think the real big wave that's going to wash over the whole field is now what we call ambient intelligence and the whole space of AI for operational improvements. So that's not medical activity per se, but for example, transcribing the conversation between a doctor and the patient."

Over in the world of finance, Andrew Reiskind is the Chief Data Officer at Mastercard. As well as discussing what his department is building, he had a good take about why you need to worry about data quality before going all in on AI.

#288 How Generative AI is Transforming Finance with Andrew Reiskind, CDO at Mastercard

"An interesting exercise we went through earlier last year was finding out where our priorities were. Our priorities are still very much about data quality. The AI basically consumes much more data, much faster, with less human intervention. And so, the quality of the data, the understandability of the data has become that much more important. It’s this feedback loop that is just strengthening the need of just getting the basics right to feed the AI so that we know what the quality metrics are relative to completeness, to accuracy, that is mission critical."

Once you have your infrastructure in place, the challenging part of an AI transformation program is the people and processes.

Steve is the CEO of Boomi and former CEO of Marketo. He’s been leading AI-driven transformation long before most companies made public declarations about going ‘AI-first.’

Here’s Steve on the human side of AI change.

#311 The Human Element of AI-Driven Transformation with Steve Lucas, CEO at Boomi

"The entire world is going to pivot from deterministic processes to non-deterministic, agentic process. That's what's going to happen. I'd say probably seven out of 10 processes that have ever been built will be augmented, rewritten, enhanced with AI agents to be more non-deterministic or agentic."

Our next guests, Iwo Szapar and Eryn Peters, are the founders of AI Maturity Index. 

Iwo had useful advice on how organisations can build momentum through low-code experimentation and internal hackathons.

#303 Increasing Your Organization's AI Maturity with Iwo Szapar & Eryn Peters, Founders at AI Maturity Index

"One of the best tactics that really works for organizations is to organize hackathons. // hackathons can be just a few hours session where you're using no code, low code tools or the platforms like cursor, replete, et cetera, where you build some prototypes as you go without the need to understand how to code or even to work with the data. You just experiment with this. But what is really cool here is that you're building solutions for your own problems, for your own challenges, right? So you can immediately see potential value that it can create, which then immediately creates a bind for such tools."

While generative AI is getting the headlines, predictive AI is still responsible for most of the value created in day-to-day decision-making. Let's talk data science!

Jean-François Puget and Chris Deotte are both Kaggle Grandmasters and distinguished engineers at NVIDIA. They spend their days pushing the boundaries of model performance and GPU-accelerated workflows.

Here’s their take on what’s changing in data science.

#286 Data Science Trends from 2 Kaggle Grandmasters with Jean-Francois Puget, Distinguished Engineer at NVIDIA & Chris Deotte, Senior Data Scientist at NVIDIA

"I've worked at NVIDIA six years and I think one of the biggest things I've seen in these years is accelerating all these algorithms on GPU. The significance of that is, besides just obvious speed, is that we're actually, this can allow us to build new hybrid models. So I've actually recently in some competition, seeing people fusing deep learning models. together with a machine learning model. example is maybe you're doing an image regression problem, but you use a an image model as a backbone to extract embeddings, and then you regress the embeddings using a support vector machine. But previously, a support vector machine running on CPU kind of wasn't fast enough to keep up with the iterative cycle of deep learning. So because of the speed now, people can kind of incorporate KNN, you know, KNN to, to compare embeddings to find similar images or similar texts. So you're seeing a lot of more uses of machine learning. So that's exciting that these new, new ways to approach problems."

Meri Nova is an AI engineer and founder of Break Into Data. We had a good discussion of how generative AI has completely overhauled natural language processing, a type of machine learning for text.

Meri also highlighted one of the biggest achievements of generative AI: it's fundamentally changed how we communicate with computers.

#275 Did Gen AI Kill NLP? with Meri Nova, Technical Founder at Break into Data

"I feel like there's a lot of opportunity when it comes to AI agents and AGI in general, just because now that we understood how to communicate with computers with English language, which is super exciting.It can do things for us just because the things that we need to manage are in English as well. And before we didn't have the capacity to navigate these or for the computer to navigate these spaces. So I think this is very exciting just to see how the field is evolving"

Some of the unsung heroes of AI are the people creating datasets to feed into the foundation models. 

Our final guests are Wendy Gonzalez, CEO at data annotation Sama, and Duncan Curtis, their SVP of GenAI..

Wendy has a surprisingly fun take on when you want to use synthetic a.k.a. "made up" data for your AI models, with an example for self-driving cars.

#307 Human Guardrails in Generative AI with Wendy Gonzalez & Duncan Curtis, CEO & SVP of Gen AI at Sama

"Wendy Gonzalezsynthetic data has, has, has good, good uses or great uses in edge cases. So imagine like nobody wants to real time capture, you know, a stroller in the street or an empty stroller, a straw with a baby in it. Or I heard I actually from a from an audio company client where they're like, you wouldn't think of this, but sometimes hog's like drop out of the sky and get in front of vehicles. That's probably not something you're ever going to catch. It has happened before. It's probably not something you're gonna catch, you know, in any regular real life."

And that’s our 2025 roundup. A year of huge change in careers, BI, literacy, agents, applications, and the foundations of responsible AI.

Wherever 2026 takes us, it’s clear the pace isn’t slowing down. Thanks for listening, and we’ll see you next week.