An Experiment - Top Data Trends for 2025 with Coalesce and Google NotebookLM - Episode #55

Welcome to the Agile Data Podcast, where we talk about the merging of agile and data ways of working in a in a simply magical way. Welcome to the Agile Data Podcast. I'm Shane Gibson, and today we've got a little bit of a different episode. Instead of a human guest, today we have two robot guests. Decided to try an experiment. My experiment was, can I upload a white paper to LLM, have it generate a podcast, listen to that podcast in my daily walk, and see whether that summary removes the need for me to actually read the white paper? So in this case, I have grabbed a white paper called, uh, Top Data Trends for 2025 from Coalesce, uploaded it to the Google Notebook LLM, and got it to generate a podcast with of two hosts chatting about the white paper. Have a listen, let me know what you think. Really keen to understand, do you think this approach is useful or is it a load of bollocks? So with that, I will let the robots take it away and hope that you have a simply magical day.

Okay, so picture this, you're prepping for like a huge strategy meeting all about data and AI and the future of it all. You know, you've got this mountain of reports But time, of course, is not on your side, and that's where we come in. This deep dive is going to distill all the key insights from Coalesce's report. It's called The Top Data Trends for 2025. It's a great report, brings together all this wisdom from a bunch of industry experts. By the end, you'll be armed with all the key trends and some pretty surprising predictions, all without having to like wade through pages of jargon. You know what's really fascinating is that this first trend, it's already well underway. We're talking about that evolution from data pipelines as we've known them traditionally to something so much more sophisticated, massive knowledge pipelines.

Okay. Knowledge pipelines. It sounds a little bit like just fancy rebranding. What's the real difference?

I think it's a complete change in how we think about data. It's not just tables and columns. It's about AI understanding all the relationships and the context hidden within the data itself. So think about it. Knowledge pipelines, they teach AI to reason about the data, not just process it. So it's not just about moving data around, it's about turning it into something that AI can actually learn from and use to make smarter decisions. And I can see why that's a big deal for like anyone who works with data, especially with all the excitement around generative AI right now.

Right. And just to give you an idea of like how quickly this is all happening, one expert in the report, John Cosgrove from CloudWorks, he predicts that by the end of 2025, 10 to 20% of all customer inquiries, they will involve interacting with a large language model.

Wow. So we're talking about a future where AI isn't just like this behind-the-scenes tool. It's going to be directly interacting with customers. And if companies aren't prepared for that, they're going to be left behind.

Exactly. We're sitting on these mountains of data in our data lakes. Yeah. Documents, videos, images, all sorts of stuff. Knowledge pipelines are all about making that ready for AI to understand it. And act on it, which is absolutely crucial in the world of generative AI. Lagging behind on this—

Yeah.

—is going to put companies at a real disadvantage. All right.

So knowledge pipeline's definitely something to keep an eye on. But what's interesting is that the report also challenges another common assumption, that having all your data in one place is the ideal. Right.

And that's where this idea of multi-engine compute comes in. It's about recognizing that different compute engines have different strengths. And weaknesses. Some might be great for real-time analytics, while others excel at handling those complex machine learning models. The future is going to be about picking the right tool for the job.

So instead of trying to force fit everything into a single system, you're saying we need to think about having like the flexibility to use different tools for different tasks.

Exactly. And to make that work, you need a unified storage layer that acts as the foundation. It's kind of like having a central garage where you store all your tools. You could take out and use whichever tool you need for a specific project.

Makes a lot more sense than trying to use a wrench to hammer in a nail, right?

Exactly. And what's interesting is that even Snowflake, which has been such a big advocate for centralized data platforms, they're now pushing for more flexible approaches. They're encouraging the use of open source formats like Iceberg Tables, specifically because it allows for this multi-engine approach.

So even the big players are starting to recognize that one size doesn't fit all when it comes to data. Speaking of big shifts, the report also predicts that 2025 will be the year of practical AI, moving beyond all the hype and focusing on real-world results.

Yeah, 2024 was the year of bold AI experiments, but 2025 is going to be all about ROI and tangible business value.

It's like the early days of the internet when everyone was building cool websites, but they didn't really have a clear idea of how to make money. It was only when businesses started focusing on e-commerce and practical applications that things really took off. That's a great analogy.

This focus on practicality, it is gonna be challenging for some AI startups. I've never figured out their business model yet, but ultimately it's gonna lead to more sustainable solutions for those who can demonstrate real value.

So it's survival of the fittest in the AI world. Those who can't adapt are gonna get left behind. The report also talks about this interesting concept of industry amnesia. Which is a little unsettling.

It's this tendency to constantly reinvent the wheel, especially in fast-paced fields like technology. We kind of forget the lessons of the past, and we end up repeating the same mistakes.

And the report suggests that AI agents might actually help us overcome this, which is kind of ironic.

Right, it's like using AI to fight AI-induced amnesia. These agents can help us learn from the past by capturing and analyzing these vast amounts of historical data, essentially filling in those knowledge gaps.

So, AI could actually help us avoid repeating the same mistakes over and over again. But as one of the experts in the report, Kent Graziano, points out, AI is costly. And just because you build it doesn't mean they will come. We can't just throw AI at every problem and expect magic to happen. Absolutely.

That's why AI governance is becoming so crucial. We need to be really thoughtful about why we're using AI and what the potential consequences might be. Yeah. We need to establish these clear guidelines for development. Development, deployment, and use, just to make sure it aligns with our values and it doesn't lead to unintended harm.

So AI governance is about making sure that AI serves us, not the other way around.

Exactly. And it's also about ensuring that AI initiatives are actually solving real business problems and delivering that tangible value.

Which brings us to another crucial point, that connection between data quality, company culture, and AI success.

Yeah. And this is something that just can't be overstated. Data quality. It's not just a technical issue. it's a cultural issue. If data literacy isn't valued and prioritized throughout your entire organization, AI initiatives are going to struggle. I mean, think about it. If people don't trust the data or understand how to use it effectively, even the most sophisticated AI is going to produce meaningless results.

Yeah, it makes you stop and think, right? Is data literacy a priority in your own workplace? Is data quality everyone's responsibility?

It's a question worth pondering. It definitely is. And this emphasis on data quality becomes even more important. When we're talking about the next trend, moving beyond all the hype and into real-world AI deployments at scale. Okay, let's get practical.

How do we bridge that gap between all the excitement around AI and actually achieving that real business value?

That's the challenge, isn't it? And the experts in the report, they all seem to agree that the answer lies in focusing on AI deployments at scale. However, that brings its own set of challenges.

Like what? What do we need to be prepared for?

Think about things like ML Ops, which is all about managing the entire lifecycle of those machine learning models, and AIOps, which focuses on using AI to automate IT operations. Governance, as we talked about earlier, is also even more crucial when you're deploying AI across an entire organization. It's not just about building a cool model. It's about building a sustainable system that can actually deliver results reliably and responsibly.

So it's a whole new level of complexity, and it sounds like data teams are going to play an even bigger role in all of this.

Absolutely. Gu Shi, who is the head of data engineering at Group 1000, 2001. He had a really insightful point in the report. He argued that data teams are going to become even more important in this world of practical AI, not less. They're the ones who have that deep understanding of the data, the infrastructure, and the business needs. They're kind of the bridge between all the hype and the actual delivery of business value.

That's good news for all the data folks out there. Sounds like their skills are going to be in high demand. So we've talked about knowledge pipelines. Multi-engine compute, the shift toward practical AI, and that critical importance of data quality. What other trends are shaking up for 2025?

Well, remember how we were talking about the need for flexibility and choosing the right compute engine for the task?

Yeah, that right tool for the right job idea.

Right. That's where open table formats come into play. Formats like Iceberg are becoming essential, especially when it comes to data lakes. They provide a way to structure your data, That allows for that flexibility and avoids getting locked in with a single vendor.

So it all ties back to that idea of avoiding that one-size-fits-all approach. You need the freedom to use different tools without having to constantly worry about those compatibility issues. Exactly.

It's all about having options and ensuring that interoperability. And speaking of having options, let's not forget about the vast world of unstructured data. Unstructured data.

That's all the stuff that doesn't fit neatly into rows and columns, right? Think audio, video, text documents, all that messy but potentially valuable information. It's always seemed so difficult to manage, let alone analyze.

It has been. But AI is changing the game. Companies are now finding ways to structure this previously untapped data, opening up these incredible new possibilities for AI-driven insights.

Do you have any examples of how that's actually working in the real world?

The report highlights a really cool case study of a Fortune 500 insurance company. That was using AI to analyze their customer service calls. They took all those audio recordings, which were previously just these unstructured blobs of data, and they used AI to generate transcripts and apply a rating scale to them. Essentially, they turned that unstructured data into this new structured dataset of customer satisfaction scores.

So they took this data source that was previously just sitting there gathering dust and turned it into something they could actually analyze. And learn from. That's impressive. What kind of insights did they get?

With this new dataset, they were able to apply machine learning models to uncover deeper trends in customer satisfaction. They could predict customer churn, identify areas where they needed to improve their service, and even personalize their service offerings based on individual customer needs.

That's a powerful example of how AI can unlock the value of unstructured data. It makes you realize how much potential is hidden in data sources that we might have previously overlooked. Exactly.

And this is just the beginning. As AI technology continues to evolve, we're going to see even more innovative ways to leverage unstructured data for business insights.

Okay, so knowledge pipelines, multi-engine compute, practical AI, data quality, OpenTable formats, and the rise of unstructured data. It sounds like the data landscape is going to be a pretty exciting place to be in 2025. What other big trends are on the horizon?

Remember the concept of data gravity we were talking about earlier?

You mean that idea that data attracts more data, kind of like a gravitational pull?

Yeah. And the report suggests that that concept is evolving into something even bigger: platform gravity. Platform gravity.

That sounds intriguing. Tell me more.

Platforms like Snowflake are becoming these centers of gravity, not just for data, but for applications, AI, and decision-making. We're talking about entire ecosystems built around a single platform.

So it's not just about bringing your data together anymore. It's about bringing everything together, the tools, the applications, the decision-making processes, all in one place.

That's right. And while this might raise some concerns about vendor lock-in, it also solves a lot of those technical problems, simplifying data management and creating a much more unified and efficient workflow.

It sounds like a double-edged sword, but definitely a trend to watch closely. And as all of this data comes together on these platforms, how do we make sure it's being used effectively to benefit the business?

That's where the trend of data products comes into play. Companies are increasingly starting to treat data as a product, which means focusing on data quality, reusability, and aligning it with business needs.

So data as a product, it's like packaging data in a way that's actually useful and valuable to the business rather than just letting it sit there as a raw material.

Exactly. The report even predicts that smaller and mid-sized businesses, they'll start moving away from those large, expensive systems like SAP in favor of building their own purpose-built data platforms to support their data product strategy. That makes sense.

It's about creating the right infrastructure to support the specific needs of the business rather than trying to shoehorn everything into a generic solution. But data quality has to be a big part of this, right?

Absolutely. Justin Green, manager of business intelligence at Shane Coe, he put it perfectly in a report. He said people want trust, building something reliable and actionable. That you have confidence in means providing that transparency for all users.

Trust and transparency, key ingredients for any successful data initiative, especially when you're talking about treating data as a product. You know what really blows my mind? We've been talking about all these trends and we haven't even touched on what might be the biggest game changer of all: semi-structured data.

Prepare to be amazed. It's estimated that 90% of the world's data is semi-structured.

90%. That's a staggering amount. It really puts into perspective how much potential we're still unlocking when it comes to data.

It really does. And while it presents a huge opportunity, it also means we need to evolve our data management practices. We need to rethink our storage formats, our security policies, and governance frameworks to handle this massive influx of semi-structured data.

It's like we're entering a whole new era of data management. Where the old rules no longer apply?

That's a great way to put it. The report included this table from Cindy Howson, who's the Chief Data Strategy Officer at ThoughtSpot, which highlights how combining structured and semi-structured data can unlock some seriously game-changing insights.

Okay, I need to hear about these game-changing insights. Give me some examples.

Imagine analyzing customer loyalty by combining those call center recordings which are semi-structured, with customer sales data, which is structured, you can start to understand how something like the empathy of a call center agent correlates with customer retention.

So you could actually measure the impact of soft skills on business outcomes. That's incredible. What else?

Or in the insurance industry, think about combining satellite images of structures with insurance policies and claims data. You could identify potential structural weaknesses that might lead to future claims.

Wow. Talk about next-level risk assessment.

It doesn't stop there. In the supply chain, you You could combine satellite images of deforestation with data on units produced to track the environmental impact of production in real time.

That would be huge for companies trying to improve their sustainability efforts.

And in life sciences, you can combine clinician notes about adverse reactions with patient demographics to identify which patient characteristics might correlate with a particular adverse reaction.

That could revolutionize drug development and personalized medicine.

Those are just a few examples, but it really highlights the transformative potential of combining these different types of data.

It definitely seems like we're at a tipping point. The way we manage and analyze data, it's fundamentally changing. And those who embrace these changes are going to have a significant advantage in the years to come.

Absolutely. And as we move into this new era of data, it's important to remember that it's not just about the technology. It's about the people and the processes as well.

That's a great point. We can't just focus on the tools and algorithms. We need to develop a data-driven culture that values Quality, transparency, and ethical use.

Exactly. And that's where the conversation gets even more interesting. But we'll have to save that for part 2 of our deep dive.

So we just did a deep dive into some pretty mind-blowing tech trends, everything from knowledge pipelines to that untapped potential of semi-structured data. But like we hinted at, technology is only one piece of the puzzle, right? What about the human element? How does that factor into the future of data? Yeah, it's about remembering that data.

It's not just about ones and zeros. It's ultimately about people. It's about the decisions we make, the actions we take, and the impact that we have on the world around us. So the real transformation comes when we combine those technical advancements with a shift in our mindset about data as a whole.

So we need to move beyond just the technical aspects and consider that bigger picture, things like the ethical implications, the social impact, and the role data will play in shaping our future. Exactly.

And one of the big themes that jumped out from this Coalesce report is this idea of closing the gap between IT and the business side. Historically, there's been this disconnect between the folks who manage the data and the folks who use it to make decisions.

Yeah, it's like they're speaking totally different languages. Sometimes you've got the tech people talking about pipelines and algorithms while the business folks are all about revenue and customer satisfaction, and never the twain shall meet.

That's a great way to put it. But the experts are pretty clear. As AI becomes more like deeply woven into the fabric of how we work, that gap needs to close. One of the experts, Patrick Buell, who's the co-founder and VP of Consulting at Hakoda, he hit the nail on the head. He said technology is never the reason why a business can't succeed or transform. It always comes back to people, processes, and culture.

So it's about breaking down those silos between departments, getting everyone talking the same language. And making sure everyone understands how to use data to achieve those shared business goals. And that's where the whole idea of data literacy comes in, right?

Data literacy is crucial. And it's not just about having technical skills. It's about everyone understanding how to interpret the data, how to ask the right questions, and then how to turn those insights into action. Data literacy for everyone.

I love it. But getting a whole organization to embrace that seems like a pretty tall order.

Where do you even start? It starts with education and training for sure. But I think more importantly, it requires a real shift in mindset. We have to move away from this idea that data is solely the IT department's problem. Everyone needs to understand that data is everyone's responsibility.

Because at the end of the day, everyone's using data to make decisions, whether they realize it or not. Precisely.

From the CEO down to the newest team member, everyone needs at least a basic understanding. Of how data works, right? And how it can make them better at what they do.

So bridging that gap between IT and business is key, and building a data-literate culture is non-negotiable. What else is crucial as we navigate this increasingly complex world of data?

Another big theme that the report highlighted is this growing importance of governance, and not just for data itself, but for AI as well.

AI governance. That sounds like a pretty weighty topic. What exactly does that look like in practice?

It's about setting these clear guidelines and principles for how AI is developed, deployed, and used within an organization. It's about making sure that AI is used ethically, responsibly, and in a way that reflects the values of the company.

So it's like putting guardrails on AI development, making sure it doesn't go off the tracks and have unintended negative consequences.

That's a good way to think about it. Yeah. And as AI becomes more powerful, those guardrails become even more important. We need to be extra cautious about avoiding those unintended consequences. And any negative societal impacts.

This reminds me of that industry amnesia concept we talked about earlier. If we don't learn from our past mistakes, especially when it comes to powerful new technologies like AI, we're doomed to repeat them. That's a key point.

And we need to consider the ethical implications of AI from the very beginning. And building diverse and inclusive teams is a crucial part of that.

Because different perspectives and experiences are so crucial when it comes to identifying and mitigating Potential biases, right? We need to make sure that AI systems are developed and used in a way that benefits everyone, not just a select few.

Yeah, diversity of thought and experience is key if we want to avoid those blind spots and create AI systems that are equitable and inclusive. Hmm. For everyone. And that leads to another critical aspect of AI governance: transparency. We need to be open about how AI systems work, how they reach their decisions, and what data they're using.

Transparency builds trust, right? If people understand how AI is being used, they're less likely to be afraid of it.

They're more likely to use it effectively. Exactly. Transparency can go a long way toward addressing those concerns about bias, discrimination, and misuse.

So AI governance is about way more than just the technical details. It's about ethics, responsibility, transparency, diversity, all those human-centric elements that can so easily get lost in the rush. To adopt the latest and greatest technology. That's a great summary.

And those considerations are only going to become more critical as we head towards 2025 and beyond.

So we've got closing the IT-business gap, building a data-literate culture, and establishing those robust AI governance frameworks. Any other human-centric trends shaping the future of data that we should be aware of?

Let's circle back to the rise of data products, which we touched on earlier. Remember how we talked about treating data as a product itself? With a focus on quality, reusability, and making sure it aligns with real business needs.

Yeah, it's all about packaging and delivering data in a way that makes it genuinely valuable for the business.

Well, this trend, it's also changing the way data teams are structured and how they work. They're becoming less like data custodians and more like product teams responsible for the entire lifecycle of a data product, from that initial idea all the way to delivery and ongoing support.

So they need to think like product managers, considering things like user experience, customer needs, and market demand. Exactly.

And this shift, it requires a different set of skills and a whole new way of thinking about their work. Data teams need to become more collaborative, more customer-focused, and more agile in their approach.

It's like the difference between building a bridge and designing a smartphone, right? A bridge is static. But a smartphone is a dynamic product that's constantly evolving based on user feedback and market trends.

That's a perfect analogy. And this shift toward data products is changing the relationship between data teams and the rest of the company to data teams are becoming much more integrated into the business as a whole. They're working hand in hand with different departments to truly understand their needs and develop those data products that actually deliver that tangible value.

So it's all about breaking down those silos we talked about earlier, and this builds a more collaborative and data data-driven culture across the entire organization.

All connected. And this focus on data products, it's a major driver of innovation as well. As data teams focus more on delivering real value, they're more likely to experiment with new technologies, explore new use cases, and find creative ways to use data to solve those business challenges.

It sounds like this positive feedback loop. The more value data teams deliver, the more trust they build, and the more freedom they have to innovate and push boundaries.

That's exactly what we're seeing, and I expect even more exciting developments. In this area as we head towards 2025.

So we've got data teams evolving into product teams, working more collaboratively, and becoming these drivers of innovation. What other human-centric shifts should we be watching out for?

Well, there's this growing awareness of the ethical implications of data and AI. It's a topic that's been making headlines a lot lately, and for good reason.

Yeah, there's been a lot of talk about bias in algorithms, the potential for AI to be used for harmful purposes. And just the need to use data responsibly. It's a big concern for a lot of people.

And rightfully so. As these technologies become more powerful and more embedded in our lives, those concerns are only going to get amplified.

So what can we do about it? How do we make sure that data and AI are used ethically and responsibly?

Well, it all starts with education and awareness. We need to have these open and honest conversations about the potential benefits and risks of these technologies. And work together to develop those ethical guidelines for their development and use.

It's about having those tough conversations and getting everyone on the same page in terms of the values and principles that should guide our use of data and AI. Exactly.

It goes back to what we were saying about building those diverse and inclusive teams, different perspectives and lived experiences. They are absolutely essential to identifying and mitigating potential biases and making sure these technologies benefit everyone, not just a select few.

So again, it's not just about the technology itself, but about the people who are building it and the people who are using it. We need to be very conscious of the human impact of data and AI and commit to using these powerful tools for good.

That's a powerful message to leave with your listeners. As we move into this new era of data and AI, it's about more than just algorithms and insights. It's about the choices we make, the actions we take, and the impact we have on the world.

We've covered a lot of ground in this deep dive, from the tech trends that are shaping the future of data to the human-centric considerations that will ultimately determine whether we use these powerful tools for good or for ill. But there's still more to explore, and we'll continue this conversation in part 3 of our deep dive. We've explored all these exciting technological advancements, and you know, those crucial human considerations that are shaping the future of data. But let's shift gears a little bit and talk strategy. How can businesses actually use all of these trends to gain, like, a competitive edge and thrive in this rapidly changing landscape?

That's the million-dollar question, isn't it? It's not enough to just understand what's happening. We need to know how to apply all these trends to real-world business challenges.

Exactly. So what does the Coalesce report say about that? What are, like, the key strategic takeaways?

Well, one thing that comes through loud and clear is the importance of agility and adaptability. The data landscape is changing faster than ever before, and businesses need to be able to move quickly to keep up.

It's like trying to navigate a speedboat through, like, a really winding river. You need to be able to react quickly to, like, the changing currents and obstacles if you want to stay on course.

That's a great analogy. And one of the key enablers of agility is this shift toward those cloud-based data platforms. They provide that flexibility, the scalability and speed that businesses need to respond to those changing market conditions and capitalize on new opportunities.

It's like having a data infrastructure that can grow and evolve right alongside your business. No more being held back by those rigid on-premises systems that just can't keep up with the pace of innovation.

Exactly. And this cloud-based agility, it's also crucial for embracing that whole data products trend we discussed earlier. Creating valuable data products requires this rapid iteration cycle and the ability to experiment and adapt quickly based on feedback.

So it's like applying that Lean Startup methodology to data, right? You build a minimum viable product, get it out there quick, gather feedback, and iterate.

Precisely. And this data product mindset, it goes hand in hand with the shift toward more customer-centric data strategies. It's not just about collecting and analyzing data for its own sake anymore. Businesses are starting to understand that data can be used to create better customer experiences, personalize interactions, and build stronger relationships.

So it's about putting the customer at the center of your data strategy, using data to really understand their needs, anticipate their wants, and just deliver a more personalized and engaging experience.

That's what it's all about. And this customer-centric approach is also essential for navigating that increasingly complex world of data privacy and regulation.

Yeah, that's a huge challenge for businesses these days, right? With regulations like GDPR and CCPA, it's hard to know how to collect, store, and use data responsibly. One wrong move and You could end up with a hefty fine or like a PR nightmare.

It's definitely a challenge, but it's also an opportunity. Businesses that prioritize data privacy and transparency, they can build trust with their customers, which is a huge differentiator in today's competitive landscape.

It's like that old saying, honesty is the best policy. In the data-driven world, transparency is crucial for building that trust and loyalty with your customers.

And this focus on data privacy, it's actually driving innovation in areas like data anonymization, differential privacy, and federated learning. These techniques allow businesses to extract valuable insights from data without compromising individual privacy.

So it's not a question of choosing between data privacy and data utility. You can have both. That's right.

And I think we're going to see even more creative solutions emerging in this space as we move towards 2025 and beyond.

So we've got agility, cloud-based platforms, data products, customer centricity, and data privacy. What other strategic pieces of the puzzle should businesses be thinking about?

I think perhaps the most important consideration is the need for a holistic data strategy. It's not enough to just focus on one or two of these trends in isolation. Businesses need a comprehensive strategy that encompasses all aspects of data, from how it's collected and stored to how it's analyzed and used to drive action.

It's like having like a master plan For your data, right? A roadmap that guides your decisions and ensures that you're getting the most value out of your data assets.

Exactly. And this holistic data strategy needs to be tightly aligned with the overall business strategy. Data shouldn't be treated as like a separate silo. It needs to be woven into every aspect of the business. Yeah, from product development to marketing to customer service.

So data as a strategic asset, not just a technical afterthought.

Exactly. And this integration of data into every facet of the business also requires a change in mindset. We need to move beyond this idea that data is just for reporting and analytics. It can be such a powerful tool for driving action, automating processes, and even creating entirely new products and services. Data as a driver of innovation and growth.

That's a powerful concept.

It is. And I think we'll see more and more businesses embracing this data-driven approach as we move towards 2025.

Okay, so we've got a holistic data strategy that's tightly integrated with the business. And a focus on using data to drive action and innovation. What's like the final piece that brings it all together?

I would say the final piece, and perhaps the most important, is talent. Businesses need to invest in building strong data teams with the skills and expertise to navigate this complex landscape. It's like any other strategic investment.

You need the right people to execute your vision, especially given how in-demand data professionals are these days. It's a competitive market out there.

It absolutely is. Yeah. And as the demand for data talent continues to grow, businesses need to be creative and thoughtful about how they attract and retain top talent. It's not just about offering competitive salaries and benefits anymore.

So what else can companies do to stand out?

It's about creating a culture that values data, fosters innovation, and provides those real opportunities for growth and development. It's about empowering those data professionals to do their best work and making them feel like they're a valued part of the team.

And it's also about recognizing that data skills aren't just for technical roles anymore, as we discussed earlier. Data literacy is essential for everyone in the organization, from the CEO to like the frontline employees. Absolutely.

Data literacy is a core competency for the 21st century workforce.

I think that's a fantastic point to end on. The future of data is incredibly exciting, and it's up to all of us to shape it responsibly and strategically. Embracing these trends, building strong data teams, and fostering that data-driven culture, businesses can unlock that incredible potential of data and thrive in this new era of possibilities.

I couldn't agree more. It's a really exciting time to be working with data, and I'm eager to see what the next few years bring.

Thank you so much for joining us on this deep dive into the top data trends for 2025. We hope you found it insightful and thought-provoking and maybe even a little bit inspiring. Until next time.

Data Magicians was another Agile Data podcast. If you'd like to learn more on applying an agile way of working to your data and analytics, head over to agiledata.io.