Data literacy and AI literacy are becoming essential skills in today's digital landscape. As organizations collect more data and deploy AI solutions, the ability to understand, interpret, and make decisions with these tools is increasingly valuable. But how do we develop these skills effectively across an organization? What does successful implementation of data and AI literacy programs look like in practice? The journey to becoming data literate doesn't require becoming a data scientist—it's about building confidence and comfort with data in your specific role. From change management strategies to measuring real value, understanding how to foster these skills can transform both individual careers and organizational outcomes. Jordan Morrow is known as the "Godfather of Data Literacy," having helped pioneer and invent the entire field. He is also the founder and CEO of Bodhi Data and currently is the Senior Vice President of Data & AI Transformation for AgileOne, helping to utilize data and AI in the total talent management space.

Jordan is a global trailblazer in the world of data literacy and enjoys his time traveling the world speaking and/or helping companies. He served as the Chair of the Advisory Board for The Data Literacy Project, has spoken at numerous conferences around the world, and is an active voice in the data and analytics community. He has also helped companies and organizations around the world, including the United Nations, build and/or understand data literacy.

In the episode, Richie and Jordan explore the progress and challenges in data literacy, the integration of AI literacy, the importance of storytelling and decision-making in data training, how organizations can foster a data-driven culture, practical tips for using AI in meetings and personal productivity, and much more.

Links Mentioned in the Show: Pre-order Jordan’s upcoming book - Data and AI Skills: Gain the Confidence You Need to SucceedJordan’s BooksConnect with JordanDataCamp Webinar Featuring the Godparents of Data Literacy - Jordan Morrow and Valerie LoganRelated Episode: Scaling Responsible AI Literacy with Uthman Ali, Global Head of Responsible AI at BPRewatch RADAR AI 

New to DataCamp? Learn on the go using the DataCamp mobile app Empower your business with world-class data and AI skills with DataCamp for business

We’re excited to be back at Big Data LDN this year—huge thanks to the organisers for hosting Databricks London once more!

Join us for an evening of insights, networking, and community with the Databricks Team and Advancing Analytics!

🎤 Agenda:

6:00 PM – 6:10 PM | Kickoff & Warm Welcome

Grab a drink, say hi, and get the lowdown on what’s coming up. We’ll set the scene for an evening of learning and laughs.

6:10 PM – 6:50 PM | The Metadata Marathon: How three projects are racing forward – Holly Smith (Staff Developer Advocate, Databricks)

With the enormous amount of discussion about open storage formats between nerds and even not-nerds, it can be hard to keep track of who’s doing what and how this actually makes any impact on day to day data projects.

Holly will take a closer look at the three big projects in this space; Delta, Hudi and Iceberg. They’re all trying to solve for similar data problems and have tackled the various challenges in different ways. Her talk will start with the very basics of how we got here, what the history is before diving deep into the underlying tech, their roadmaps, and their impacts on the data landscape as a whole.

6:50 PM – 7:10 PM | What’s New in Databricks & Databricks AI – Simon Whiteley & Gavi Regunath

Hot off the press! Simon and Gavi will walk you through the latest and greatest from Databricks, including shiny new AI features and platform updates you’ll want to try ASAP.

7:10 PM onwards | Q&A Panel + Networking

Your chance to ask the experts anything—then stick around for drinks, snacks, and some good old-fashioned data geekery.

In this, the 10th year of Big Data LDN, in its flagship Great Dat Debate keynote panel, conference chair and leading industry analyst Mike Ferguson welcomes executives from leading software vendors to discuss key topics in data management and analytics. Panellists will debate the challenges and success factors in building an agentic enterprise, the importance of unified data and AI governance, the implications of key industry trends in data management, how best to deal with real-world customer challenges, how to build a modern data and analytics (D&A) architecture, and issues on-the-horizon that companies should be planning for today.

Attendees will learn best practices for data and analytics implementation in a modern data and AI -driven enterprise from seasoned executives and an experienced industry analyst in a packed, unscripted, candid discussion.

Data leaders today face a familiar challenge: complex pipelines, duplicated systems, and spiraling infrastructure costs. Standardizing around Kafka for real-time and Iceberg for large-scale analytics has gone some way towards addressing this but still requires separate stacks, leaving teams to stitch them together at high expense and risk.

This talk will explore how Kafka and Iceberg together form a new foundation for data infrastructure. One that unifies streaming and analytics into a single, cost-efficient layer. By standardizing on these open technologies, organizations can reduce data duplication, simplify governance, and unlock both instant insights and long-term value from the same platform.

You will come away with a clear understanding of why this convergence is reshaping the industry, how it lowers operational risk, and advantages it offers for building durable, future-proof data capabilities.

Brought to You By: •⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. Statsig built a complete set of data tools that allow engineering teams to measure the impact of their work. This toolkit is SO valuable to so many teams, that OpenAI - who was a huge user of Statsig - decided to acquire the company, the news announced last week. Talk about validation! Check out Statsig. •⁠ Linear – The system for modern product development. Here’s an interesting story: OpenAI switched to Linear as a way to establish a shared vocabulary between teams. Every project now follows the same lifecycle, uses the same labels, and moves through the same states. Try Linear for yourself. — What does it take to do well at a hyper-growth company? In this episode of The Pragmatic Engineer, I sit down with Charles-Axel Dein, one of the first engineers at Uber, who later hired me there. Since then, he’s gone on to work at CloudKitchens. He’s also been maintaining the popular Professional programming reading list GitHub repo for 15 years, where he collects articles that made him a better programmer.  In our conversation, we dig into what it’s really like to work inside companies that grow rapidly in scale and headcount. Charles shares what he’s learned about personal productivity, project management, incidents, interviewing, plus how to build flexible skills that hold up in fast-moving environments.  Jump to interesting parts: • 10:41 – the reality of working inside a hyperscale company • 41:10 – the traits of high-performing engineers • 1:03:31 – Charles’ advice for getting hired in today’s job market We also discuss: • How to spot the signs of hypergrowth (and when it’s slowing down) • What sets high-performing engineers apart beyond shipping • Charles’s personal productivity tips, favorite reads, and how he uses reading to uplevel his skills • Strategic tips for building your resume and interviewing  • How imposter syndrome is normal, and how leaning into it helps you grow • And much more! If you’re at a fast-growing company, considering joining one, or looking to land your next role, you won’t want to miss this practical advice on hiring, interviewing, productivity, leadership, and career growth. — Timestamps (00:00) Intro (04:04) Early days at Uber as engineer #20 (08:12) CloudKitchens’ similarities with Uber (10:41) The reality of working at a hyperscale company (19:05) Tenancies and how Uber deployed new features (22:14) How CloudKitchens handles incidents (26:57) Hiring during fast-growth (34:09) Avoiding burnout (38:55) The popular Professional programming reading list repo (41:10) The traits of high-performing engineers  (53:22) Project management tactics (1:03:31) How to get hired as a software engineer (1:12:26) How AI is changing hiring (1:19:26) Unexpected ways to thrive in fast-paced environments (1:20:45) Dealing with imposter syndrome  (1:22:48) Book recommendations  (1:27:26) The problem with survival bias  (1:32:44) AI’s impact on software development  (1:42:28) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: •⁠ Software engineers leading projects •⁠ The Platform and Program split at Uber •⁠ Inside Uber’s move to the Cloud •⁠ How Uber built its observability platform •⁠ From Software Engineer to AI Engineer – with Janvi Kalra — Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected].

Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

As organizations increasingly adopt data lake architectures, analytics databases face significant integration challenges beyond simple data ingestion. This talk explores the complex technical hurdles encountered when building robust connections between analytics engines and modern data lake formats.

We'll examine critical implementation challenges, including the absence of native library support for formats like Delta Lake, which necessitates expansion into new programming languages such as Rust to achieve optimal performance. The session explores the complexities of managing stateful systems, addressing caching inconsistencies, and reconciling state across distributed environments.

A key focus will be on integrating with external catalogs while maintaining data consistency and performance - a challenge that requires careful architectural decisions around metadata management and query optimization. We'll explore how these technical constraints impact system design and the trade-offs involved in different implementation approaches.

Attendees will gain a practical understanding of the engineering complexity behind seamless data lake integration and actionable approaches to common implementation obstacles.

In a world where AI agents, complex workflows, and accelerating data demands are reshaping every enterprise, the challenge isn’t just managing data, it’s creating trusted context that connects people, processes, and technology. 

Join Rebecca O’Kill, Chief Data & Analytics Officer at Axis Capital, for an Honest No-BS conversation about how her team is transforming governance from a compliance checkbox into a strategic enabler of business value. 

Together, we’ll unpack: 

• Minimal Valuable Governance (MVG): why the old ivory tower “govern everything” mindset fails, and how focusing on just enough governance creates immediate business impact. 

• The ACTIVE framework, a practical approach for governance built on: Alignment, Clarity, Trust, Iterative, Value, Enablement 

• How Axis Capital is embedding governance across the organization by uniting the “front office” (what and why) with the “back office” (how). 

• Why context and knowledge are critical for the next era of agentic AI and multi-agent workflows, and how Axis is preparing for it today. 

By the end, you’ll see how Axis Capital is turning governance into a competitive advantage and why this approach is essential for any organization looking to thrive in a world of AI-driven automation and connected workflows. 

Analytical Data Product success is traditionally measured with classic reliability metrics. If we were ambitious, we might track user engagement by dashboard views or self-serve activity; they are blunt, woolly indicators at best. The real goal was always to enable better decisions, but we often struggle to measure whether our data products actually help. Conversational BI changes this equation. Now we can see the exact questions users are asking, what follow-ups they need, and where the data model delights or frustrates them. This creates a richer feedback loop than ever before, but it also puts our data model front and centre, exposed directly to business users in a way that makes design quality impossible to hide.

This session will recap the foundations of good data product design, then dive into what conversational BI means for analytics teams. How do we design models that give the best foundation? How can we capture and interpret this new stream of usage feedback? What does success look like? We'll answer all of these questions and more.

Data’s not what it was. Analytics is shifting fast - so how should we rethink data product design before we’re left in the dust?

Practicing analytics well takes more than just tools and tech. It requires data modeling practices that unify and empower all teams within analytics, from engineers to analysts. This is especially true as AI becomes a part of analytics. Without a governed data model that provides consistent data interpretation, AI tools are left to guess. Join panelists Joe Reis, Sarah Levy, Harry Gollop, Rob Hulme, Shachar Meir, and Guy Fighel, as they share battle-tested advice on overcoming conflicting definitions and accurately mapping business intent to data, reports and dashboards at scale. This panel is for data & analytics engineers seeking a clear framework to capture business logic across layers, and for data leaders focused on building a reliable foundation for Gen AI.

Open Banking data offers transaction-level granularity at scale. This is ideal for building models that power credit underwriting, customer segmentation, and market trend analysis. This session covers practical techniques for transforming raw transaction data into structured features using entity resolution, feature engineering, and aggregation pipelines.

Learn how BP and Kingfisher scale data quality across cloud, analytics, and AI—driving reliable insights and business outcomes with Anomalo.

Maximize the value of your SAP investments by harnessing the power of Data and AI on Azure. In this session, you’ll learn proven strategies to leverage SAP landscapes, unify data across SAP and non-SAP systems, and unlock advanced analytics with Azure’s Agentic AI capabilities. We’ll showcase industry-ready accelerators that accelerate transformation, enable governed data access, and turn insights into action to fuel innovation, agility, and measurable business outcomes.