DataFramed

The modern data stack has transformed how organizations work with data, but are our BI tools keeping pace with these changes? As data schemas become increasingly fluid and analysis needs range from quick explorations to production-grade reporting, traditional approaches are being challenged. How can we create analytics experiences that accommodate both casual spreadsheet users and technical data modelers? With semantic layers becom...

Retrieval Augmented Generation (RAG) continues to be a foundational approach in AI despite claims of its demise. While some marketing narratives suggest RAG is being replaced by fine-tuning or long context windows, these technologies are actually complementary rather than competitive. But how do you build a truly effective RAG system that delivers accurate results in high-stakes environments? What separates a basic RAG implementati...

GPU acceleration is transforming how data scientists tackle computationally intensive problems in the AI and materials science fields. When dealing with billions of potential molecular combinations or massive datasets requiring dimensionality reduction, traditional CPU approaches often become prohibitively slow and expensive. How can data professionals determine when GPU acceleration will provide meaningful benefits to their workfl...

AI maturity isn't achieved through technology alone—it requires organizational alignment, cultural readiness, and strategic implementation. Companies across industries are working to move beyond experimental AI use toward systematic integration that delivers measurable business value. How do you assess where your organization stands on the AI maturity spectrum? What frameworks can help prioritize your efforts?

Eryn Peters, Co-founde...

AI tooling continues to expand with specialized solutions for every step of the development process. For data scientists and engineers, this creates a paradox: more options but potentially more complexity and integration challenges. How do you determine which tools actually improve productivity versus adding unnecessary overhead? Should you prioritize flexibility with individual best-of-breed components or streamline with integrate...

The like button has transformed how we interact online, becoming a cornerstone of digital engagement with over 7 billion clicks daily. What started as a simple user interface solution has evolved into a powerful data collection tool that companies use to understand customer preferences, predict trends, and build sophisticated recommendation systems. The data behind these interactions forms what experts call the 'like graph' - a val...

Welcome to DataFramed Industry Roundups! In this series of episodes, Adel & Richie sit down to discuss the latest and greatest in data & AI. In this episode, we touch upon the launch of OpenAI’s O3 and O4-mini models, Meta’s rocky release of Llama 4, Google’s new agent tooling ecosystem, the growing arms race in AI, the latest from the Stanford AI Index report, the plausibility of AGI and superintelligence, how agents might...

The roles within AI engineering are as diverse as the challenges they tackle. From integrating models into larger systems to ensuring data quality, the day-to-day work of AI professionals is anything but routine. How do you navigate the complexities of deploying AI applications? What are the key steps from prototype to production? For those looking to refine their processes, understanding the full lifecycle of AI development is ess...

Data-driven turnarounds are transforming how struggling businesses find their path back to profitability. When companies falter, the key to recovery can often lies in understanding which 20% of customers and products generate 80% of profits. But how do you quickly identify these critical assets when time is running out? What metrics should you prioritize when cash flow is tight? For data professionals, the challenge extends beyond ...

We live in an era where data is abundant, yet making sense of it is harder than ever. The best insights often go unnoticed—not because they lack value, but because they lack a compelling story. Simply presenting numbers isn’t enough; the way we shape and frame data determines whether it sparks action or fades into the background. Crafting a strong data story means knowing your audience, structuring your insights around a clear prob...

Misconceptions about AI's capabilities and the role of data are everywhere. Many believe AI is a singular, all-knowing entity, when in reality, it's a collection of algorithms producing intelligence-like outputs. Navigating and understanding the history and evolution of AI, from its origins to today's advanced language models is crucial. How do these developments, and misconceptions, impact your daily work? Are you leveraging the r...

The explosion of content in market research has created a paradox - more information but less time to consume it. Companies are now turning to AI chatbots to solve this problem, transforming how professionals interact with research data. Instead of expecting teams to read everything, these tools allow users to extract precisely what they need when they need it. This approach is proving not just more efficient but actually increases...

The role of data and AI engineers is more critical than ever. With organizations collecting massive amounts of data, the challenge lies in building efficient data infrastructures that can support AI systems and deliver actionable insights. But what does it take to become a successful data or AI engineer? How do you navigate the complex landscape of data tools and technologies? And what are the key skills and strategies needed to ex...

As data professionals, mastering the technical aspects of AI and data is only half the battle. The real challenge lies in effectively communicating insights to drive action and influence decisions. How do you ensure your data stories resonate with diverse audiences? It's not just about the numbers—it's about crafting a narrative that speaks to stakeholders. What strategies can you employ to make your insights not only heard but imp...

The integration of AI into everyday business operations raises questions about the future of work and human agency. With AI's potential to automate and optimize, how do we ensure that it complements rather than competes with human capabilities? What measures can be taken to prevent AI from overshadowing human input and creativity? How do we strike a balance between embracing AI's benefits and preserving the essence of human contrib...

In the retail industry, data science is not just about crunching numbers—it's about driving business impact through well-designed experiments. A-B testing in a physical store setting presents unique challenges that require careful planning and execution. How do you balance the need for statistical rigor with the practicalities of store operations? What role does data science play in ensuring that test results lead to actionable ins...

The integration of speech AI into everyday business operations is reshaping how we communicate and process information. With applications ranging from customer service to quality control, understanding the nuances of speech AI is crucial for professionals. How do you tackle the complexities of different languages and accents? What are the best practices for implementing speech AI in your organization? Explore the transformative pow...

The rise of AI tools has democratized access to technology, but with it comes the responsibility to use these tools ethically. How do organizations ensure their employees are not only aware of AI's capabilities but also its risks? What does it mean to have a responsible AI strategy that is both comprehensive and adaptable to future advancements? As companies strive to align their AI initiatives with ethical standards, what are the ...

The rise of A-B testing has transformed decision-making in tech, yet its application isn't without challenges. As professionals, how do you navigate the balance between short-term gains and long-term sustainability? What strategies can you employ to ensure your testing methods enhance rather than hinder user experience? And how do you effectively communicate the insights gained from testing to drive meaningful change within your or...