The way company’s report on their environmental compliance is being watched and scrutinised more than ever before. It’s no longer enough to publicly declare the efforts of a company without sufficient evidence; stakeholders and investors working in the natural resources sector want to see data-backed reporting of a company’s environmental performance and initiatives. 

The two waves of innovation driving how companies use their environmental data are: 
•	changes to regulatory standards around how companies report their data 
•	advances in technology that shape how we capture, report and analyse environmental data. 

This week’s podcast guests, Stuart van de Water, Environmental Leader at acQuire, and Che DeDear, EnviroSys Commercial Manager, sit down with us to explore the emerging regulatory changes and technological trends shaping how companies report on their environmental data.

Emerging trends shaping environmental reporting

Most data leaders want to deliver data products, but few are doing it. 

Let's face it: most data teams today function as internal service bureaus that fulfill customer requests that arrive via ticketing systems, email, handwritten notes, or calls from colleagues looking for a favor. Most work double time to keep their request backlogs from ballooning from weeks to months. In this environment, few data leaders have time or capacity to switch from a project management approach to a product management one.

Even if data leaders had time, most wouldn't know how to make this transition. Most have no experience in product management, nor do they have a good idea of a data product. So asking data leaders to deliver data products is like asking them to build a rocket ship that can travel to the moon.

In this episode, Wayne Eckerson interviews Henrik Strandberg, a strong proponent of running data teams using product management principles. Henrik Strandberg is a seasoned data transformation leader who, for the past 25 years, has helped numerous organizations bridge gaps between business and technology. In stints at publishing and gaming companies, Henrik has developed a unique understanding of building and delivering data products at scale that delight customers.

Achieving Success with Data Products: An Interview with Henrik Strandberg

In today’s fast-paced digital world, the need for efficient and secure cloud storage solutions has never been greater. Introducing Cloud Supply, a cutting-edge product designed to revolutionize your digital storage experience. Say goodbye to the limitations of traditional storage methods and embrace the power and flexibility of the cloud.

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One of the key benefits of Cloud Supply is its unparalleled security features. With end-to-end encryption and advanced access controls, you can rest assured that your data is safe from unauthorized access or breaches. Plus, with regular security updates and proactive monitoring, Cloud Supply stays ahead of emerging threats to keep your data secure at all times.

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We are Excited to Share insights from a data management Industry Expert Mohammad AI-Sogour Executive VP of Shared Services.

National Water Company on navigating data quality and Governance in the AI Era. 

Stay ahead with powerfull strategies for reliable insights for impactfull decision making.

We are Excited to Share insights from a data management

With numerous data catalog options available, all claiming to be the best, how do you make an informed decision without exhaustive research?
Published at:
https://www.eckerson.com/articles/unveiling-the-future-of-data-catalogs

Unveiling the Future of Data Catalogs - Audio Blog

In this episode of the Supply Chain Next Podcast, host Richard Donaldson and guest Sean Maisonneuve delve into Sean's journey as an entrepreneur and the evolution of his company, Mecka Inc. They explore Sean's background and his entry into the auto parts industry, highlighting the challenges of managing vast amounts of product data and the innovative solutions that Mecka provides to streamline and automate supply chain processes. Sean discusses the company's commitment to reducing busy work through digital automation, creating a unified platform that allows businesses to manage everything from inventory to sales across multiple channels with ease. The conversation also touches on the future of supply chain management, emphasizing the importance of digitization, real-time data accuracy, and the potential for expanding Mecka's capabilities into other industries, such as aerospace. Richard and Sean reflect on the broader implications of supply chain digitization and the importance of building platforms that integrate and simplify complex business operations on a global scale.

Supply Chain Next Podcast

067 - Sean Maisonneuve - Revolutionizing Supply Chain Through Automation

067 - Sean Maisonneuve - The Supply Chain Automation Revolution

JetTrac Connect is a powerful software solution that bridges the gap between disconnected systems. It acts as a central hub for processing and managing data, forms, and documents from various sources. JetTrac Connect achieves this through a combination of standard and customizable modules, enabling flexible data integration and workflow automation. The system's core functionality lies in the JetTrac JobController, a powerful processing engine that orchestrates production jobs. By utilizing a modular approach, JetTrac Connect offers a robust solution for diverse data management challenges, allowing organizations to connect and leverage information across disparate systems and streamlining data-driven processes.

JetTrac Connect Solution Podcast:ProTechnology Podcast Series

This week, Shen speaks with James Crosby, co-founder and CEO of data management provider Fencore. They discuss the evolutionary nature of the buy side’s approach to technology platforms and data and how it has come full circle. 

9:30 – James joins, and we discuss the buy-side operating model
14:30 – How it has evolved
16:30 – Are we repeating old mistakes, and why that’s a problem
21:00 – How should firms put an EDM in place
23:30 – A future-proof model
26:00 – What is a good organizational operating model

Episode 292: Fencore’s James Crosby

Exploring data observability’s limits in data migration, integrity audits, and the need for specialized tools for reliability.
Published at:
https://www.eckerson.com/articles/the-shiny-allure-of-data-observability-its-limits-in-data-migration-integrity-audits-and-certification

The Shiny Allure of Data Observability - Audio Blog

Join Matt Bradford and Don Poorman as they help me unpack an incredible year of technological advancement at Pure Storage, taking listeners on a thorough journey through our most groundbreaking announcements and product developments. From hardware innovations like the FlashArray//C20 to cutting-edge cloud solutions and AI reference architectures, this episode offers an insider's perspective on how Pure continues to push the boundaries of data storage and management.
Traveling through key solution areas including hardware, cloud, cybersecurity, AI, and digital experience, Matt and Don provide expert commentary on game-changing developments. Highlights include the expansion of Pure Storage Cloud for AVS, exciting enhancements to DRaaS, the transformative OVX reference architecture for AI initiatives, and the introduction of Pure1 assessments that are reshaping how enterprises approach technology evaluation and implementation.
Beyond the technical details, this episode celebrates Pure Storage's commitment to customer-driven innovation and continuous improvement. From the launch of Pure Skill's new subscription-based learning platform to the expansion of Evergreen subscriptions and the intriguing glimpse of an AI Copilot, Matt and Don paint a vivid picture of a technology company relentlessly focused on solving real-world challenges and empowering digital transformation. Whether you're a Pure Storage enthusiast, a technology professional, or simply curious about the latest in data infrastructure, this year-in-review episode is a must-listen.

ICYMI: A Year in Review of Pure Always-On Innovation

Welcome to "AI with Shaily"! 🌟 I'm Shailendra Kumar, your enthusiastic guide through the fascinating realm of artificial intelligence. Here, we explore the latest trends and insights in AI, sprinkled with my personal experiences and expert advice. 

Today, we're focusing on the essentials of MLOps best practices, specifically tailored for beginners eager to find their footing in this intricate field. Imagine stepping into a bustling engine room, where each component plays a vital role in the overall operation—this is your MLOps environment! 🛠️

We begin our journey by establishing clear paths with defined business objectives and SMART goals. Think of this as plotting a detailed roadmap for a cross-country adventure—specific destinations, measurable milestones, and time-bound checkpoints are crucial to aligning with your organizational ambitions. 🗺️

Next, we dive into the core of our engine room: data management and naming conventions. Visualize a perfectly organized library, where every book is easily accessible. This meticulous organization is essential in MLOps, especially when we consider the principle that "Changing Anything Changes Everything" (CACE). 📚

As we progress, selecting the right models and tools becomes paramount. This step is akin to assembling the ideal toolkit for our journey. Just as different terrains require various vehicles, each model is uniquely suited to our data and CI/CD pipeline. Tools like Kubernetes serve as the robust vehicles that manage our deployment journey. 🚗

Continuous monitoring acts as our reliable compass throughout the MLOps journey. Regular evaluations help us stay on course, ensuring our well-oiled machine remains efficient and effective. Methods like the ML Test Score become our guiding stars, illuminating the path ahead. 🧭

Collaboration is the heartbeat of our engine room, keeping everything in sync. Platforms like Cookiecutter data science build strong connections between teams, ensuring that data scientists, ML engineers, and DevOps experts work together seamlessly. 🤝

Finally, envision a vast reservoir capable of holding every bit of data we gather. Scalable data infrastructure is crucial for managing extensive datasets efficiently, with tools like MinIO ready to handle large volumes of data with speed and accuracy. 🌊

As we wrap up, remember this key takeaway: when mastering MLOps, always build your foundation with scalable and adaptable practices that can grow with your evolving needs. 

Before we part ways, I’d like to leave you with a thought: "The bridge between ones and zeros must be lit with collaboration, adaptability, and foresight." 🔗 Join me on social media platforms like YouTube, Twitter, LinkedIn, and Medium to share your insights, subscribe for regular AI updates, and leave your comments. Your thoughts are the fuel for our discussions. Until next time, keep your curiosity ignited and your compass pointed true! 🔥✨

Mastering MLOps: A Beginner's Guide to AI Success

Despite $180 billion spent on big data tools and technologies, poor data quality remains a significant barrier for businesses, especially in achieving Generative AI goals.
Published at:
https://www.eckerson.com/articles/poor-data-quality-is-a-full-blown-crisis-a-2024-customer-insight-report

Poor Data Quality is a Full-Blown Crisis: A 2024 Customer Insight Report - Audio Blog

All data management activities—whether internally or externally focused—should either reduce costs or grow earnings.
Published at:
https://www.eckerson.com/articles/book-review-how-to-make-money-with-data

Book Review: How to Make Money with Data - Audio Blog

Welcome to *AI with Shaily,* hosted by Shailendra Kumar, your friendly guide to the fascinating world of artificial intelligence! 🤖✨ In this episode, Shaily dives into a powerful but often overlooked innovation in machine learning called feature stores—a centralized system that organizes and manages the "features," or key data elements, that fuel ML models. Imagine it as a well-stocked pantry 🍽️ where all your essential ingredients are neatly stored and easily accessible, making your cooking (or in this case, your ML projects) smooth and efficient.

Shaily explains that feature stores have become a game-changer in MLOps (machine learning operations) as of 2025. They serve as a single source of truth, eliminating duplicated efforts and inconsistencies when different teams build models. Having worked extensively in AI development, Shaily shares a personal story where introducing a feature store transformed collaboration—teams stopped reinventing similar features under different names, saving time and boosting trust in the data. This also ensured that features used during model training matched those used during real-time prediction, leading to faster and more reliable outcomes. ⚡️🔍

Beyond organization, feature stores bring automation and pipeline management to the table. They track every change with versioning and governance—think of it like a detailed history book 📚 for your data ingredients—and perform quality checks by spotting outliers and missing values before the data reaches the model. This rigor helps build more robust and dependable ML systems.

Scalability is another highlight. Feature stores are designed to handle massive data volumes and provide real-time access, making them essential for production environments. Industry leaders like Databricks and Hopsworks offer enterprise-grade platforms, while open-source tools like Feast make these capabilities accessible to a wider audience of engineers. 🌐🚀

Shaily poses a thoughtful question: How often do ML projects slow down because teams don’t share a common “feature language”? If that sounds familiar, feature stores could be the solution you need. As a bonus tip, Shaily advises choosing feature store solutions that integrate smoothly with your existing MLOps pipeline, ensuring seamless connection between feature engineering, model training, deployment, and monitoring—key to operational success. 🔗💡

To close, Shaily quotes AI pioneer Andrew Ng: “AI is the new electricity.” Just like electricity, AI’s true power depends on solid infrastructure—and feature stores are the wiring that energizes machine learning today. ⚡️🔌

Thanks for tuning in to *AI with Shaily!* Don’t forget to subscribe on YouTube, follow Shailendra Kumar on Twitter and LinkedIn, and explore his deep-dive articles on Medium. Share your thoughts on feature stores or any AI topic in the comments—Shaily loves engaging with the community! Until next time, keep innovating and remember: the future is feature-rich! 🌟📈

Feature Stores Uncovered: The Secret to Faster, Smarter Machine Learning

🎧 Tackling Data – Episode 2
Title: Smarter, Faster, Fitter: The Data Revolution in Elite Performance

What happens when you combine elite sport, cutting-edge genetics, and Silicon Valley thinking?

In this episode of Tackling Data, host Rory Teague sits down with Ralph Pethica, CEO of Genetrainer and former Googler, to explore how data is reshaping the world of high performance — from elite athletes to corporate execs.

They unpack:
🔹 How Genetrainer uses DNA, recovery, and training data to personalise athlete development
🔹 Why “gut feel” is giving way to real-time, data-led decision making
🔹 The rise of the “Business Athlete” and what leaders can learn from sport
🔹 How athlete-owned data is building accountability and motivation
🔹 What a truly data-optimised performance culture looks like

Whether you're in sport, health, or business — this episode is packed with insight on turning data into a competitive edge.

🎙️ Host: Rory Teague
👤 Guest: Ralph Pethica, CEO of Genetrainer
⏱️ Duration: ~25 minutes

#DataDriven #Performance #Genetics #AthleteData #HighPerformance #TacklingDataPodcast

Series: Tackling Data | Episode 2: Smarter, Faster, Fitter: The Data Revolution in Elite Performance

Welcome to *AI with Shaily*! 🎙️ Hosted by Shailendra Kumar, this show brings you sharp, insightful updates on artificial intelligence and machine learning for curious minds eager to stay ahead. Today’s episode dives deep into the crucial yet often overlooked topic of **data versioning**—the backbone of successful machine learning projects in 2025 and beyond. 📊🤖

Shailendra draws a vivid analogy comparing data versioning to baking a cake 🍰: just like tweaking ingredients or oven temperature needs careful note-taking to replicate the perfect cake, managing datasets with precision is essential for reliable machine learning outcomes. Even tiny changes in data can dramatically impact model performance, making data versioning a superhero cape 🦸‍♂️ for ML teams to track, audit, and improve experiments consistently.

The episode breaks down five best practices for data versioning:

1. **Define what and how granular to version**: Focus on tracking significant changes that affect results, avoiding overload from minor tweaks. This keeps resources optimized while maintaining clarity. 📋✅

2. **Centralize data repositories with clear hierarchies**: Organize raw and processed data in separate, logical spaces—like having distinct shelves for flour and sugar—so data never gets mixed up. 🗂️📁

3. **Leverage modern data version control tools**: Traditional Git struggles with large datasets, but new tools like lakeFS enable seamless tracking of data versions alongside code and models, boosting reproducibility and audit compliance. 🛠️💾

4. **Automate versioning within ML pipelines**: Syncing data snapshots automatically with model experiments reduces manual work and minimizes errors or mismatches. 🤖🔄

5. **Promote collaboration and auditability**: Transparent data change histories improve teamwork across global teams, simplify troubleshooting, and ensure governance policies are met. 🌍🤝

Shailendra shares a personal story about early career challenges when untracked dataset changes caused model performance to vanish mysteriously—an experience that fueled his passion for robust data versioning. Thanks to tools like lakeFS, many ML teams now enjoy harmonious integration of data and model histories. 🎯📈

As a **Bonus Tip**, he recommends integrating data version control with cloud infrastructure ☁️ for scalable, secure, and remote team access—eliminating confusion over which dataset version is the latest.

He leaves listeners with a thought-provoking question: How do you currently track and manage your datasets? Could your approach use a versioning makeover? 🤔

And a memorable closing quote: *“In machine learning, reproducibility is king; data versioning is its crown.”* 👑

If you enjoyed the episode, Shailendra invites you to connect on YouTube, Twitter, LinkedIn, and Medium by searching *Shailendra Kumar*. Subscribe to *AI with Shaily* for more cutting-edge AI news, tips, and discussions. Don’t hesitate to share your thoughts or questions—let’s learn and build better AI together! 🌟💬

Signing off with warmth and encouragement to keep exploring the intelligent path ahead, this is Shailendra Kumar from *AI with Shaily*—where AI meets real life. 🚀🤓

Data Versioning Demystified: The Secret Sauce for Reliable Machine Learning in 2025

📊 Bulk Upload or Real-Time API? Which one fits your business better?

Whether you’re dealing with large data dumps once a month or need up-to-the-minute updates inside your app, choosing the right method can save time and money.

This guide breaks down the pros, cons, and real-world use cases—so you can pick what works for your workflow (or combine both!).

👉 Read now: https://www.searchbug.com/info/bulk-data-processing-vs-api/

#DataProcessing #APIs #BusinessData #Searchbug #BulkUpload #RealTimeData #CRMIntegration #DataManagement #DeveloperTools

Bulk Data Processing vs. API Which Is Best for Your Business

🚀 Welcome to the Weekly AI News Roundup with Shaily! I’m Shailendra Kumar (Shaily), your trusted guide for the latest machine learning developments and deep learning breakthroughs, covering the week starting August 4, 2025.

This week, AI flexes its muscle across industries. Microsoft redefines enterprise data with the release of SQL Server 2025—AI-native databases, vector search, and semantic indexing are transforming how organizations manage information. GitLab launches the Duo Agent Platform, letting specialized AI agents collaborate for smarter, faster, and safer software development.

OpenAI and SoftBank join forces, creating compact AI data centers for energy-efficient, decentralized infrastructure—an innovation that matters for both startups and governments. Plus, the UK’s expanded partnership with OpenAI promises secure, ethical public service transformation, while UK police roll out AI-enabled cameras to enhance road safety.

Why does this matter? Whether you’re a researcher, developer, or business leader, these Artificial Intelligence advancements are shaping data strategies, software workflows, and public policy worldwide. My take: Microsoft’s AI-native SQL revolution sets the stage for data-driven enterprises, while multi-agent teamwork and sustainable AI compute hint at the future.

🔔 Subscribe for your weekly dose of AI insights! Like, share, and connect with me (Shaily) on YouTube, Twitter, LinkedIn, and Medium—let’s keep making sense (and money) out of data!

Is SQL Server 2025 the Future of AI? News with Shaily for Week Starting Aug 4

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Unlock the secrets of data mastery with Shailendra, a 27-year veteran in AI, ML, and Data Science, driving AU$ 6 Billion in transformative value. As the author of 'Making Money out of Data,' their strategies redefine digital success. With expertise honed at IBM and Accenture, they drive innovation and data monetization. Explore how loyalty programs like Flybuys and Everyday Rewards revolutionize business—connect with Shaily today.

ProTechology offers cutting-edge mobile data capture solutions and field service automation tools designed to optimize business operations. With a focus on seamless integration, their innovative software enhances productivity by streamlining data collection, workflow management, and system connectivity, enabling businesses to operate more efficiently and effectively in the digital age.

Cloud Perspective helps businesses master customer data, integrate systems, and enhance governance. Serving industries like travel, retail, and finance, we provide cutting-edge data solutions to drive digital transformation.

Wei-Shen Wong and Anthony Malakian discuss the hottest financial technology topics in the capital markets every week.

acQuire Connected is your compass in the world of data. acQuire Connected features conversations with thought leaders and technology experts who bring together people, process and technology for tackling increasingly complex data challenges impacting Environmental, Social and Governance (ESG) as it relates to the earth's resources, the natural environment and communities.

Listen to data and analytics leaders share the secrets of their success. Wayne Eckerson, long-time global thought leader interviews guests who run data and analytics programs at Fortune 2000 organizations around the world. Tune in to stay abreast of the latest technologies, techniques, and trends in our fast-paced industry.

Bringing you direct access to Pure Storage thought leaders, technology experts, alliance partners, customer insights, and our latest product news.

Supply Chain Next is a podcast series focused on the digitization of the enterprise supply chain. This show, hosted by Richard Donaldson, investigates the next steps for supply chain, both for the supply chain organization within enterprises and the supply chain professional. 

We dive into the tectonic shifts happening today for supply chain organizations, as they begin to embrace technology to optimize, create efficiencies, drive lower costs, and improve margins. We aim to understand what these changes really mean, not only for the organizations and leaders with in them, but also what does takes to be relevant in supply chain over the next 10 years.

We are sitting upon one of the biggest economic tsunamis to come online since the internet itself. How will supply chain organizations use this opportunity to dramatically change the landscape? Supply Chain Next is here to better understand just that.

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New tracks tagged #DataManagement