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 analysis to implementation—balancing the need for automation of routine tasks while reskilling employees for higher-value work. The intersection of empathy and analytics becomes crucial as teams navigate the emotional journey of organizational change while making tough decisions based on hard numbers. Bill Canady is CEO at Arrowhead Engineered Products and a global business executive with over 30 years of experience across a range of industries. Bill is known for aligning with stakeholders to establish clear, growth-oriented strategies, as well as leading global public, private, and private equity-owned companies by building strong leadership teams and fostering deep relationships. As the former CEO of OTC Industrial Technologies, he oversaw $1 billion in annual sales. Under his leadership, OTC achieved over 43% revenue growth and a 78% increase in earnings. Throughout his career, Bill has guided organizations through complex challenges in regulatory, investor, and media landscapes. Drawing on his extensive experience, he developed the Profitable Growth Operating System (PGOS) to help business leaders worldwide drive sustainable, profitable growth. In the episode, Richie and Bill explore the journey from panic to profit in failing companies, the 100-day turnaround process, leveraging data for decision-making, the Pareto principle in business, automation's role in efficiency, and the importance of empathy and continuous learning in leadership, and much more. Links Mentioned in the Show: Bill’s new book: From Panic to ProfitThe 80/20 CEO by Bill CanadyConnect with BillBill’s websiteSkill Track: AI LeadershipRelated Episode: Leadership in the AI Era with Dana Maor, Senior Partner at McKinsey & CompanySign up to attend RADAR: Skills Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

In the first episode of our new season on developer experience, the cofounder and CTO of SDF Labs, now a part of dbt Labs, discusses databases, compilers, and dev tools. Wolfram spent close to two decades in Microsoft Research and several years at Meta building their data platform. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

Richard Barkham, Senior Economic Advisor at CBRE, joins the podcast to discuss the outlook for commercial real estate and the economy. Richard is decidedly more sanguine than the podcast hosts. Mark, Cris and Marisa also discuss the economic team’s recent win for Most Accurate U.S. Forecast for 2023-2024 by Consensus Economics. They debate how much of the win can be chalked up to skill, luck, or the Chief Economist. Guest: Richard Barkham – Senior Economic Advisor, CBRE Hosts: Mark Zandi – Chief Economist, Moody’s Analytics, Cris deRitis – Deputy Chief Economist, Moody’s Analytics, Marisa DiNatale – Senior Director - Head of Global Forecasting, Moody’s Analytics Follow Mark Zandi on 'X', BlueSky or LinkedIn @MarkZandi, Cris deRitis on LinkedIn, and Marisa DiNatale on LinkedIn

Questions or Comments, please email us at [email protected]. We would love to hear from you.    To stay informed and follow the insights of Moody's Analytics economists, visit Economic View.

Are you ready to level up your analytics game and tackle the challenges that come with data-heavy projects? In this episode, Harpreet Sahota, a data science leader with years of experience helping analysts and teams thrive, shares actionable insights and strategies for staying ahead in the fast-evolving world of data. Harpreet will help you develop a practical mindset to tackle real-world challenges and build the confidence to lead impactful projects. From cleaning messy datasets, to deciding between building or buying a solution, to training a computer vision model, Harpreet is here to share his expertise. Whether you're an aspiring data analyst or a seasoned professional, this episode will equip you with the skills and clarity to succeed. What You'll Learn: Data Cleaning for Any Data Type: Proven techniques to clean and prepare your data for analysis. Training a Computer Vision Model: What to consider before you start and how to ensure success. Build vs. Buy for LLMs: When to create your own solution and when to leverage existing tools. Setting Yourself Up for Success as an Analyst: Strategies to stand out and make your work impactful.   Register for free to be part of the next live session: https://bit.ly/3XB3A8b   Interested in learning more from Harpreet? Connect with him on LinkedIn   Follow us on Socials: LinkedIn YouTube Instagram (Mavens of Data) Instagram (Maven Analytics) TikTok Facebook Medium X/Twitter

Amazon Redshift Cookbook provides practical techniques for utilizing AWS's managed data warehousing service effectively. With this book, you'll learn to create scalable and secure data analytics solutions, tackle data integration challenges, and leverage Redshift's advanced features like data sharing and generative AI capabilities. What this Book will help me do Create end-to-end data analytics solutions from ingestion to reporting using Amazon Redshift. Optimize the performance and security of Redshift implementations to meet enterprise standards. Leverage Amazon Redshift for zero-ETL ingestion and advanced concurrency scaling. Integrate Redshift with data lakes for enhanced data processing versatility. Implement generative AI and machine learning solutions directly within Redshift environments. Author(s) Shruti Worlikar, Harshida Patel, and Anusha Challa are seasoned data experts who bring together years of experience with Amazon Web Services and data analytics. Their combined expertise enables them to offer actionable insights, hands-on recipes, and proven strategies for implementing and optimizing Amazon Redshift-based solutions. Who is it for? This book is best suited for data analysts, data engineers, and architects who are keen on mastering modern data warehouse solutions using Redshift. Readers should have some knowledge of data warehousing and familiarity with cloud concepts. Ideal for professionals looking to migrate on-premises systems or build cloud-native analytics pipelines leveraging Redshift.

This book takes an advanced dive into using Tableau for professional data visualization and analytics. You will learn techniques for crafting highly interactive dashboards, optimizing their performance, and leveraging Tableau's APIs and server features. With a focus on real-world applications, this resource serves as a guide for professionals aiming to master advanced Tableau skills. What this Book will help me do Build robust, high-performing Tableau data models for enterprise analytics. Use advanced geospatial techniques to create dynamic, data-rich mapping visualizations. Leverage APIs and developer tools to integrate Tableau with other platforms. Optimize Tableau dashboards for performance and interactivity. Apply best practices for content management and data security in Tableau implementations. Author(s) Pablo Sáenz de Tejada and Daria Kirilenko are seasoned Tableau experts with vast professional experience in implementing advanced analytics solutions. Pablo specializes in enterprise-level dashboard design and has trained numerous professionals globally. Daria focuses on integrating Tableau into complex data ecosystems, bringing a practical and innovative approach to analytics. Who is it for? This book is tailored for professionals such as Tableau developers, data analysts, and BI consultants who already have a foundational knowledge of Tableau. It is ideal for those seeking to deepen their skills and gain expertise in tackling advanced data visualization challenges. Whether you work in corporate analytics or enjoy exploring data in your own projects, this book will enhance your Tableau proficiency.

10-min startup demo in AI Launchpad.

Megan Bowers took an unconventional path to break into the data world. Starting from a self-guided Data Science Bootcamp, she shared her journey through blogging and gained millions of views, and then BOOM! Job offers and monetization opportunities flooded. This is her story. 📌 Interested in blogging for my publication? Get on this interest list: https://tally.so/r/3l4xQW 💌 Join 10k+ aspiring data analysts & get my tips in your inbox weekly 👉 https://www.datacareerjumpstart.com/newsletter 🆘 Feeling stuck in your data journey? Come to my next free "How to Land Your First Data Job" training 👉 https://www.datacareerjumpstart.com/training 👩‍💻 Want to land a data job in less than 90 days? 👉 https://www.datacareerjumpstart.com/daa 👔 Ace The Interview with Confidence 👉 https://www.datacareerjumpstart.com/interviewsimulator ⌚ TIMESTAMPS 00:00 - Introduction 03:28 - Gaining traction and recognition through blogging. 07:08 - Career Growth and transition to Alteryx. 14:18 -  Leveraging and advertising your domain expertise. 19:25 - What is a Data Journalist? 22:21 - Writing content. 24:29 - What is Alteryx? 🔗 CONNECT WITH MEGAN 🎥 YouTube Podcast Channel: https://www.youtube.com/playlist?list=PLfSLx4WE4q501UZjL3Hx-DiS4zyeePEN2 🤝 LinkedIn: https://www.linkedin.com/in/megandibble1/ 📸 Instagram: https://www.instagram.com/alteryx/ 💻 Alteryx Website: https://www.alteryx.com/ 🔗 CONNECT WITH AVERY 🎥 YouTube Channel: https://www.youtube.com/@averysmith 🤝 LinkedIn: https://www.linkedin.com/in/averyjsmith/ 📸 Instagram: https://instagram.com/datacareerjumpstart 🎵 TikTok: https://www.tiktok.com/@verydata 💻 Website: https://www.datacareerjumpstart.com/ Mentioned in this episode: Join the last cohort of 2025! The LAST cohort of The Data Analytics Accelerator for 2025 kicks off on Monday, December 8th and enrollment is officially open!

To celebrate the end of the year, we’re running a special End-of-Year Sale, where you’ll get: ✅ A discount on your enrollment 🎁 6 bonus gifts, including job listings, interview prep, AI tools + more

If your goal is to land a data job in 2026, this is your chance to get ahead of the competition and start strong.

👉 Join the December Cohort & Claim Your Bonuses: https://DataCareerJumpstart.com/daa https://www.datacareerjumpstart.com/daa

This episode marks our four-year anniversary doing the Inside Economic podcast, and we devote the conversation to responding to listener questions.  We’ve been getting lots of great Qs, ranging from the global trade war and DOGE cuts to immigration and productivity growth.  Keep the questions coming.   Hosts: Mark Zandi – Chief Economist, Moody’s Analytics, Cris deRitis – Deputy Chief Economist, Moody’s Analytics, Marisa DiNatale – Senior Director - Head of Global Forecasting, Moody’s Analytics Follow Mark Zandi on 'X', BlueSky or LinkedIn @MarkZandi, Cris deRitis on LinkedIn, and Marisa DiNatale on LinkedIn

Questions or Comments, please email us at [email protected]. We would love to hear from you.    To stay informed and follow the insights of Moody's Analytics economists, visit Economic View.

Artificial Intelligence is transforming the world of analytics, but how much of the job is changing? In this episode, we explore the AI-powered future of analytics: real-time insights, smarter Business Intelligence (BI) tools, and how Excel is turning heads with ChatGPT integration. Will AI replace analysts or supercharge what they already do best? Ravit Jain, Founder of the Ravit Show, will dive into what's staying the same, what's evolving, and what you need to know to keep up. Discover how AI is revolutionizing analytics, from real-time insights to the future of BI tools and Excel's new ChatGPT capabilities. Stay ahead of the curve— uncover how you can thrive in the AI-powered future of analytics! What You'll Learn: How AI-powered Excel is simplifying data wrangling and reporting. What parts of an analyst's role are evolving, and what core skills will always matter. How you can future-proof your analytics career in an AI-driven world.   Register for free to be part of the next live session: https://bit.ly/3XB3A8b   Interested in learning more from Ravit? Check out The Ravit Show!    Follow us on Socials: LinkedIn YouTube Instagram (Mavens of Data) Instagram (Maven Analytics) TikTok Facebook Medium X/Twitter

In this episode of Hub & Spoken, Jason Foster, CEO of Cynozure, chats with Luis Mejia, VP Data, Platforms & AI at PensionBee, about how the company is transforming the pension industry through smart use of data and AI. Luis shares how a digital-first mindset is helping PensionBee enhance customer experience, manage data effectively, and fuel business growth. He dives into how AI is being used in customer service, blending tech with human touch to build trust, and why ethics and transparency matter more than ever. From marketing to customer support, this episode explores the real-world challenges and opportunities of using data and AI. Luis also looks ahead to a future where AI helps democratise data and puts power in the hands of individuals. A must-listen for data and business leaders driving change in a digital world. Research Luis mentioned in the episode: https://www.pensionbee.com/uk/press/ai-and-pensions https://www.pensionbee.com/uk/press/age-vs-ai   Follow Luis on LinkedIn Follow Jason on LinkedIn *****    Cynozure is a leading data, analytics and AI company that helps organisations to reach their data potential. It works with clients on data and AI strategy, data management, data architecture and engineering, analytics and AI, data culture and literacy, and data leadership. The company was named one of The Sunday Times' fastest-growing private companies in both 2022 and 2023 and recognised as The Best Place to Work in Data by DataIQ in 2023 and 2024. Cynozure is a certified B Corporation. 

Today, I’m talking with Natalia Andreyeva from Infor about AI / ML product management and its application to supply chain software. Natalia is a Senior Director of Product Management for the Nexus AI / ML Solution Portfolio, and she walks us through what is new, and what is not, about designing AI capabilities in B2B software. We also got into why user experience is so critical in data-driven products, and the role of design in ensuring AI produces value. During our chat, Natalia hit on the importance of really nailing down customer needs through solid discovery and the role of product leaders in this non-technical work.

We also tackled some of the trickier aspects of designing for GenAI, digital assistants, the need to keep efforts strongly grounded in value creation for customers, and how even the best ML-based predictive analytics need to consider UX and the amount of evidence that customers need to believe the recommendations. During this episode, Natalia emphasizes a huge key to her work’s success: keeping customers and users in the loop throughout the product development lifecycle.

Highlights/ Skip to

What Natalia does as a Senior Director of Product Management for Infor Nexus (1:13) Who are the people using Infor Nexus Products and what do they accomplish when using them (2:51) Breaking down who makes up Natalia's team (4:05) What role does AI play in Natalia's work? (5:32) How do designers work with Natalia's team? (7:17) The problem that had Natalia rethink the discovery process when working with AI and machine learning applications (10:28) Why Natalia isn’t worried about competitors catching up to her team's design work (14:24) How Natalia works with Infor Nexus customers to help them understand the solutions her team is building (23:07) The biggest challenges Natalia faces with building GenAI and machine learning products (27:25) Natalia’s four steps to success in building AI products and capabilities (34:53) Where you can find more from Natalia (36:49)

Quotes from Today’s Episode

“I always launch discovery with customers, in the presence of the UX specialist [our designer]. We do the interviews together, and [regardless of who is facilitating] the goal is to understand the pain points of our customers by listening to how they do their jobs today. We do a series of these interviews and we distill them into the customer needs; the problems we need to really address for the customers. And then we start thinking about how to [address these needs]. Data products are a particular challenge because it’s not always that you can easily create a UX that would allow users to realize the value they’re searching for from the solution. And even if we can deliver it, consuming that is typically a challenge, too. So, this is where [design becomes really important]. [...] What I found through the years of experience is that it’s very difficult to explain to people around you what it is that you’re building when you’re dealing with a data-driven product. Is it a dashboard? Is it a workboard? They understand the word data, but that’s not what we are creating. We are creating the actual experience for the outcome that data will deliver to them indirectly, right? So, that’s typically how we work.” - Natalia Andreyeva (7:47) “[When doing discovery for products without AI], we already have ideas for what we want to get out. We know that there is a space in the market for those solutions to come to life. We just have to understand where. For AI-driven products, it’s not only about [the user’s] understanding of the problem or the design, it is also about understanding if the data exists and if it’s feasible to build the solution to address [the user’s] problem. [Data] feasibility is an extremely important piece because it will drive the UX as well.” - Natalia Andreyeva (10:50) “When [the team] discussed the problem, it sounded like a simple calculation that needed to be created [for users]. In reality, it was an entire process of thinking of multiple people in the chain [of command] to understand whether or not a medical product was safe to be consumed. That’s the outcome we needed to produce, and when we finally did, we actually celebrated with our customers and with our designers. It was one of the most difficult things that we had to design. So why did this problem actually get solved, and why we were the ones who solved it? It’s because we took the time to understand the current user experience through [our customer] interviews. We connected the dots and translated it all into a visual solution. We would never be able to do that without the proper UX and design in that place for the data.” - Natalia Andreyeva (13:16) “Everybody is pressured to come up with a strategy [for AI] or explain how AI is being incorporated into their solutions and platform, but it is still essential for all of my peers in product management to focus on the value [we’re] creating for customers. You cannot bypass discovery. Discovery is the essential portion where you have to spend time with your customers, champions, advisors, and their leads, but especially users who are doing this [supply chain] job every single day—so we understand where the pain point really is for them, we solve that pain, and we solve it with our design team as a partner, so that solution can surface value. ” - Natalia Andreyeva (22:08) “GenAI is a new field and new technology. It’s evolving quickly, and nobody really knows how to properly adapt or drive the adoption of AI solutions. The speed of innovation [in the AI field] is a challenge for everybody. People who work on the frontlines (i.e. product, engineering teams), have to stay way ahead of the market. Meanwhile, customers who are going to be using these [AI] solutions are not going to trust the [initial] outcomes. It’s going to take some time for people to become comfortable with them. But it doesn’t mean that your solution is bad or didn’t find the market fit. It’s just not time for your [solution] yet. Educating our users on the value of the solution is also part of that challenge, and [designers] have to be very careful that solutions are accessible. Users do not adopt intimidating solutions.” - Natalia Andreyeva (27:41) “First, discovery—where we search for the problems. From my experience, [discovery] works better if you’re very structured. I always provide [a customer] with an outline of what needs to happen so it’s not a secret. Then, do the prototyping phase and keep the customer engaged so they can see the quick outcomes of those prototypes. This is where you also have to really include the feasibility of the data if you’re building an AI solution, right? [Prototyping] can be short or long, but you need to keep the customer engaged throughout that phase so they see quick outcomes. Keep on validating this conceptually, you know, on the napkin, in Figma, it doesn’t really matter; you have to keep on keeping them engaged. Then, once you validate it works and the customer likes it, then build. Don’t really go into the deep development work until you know [all of this!] When you do build, create a beta solution. It only has to work so much to prove the value. Then, run the pilot, and if it’s successful, build the MVP, then launch. It’s simple, but it is a lot of work, and you have to keep your customers really engaged through all of those phases. If something doesn’t work [along the way], try to pivot early enough so you still have a viable product at the end.” - Natalia Andreyeva (34:53)

Links

Natalia's LinkedIn

YOU want to break into data analytics but not sure where to start? This interactive choose-your-own-adventure episode will help you! Get ready to make real-life decisions that will shape your data career. Play now and see where your choices take you. 💌 Join 10k+ aspiring data analysts & get my tips in your inbox weekly 👉 https://www.datacareerjumpstart.com/newsletter 🆘 Feeling stuck in your data journey? Come to my next free "How to Land Your First Data Job" training 👉 https://www.datacareerjumpstart.com/training 👩‍💻 Want to land a data job in less than 90 days? 👉 https://www.datacareerjumpstart.com/daa 👔 Ace The Interview with Confidence 👉 https://www.datacareerjumpstart.com/interviewsimulator ⌚ Control this audio using these timestamps: 1:54 - 1 - Data Scientist 3:48 - 2 - Data Analyst 5:42 - 3 - Python 7:36 - 4 - SQL 9:30 - 5 - Keep Learning 11:24 - 6 - Browse Some Jobs 13:18 - 7 - Move On 15:12 - 8 - Apply 17:06 - 9 - Try to Network 🔗 CONNECT WITH AVERY 🎥 YouTube Channel: https://www.youtube.com/@averysmith 🤝 LinkedIn: https://www.linkedin.com/in/averyjsmith/ 📸 Instagram: https://instagram.com/datacareerjumpstart 🎵 TikTok: https://www.tiktok.com/@verydata 💻 Website: https://www.datacareerjumpstart.com/ Mentioned in this episode: Join the last cohort of 2025! The LAST cohort of The Data Analytics Accelerator for 2025 kicks off on Monday, December 8th and enrollment is officially open!

To celebrate the end of the year, we’re running a special End-of-Year Sale, where you’ll get: ✅ A discount on your enrollment 🎁 6 bonus gifts, including job listings, interview prep, AI tools + more

If your goal is to land a data job in 2026, this is your chance to get ahead of the competition and start strong.

👉 Join the December Cohort & Claim Your Bonuses: https://DataCareerJumpstart.com/daa https://www.datacareerjumpstart.com/daa

Neste episódio, batemos um papo sobre os impactos da inteligência artificial nas carreiras em tecnologia: o aumento de 11,8% nos salários da área, como se destacar nas redes sociais, e o futuro dos empregos com automação e IA. Recebemos Lucas Carvalho (Tech & Innovation Editor no Linkedin), para trocar ideias sobre os principais movimentos do mercado, o papel das comunidades como o Data Hackers na aceleração de carreiras, e dicas práticas pra crescer com consistência nesse novo cenário. Lembrando que você pode encontrar todos os podcasts da comunidade Data Hackers no Spotify, iTunes, Google Podcast, Castbox e muitas outras plataformas. Falamos no episódio: Lucas Carvalho - Tech & Innovation Editor no Linkedin ossa Bancada Data Hackers: Monique Femme — Head of Community Management na Data Hackers Paulo Vasconcellos - Co-founder da Data Hackers e Principal Data Scientist na Hotmart. Gabriel Lages — Co-founder da Data Hackers e Data & Analytics Sr. Director na Hotmart.

Just wrapped up a whirlwind tour, giving a workshop in Atlanta and then attending Google Cloud Next. B2b nonstop action, and I'm glad to home for a bit.

While at Next, I had a conversation with another tech old timer friend. We talked about how much we're having using AI as a coding assistant. I'm having fun coming up with wild stuff and seeing if it's possible to build with code. AI's made coding fun again!

📈 This episode is brought to you by GoodData. Design and deploy custom data applications and integrate AI-assisted analytics capabilities wherever your users need them.

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