Avery talks with guest Shailvi Wakhlu, a seasoned data professional who navigated her way from individual contributor to head of analytics and senior director roles.

In this episode, Shailvi shares practical tips on self-advocacy, negotiating salaries, and effectively showcasing your skills.

🤝 Connect with Shailvi Wakhlu on LinkedIn

📙 Get Self-Advocacy Book

🤝 Ace your data analyst interview with the interview simulator

📩 Get my weekly email with helpful data career tips

📊 Come to my next free “How to Land Your First Data Job” training

🏫 Check out my 10-week data analytics bootcamp

Timestamps:

(03:34) Why Self-Advocacy is Essential

(10:17) Navigating Workplace Challenges

(28:51) Encouragement for Aspiring Data Professionals

Connect with Avery:

📺 Subscribe on YouTube

🎙Listen to My Podcast

👔 Connect with me on LinkedIn

📸 Instagram

🎵 TikTok

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

Speaker: Clark Wright (Staff Analytics Engineer at Airbnb)

This tech talk is a part of the Data Engineering Open Forum at Netflix 2024. Recently, Airbnb published a post to their Tech Blog called Data Quality Score: The next chapter of data quality at Airbnb. In this talk, Clark Wright shares the narrative of how data practitioners at Airbnb recognized the need for higher-quality data and then proposed, conceptualized, and launched Airbnb’s first Data Quality Score.

If you are interested in attending a future Data Engineering Open Forum, we highly recommend you join our Google Group (https://groups.google.com/g/data-engineering-open-forum) to stay tuned to event announcements.

Organizations have access to an ever-growing range of ways for their users to get value out of data, including most recently, generative AI. This session explores how AWS empowers users working with their data through modern access and audit patterns across a diverse array of data use cases. Enterprises need the ability to establish access controls so users access only the data they require, with each access traceable to an individual user. Learn how you can use AWS to personalize user-centric experiences and Zero Trust controls across various workloads, including analytics, machine learning, and generative AI.

Learn more about AWS re:Inforce at https://go.aws/reinforce.

Subscribe: More AWS videos: http://bit.ly/2O3zS75 More AWS events videos: http://bit.ly/316g9t4

ABOUT AWS Amazon Web Services (AWS) hosts events, both online and in-person, bringing the cloud computing community together to connect, collaborate, and learn from AWS experts.

AWS is the world's most comprehensive and broadly adopted cloud platform, offering over 200 fully featured services from data centers globally. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—are using AWS to lower costs, become more agile, and innovate faster.

reInforce2024 #CloudSecurity #AWS #AmazonWebServices #CloudComputing

Wait, I’m talking to a head of data management at a tech company? Why!? Well, today I'm joined by Malcolm Hawker to get his perspective around data products and what he’s seeing out in the wild as Head of Data Management at Profisee. Why Malcolm? Malcolm was a former head of product in prior roles, and for several years, I’ve enjoyed Malcolm’s musings on LinkedIn about the value of a product-oriented approach to ML and analytics. We had a chance to meet at CDOIQ in 2023 as well and he went on my “need to do an episode” list! 

According to Malcom, empathy is the secret to addressing key UX questions that ensure adoption and business value. He also emphasizes the need for data experts to develop business skills so that they're seen as equals by their customers. During our chat, Malcolm stresses the benefits of a product- and customer-centric approach to data products and what data professionals can learn approaching problem solving with a product orientation. 

Highlights/ Skip to:

Malcolm’s definition of a data product (2:10) Understanding your customers’ needs is the first step toward quantifying the benefits of your data product (6:34) How product makers can gain access to users to build more successful products (11:36)  Answering the UX question to get past the adoption stage and provide business value (16:03) Data experts must develop business expertise if they want to be seen as equals by potential customers (20:07) What people really mean by “data culture" (23:02) Malcolm’s data product journey and his changing perspective (32:05) Using empathy to provide a better UX in design and data (39:24) Avoiding the death of data science by becoming more product-driven (46:23) Where the majority of data professionals currently land on their view of product management for data products (48:15)

Quotes from Today’s Episode “My definition of a data product is something that is built by a data and analytics team that solves a specific customer problem that the customer would otherwise be willing to pay for. That’s it.” - Malcolm Hawker (3:42) “You need to observe how your customer uses data to make better decisions, optimize a business process, or to mitigate business risk. You need to know how your customers operate at a very, very intimate level, arguably, as well as they know how their business processes operate.” - Malcolm Hawker (7:36)

“So, be a problem solver. Be collaborative. Be somebody who is eager to help make your customers’ lives easier. You hear "no" when people think that you’re a burden. You start to hear more “yeses” when people think that you are actually invested in helping make their lives easier.” - Malcolm Hawker (12:42)

“We [data professionals] put data on a pedestal. We develop this mindset that the data matters more—as much or maybe even more than the business processes, and that is not true. We would not exist if it were not for the business. Hard stop.” - Malcolm Hawker (17:07)

“I hate to say it, I think a lot of this data stuff should kind of feel invisible in that way, too. It’s like this invisible ally that you’re not thinking about the dashboard; you just access the information as part of your natural workflow when you need insights on making a decision, or a status check that you’re on track with whatever your goal was. You’re not really going out of mode.” - Brian O’Neill (24:59)

“But you know, data people are basically librarians. We want to put things into classifications that are logical and work forwards and backwards, right? And in the product world, sometimes they just don’t, where you can have something be a product and be a material to a subsequent product.” - Malcolm Hawker (37:57)

“So, the broader point here is just more of a mindset shift. And you know, maybe these things aren’t necessarily a bad thing, but how do we become a little more product- and customer-driven so that we avoid situations where everybody thinks what we’re doing is a time waster?” - Malcolm Hawker (48:00)

Links Profisee: https://profisee.com/  LinkedIn: https://www.linkedin.com/in/malhawker/  CDO Matters: https://profisee.com/cdo-matters-live-with-malcolm-hawker/

Rui Falhas Santos - is the Manager of Data Analytics at one of the largest banks in the world. He joined us to discuss the AI applications they have implemented. The creativity and the use case are very captivating, as it is applied in one of the most regulated and legacy-bound industries. Rui is a hands-on manager with an extensive background in data. His data team helped build a solution that reads, on average, 300k articles a month to catch any alarming signals about their clients. Additionally, we discussed responsible AI from the decision-making side of model output and how data is collected for model processing.

Summary

Streaming data processing enables new categories of data products and analytics. Unfortunately, reasoning about stream processing engines is complex and lacks sufficient tooling. To address this shortcoming Datorios created an observability platform for Flink that brings visibility to the internals of this popular stream processing system. In this episode Ronen Korman and Stav Elkayam discuss how the increased understanding provided by purpose built observability improves the usefulness of Flink.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management This episode is supported by Code Comments, an original podcast from Red Hat. As someone who listens to the Data Engineering Podcast, you know that the road from tool selection to production readiness is anything but smooth or straight. In Code Comments, host Jamie Parker, Red Hatter and experienced engineer, shares the journey of technologists from across the industry and their hard-won lessons in implementing new technologies. I listened to the recent episode "Transforming Your Database" and appreciated the valuable advice on how to approach the selection and integration of new databases in applications and the impact on team dynamics. There are 3 seasons of great episodes and new ones landing everywhere you listen to podcasts. Search for "Code Commentst" in your podcast player or go to dataengineeringpodcast.com/codecomments today to subscribe. My thanks to the team at Code Comments for their support. Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Ronen Korman and Stav Elkayam about pulling back the curtain on your real-time data streams by bringing intuitive observability to Flink streams

Interview

Introduction How did you get involved in the area of data management? Can you describe what Datorios is and the story behind it? Data observability has been gaining adoption for a number of years now, with a large focus on data warehouses. What are some of the unique challenges posed by Flink?

How much of the complexity is due to the nature of streaming data vs. the architectural realities of Flink?

How has the lack of visibility into the flow of data in Flink impacted the ways that teams think about where/when/how to apply it? How have the requirements of generative AI shifted the demand for streaming data systems?

What role does Flink play in the architecture of generative AI systems?

Can you describe how Datorios is implemented?

How has the design and goals of Datorios changed since you first started working on it?

How much of the Datorios architecture and functionality is specific to Flink and how are you thinking about its potential application to other streaming platforms? Can you describe how Datorios is used in a day-to-day workflow for someone building streaming applications on Flink? What are the most interesting, innovative, or unexpected ways that you have seen Datorios used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Datorios? When is Datorios the wrong choice? What do you have planned for the future of Datorios?

Contact Info

Ronen

LinkedIn

Stav

LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don't forget to

Yohei Nakajima is an investor by day and coder by night. In particular, one of his projects, an AI agent framework called BabyAGI that creates a plan-execute loop, got a ton of attention in the past year. The truth is that AI agents are an extremely experimental space, and depending on how strict you want to be with your definition, there aren't a lot of production use cases today.  Yohei discusses the current state of AI agents and where they might take us.  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.

In a rare Saturday morning taping of the podcast, Dante and Matt join Mark (where’s Cris and Marisa?) to disentangle the considerable crosscurrents in the May jobs report.  Surging immigration is complicating interpretation of the numbers. Next week’s all-important report on consumer price inflation was also the fodder of discussion, as was Mark’s Washington Post op-ed arguing the Fed should cut rates.   Guest Hosts: Matt Colyar - Assistant Director, Moody's Analytics, Dante DeAntonio - Senior Director, Moody's Analytics Hosts: Mark Zandi – Chief Economist, Moody’s Analytics, Cris deRitis – Deputy Chief Economist, Moody’s Analytics, and Marisa DiNatale – Senior Director - Head of Global Forecasting, Moody’s Analytics Follow Mark Zandi on 'X' @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.

Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Dive into the fascinating world of graph theory and its applications with 'Modern Graph Theory Algorithms with Python.' Through Python programming and real-world case studies, this book equips you with the tools to transform data into graph structures, apply algorithms, and uncover insights, enabling effective solutions in diverse domains such as finance, epidemiology, and social networks. What this Book will help me do Understand how to wrangle a variety of data types into network formats suitable for analysis. Learn to use graph theory algorithms and toolkits such as NetworkX and igraph in Python. Apply network theory to predict and analyze trends, from epidemics to stock market dynamics. Explore the intersection of machine learning and graph theory through advanced neural network techniques. Gain expertise in database solutions with graph database querying and applications. Author(s) Colleen M. Farrelly, an experienced data scientist, and Franck Kalala Mutombo, a seasoned software engineer, bring years of expertise in network science and Python programming to every page of this book. Their professional experience includes working on cutting-edge problems in data analytics, graph theory, and scalable solutions for real-world issues. Combining their practical know-how, they deliver a resource aimed at both learning and applying techniques effectively. Who is it for? This book is tailored for data scientists, researchers, and analysts with an interest in using graph-based approaches for solving complex data problems. Ideal for those with a basic Python knowledge and familiarity with libraries like pandas and NumPy, the content bridges the gap between theory and application. It also provides insights into broad fields where network science can be impactful, contributing value to both students and professionals.

Join Jason Foster in this thought-provoking discussion with Tamara Kneese, the lead of the Algorithmic Methods Lab at Data & Society Research Institute, to delve into the intricate world of algorithmic systems and their far-reaching impacts. In this episode, they tackle the pressing questions surrounding the assessment of algorithms—environmental impact, societal fairness, and the need for responsible AI. Join the conversation as they discuss the challenges and opportunities these systems present and the vital steps organisations must take to navigate their influence.

Cynozure is a leading data, analytics and AI company that helps organisations to reach their data potential. They work with clients on data and AI strategy, data management, data architecture and engineering, analytics and AI, data culture and literacy, and change management and leadership. The company was named one of The Sunday Times' fastest-growing private companies in 2022 and 2023 and named the Best Place to Work in Data by DataIQ in 2023. For more information, visit www.cynozure.com.Check out our free AI Scorecard and we'll send you a personalised report.

Cognitive Science, Computational Intelligence, and Data Analytics: Methods and Applications with Python introduces readers to the foundational concepts of data analysis, cognitive science, and computational intelligence, including AI and Machine Learning. The book's focus is on fundamental ideas, procedures, and computational intelligence tools that can be applied to a wide range of data analysis approaches, with applications that include mathematical programming, evolutionary simulation, machine learning, and logic-based models. It offers readers the fundamental and practical aspects of cognitive science and data analysis, exploring data analytics in terms of description, evolution, and applicability in real-life problems. The authors cover the history and evolution of cognitive analytics, methodological concerns in philosophy, syntax and semantics, understanding of generative linguistics, theory of memory and processing theory, structured and unstructured data, qualitative and quantitative data, measurement of variables, nominal, ordinals, intervals, and ratio scale data. The content in this book is tailored to the reader's needs in terms of both type and fundamentals, including coverage of multivariate analysis, CRISP methodology and SEMMA methodology. Each chapter provides practical, hands-on learning with real-world applications, including case studies and Python programs related to the key concepts being presented. Demystifies the theory of data analytics using a step-by-step approach Covers the intersection of cognitive science, computational intelligence, and data analytics by providing examples and case studies with applied algorithms, mathematics, and Python programming code Introduces foundational data analytics techniques such as CRISP-DM, SEMMA, and Object Detection Models in the context of computational intelligence methods and tools Covers key concepts of multivariate and cognitive data analytics such as factor analytics, principal component analytics, linear regression analysis, logistic regression analysis, and value chain applications

Get insights into career transitions, the importance of networking, and the tools used in data positions in this episode!

Avery talks with data experts Steven Stark and Adam Dijans as they explore the fascinating field of operations research.

🤝 Connect with Adam De Jans

🤝 Connect with Steven Stark

🧙‍♂️ Ace the Interview with Confidence

⁠

⁠📩 Get my weekly email with helpful data career tips⁠

⁠📊 Come to my next free “How to Land Your First Data Job” training⁠

⁠🏫 Check out my 10-week data analytics bootcamp

Timestamps:

(03:17) The Value of Data Titles (22:53) Breaking into Operations Research (34:43) Advice for Aspiring Data Professionals

Connect with Avery:

📺 Subscribe on YouTube

🎙Listen to My Podcast

👔 Connect with me on LinkedIn

📸 Instagram

🎵 TikTok

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

The AWS AI and Data Conference is a one-day event showcasing the power of Generative AI, Predictive Machine Learning, and Advanced Data Analytics for organizations. It highlights real-world applications and innovations in various industries, offering a chance to learn from AWS experts and customers who have used these technologies to scale operations and meet evolving customer demands. Attendees will explore the latest trends, best practices, and AWS tools like Amazon Bedrock, enabling efficient AI application development. Open to both experts and newcomers, the conference provides essential insights and networking opportunities to boost innovation within your organization.

Learn more: https://go.aws/3x2mha0 Learn more about AWS events: https://go.aws/3kss9CP

Subscribe: More AWS videos: http://bit.ly/2O3zS75 More AWS events videos: http://bit.ly/316g9t4

ABOUT AWS Amazon Web Services (AWS) hosts events, both online and in-person, bringing the cloud computing community together to connect, collaborate, and learn from AWS experts. AWS is the world’s most comprehensive and broadly adopted cloud platform, offering over 200 fully featured services from data centers globally. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—are using AWS to lower costs, become more agile, and innovate faster.

AWSEvents #GenerativeAI #AI #Cloud #AWSAIandDataConference

Uncover the power of Zero-ETL integrations for your data in AWS. Learn firsthand how integrating Amazon Aurora with Amazon Redshift and Amazon DynamoDB with Amazon OpenSearch enables near real-time analytics and machine learning. Tailored for developers, architects, and administrators, discover key insights, best practices, and techniques for seamless integration of fully managed services, reducing latency and unlocking the full potential of your data in AWS.

Learn more: https://go.aws/3x2mha0 Learn more about AWS events: https://go.aws/3kss9CP

Subscribe: More AWS videos: http://bit.ly/2O3zS75 More AWS events videos: http://bit.ly/316g9t4

ABOUT AWS Amazon Web Services (AWS) hosts events, both online and in-person, bringing the cloud computing community together to connect, collaborate, and learn from AWS experts. AWS is the world’s most comprehensive and broadly adopted cloud platform, offering over 200 fully featured services from data centers globally. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—are using AWS to lower costs, become more agile, and innovate faster.

AWSEvents #GenerativeAI #AI #Cloud #AWSAIandDataConference

Learn how Sonrai Analytics leverage AI/ML on AWS to reduce cancer drug trial times. By analysing different data types in the Machine Learning Pipeline, Sonrai Analytics are able to improve diagnosis and treatments for patients worldwide. In this session we will cover some common AI/ML pipeline approaches when building, training and deploying end-to-end AI/ML models on Amazon SageMaker.

Learn more: https://go.aws/3x2mha0 Learn more about AWS events: https://go.aws/3kss9CP

Subscribe: More AWS videos: http://bit.ly/2O3zS75 More AWS events videos: http://bit.ly/316g9t4

ABOUT AWS Amazon Web Services (AWS) hosts events, both online and in-person, bringing the cloud computing community together to connect, collaborate, and learn from AWS experts. AWS is the world’s most comprehensive and broadly adopted cloud platform, offering over 200 fully featured services from data centers globally. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—are using AWS to lower costs, become more agile, and innovate faster.

AWSEvents #GenerativeAI #AI #Cloud #AWSAIandDataConference

Confidently use predictive analytic and statistical techniques to identify key relationships and trends in HR-related data to aid strategic organizational decision-making.

Mark, Marisa, and Cris are joined by their colleague Matt Colyar as they delve into the resilience of the U.S. economy. Matt kicks off the conversation with a rundown of the latest Personal Consumption Expenditures (PCE) inflation data and its implications for monetary policy. Following a brief, engaging Stats Game, the team explores the reasons behind the U.S. economy's rapid and robust recovery compared to the rest of the world. The discussion concludes with answers to audience questions, focusing on the implications of quantitative easing/tightening and the predictive power of the yield curve.   Guests: Matt Colyar – Assistant Director, Economist - Moody's Analytics Hosts: Mark Zandi – Chief Economist, Moody’s Analytics, Cris deRitis – Deputy Chief Economist, Moody’s Analytics, and Marisa DiNatale – Senior Director - Head of Global Forecasting, Moody’s Analytics Follow Mark Zandi on 'X' @MarkZandi, Cris deRitis on LinkedIn, and Marisa DiNatale on LinkedIn for additional insight. For more on Jonthan Smoke Click here

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.

Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

On this podcast episode of Data Unchained, Shiraz Ritwik, Head of Growth at LatentView Analytics, joins us to discuss how he and his company are help get clean data for marketing campaigns, using data analytics to launch AI initiatives, and merging data sets to get insights on all platforms. www.latentview.com

data #podcast #ai #artificialintelligence #datascience #datasets #podcast #podcasts #podcasting #lifescience #datastorage

Cyberpunk by jiglr | https://soundcloud.com/jiglrmusic Music promoted by https://www.free-stock-music.com Creative Commons Attribution 3.0 Unported License https://creativecommons.org/licenses/by/3.0/deed.en_US Hosted on Acast. See acast.com/privacy for more information.

Augmented Analytics isn't just another book on data and analytics; it's a holistic resource for reimagining the way your entire organization interacts with information to become insight-driven. Moving beyond traditional, limited ways of making sense of data, Augmented Analytics provides a dynamic, actionable strategy for improving your organization's analytical capabilities. With this book, you can infuse your workflows with intelligent automation and modern artificial intelligence, empowering more team members to make better decisions. You'll find more in these pages than just how to add another forecast to your dashboard; you'll discover a complete approach to achieving analytical excellence in your organization. You'll explore: Key elements and building blocks of augmented analytics, including its benefits, potential challenges, and relevance in today's business landscape Best practices for preparing and implementing augmented analytics in your organization, including analytics roles, workflows, mindsets, tool sets, and skill sets Best practices for data enablement, liberalization, trust, and accessibility How to apply a use-case approach to drive business value and use augmented analytics as an enabler, with selected case studies This book provide a clear, actionable path to accelerate your journey to analytical excellence.

Avery Smith interviews Alex Sanchez, a high school math teacher who transitioned to a data analyst role at 7-Eleven in just 50 days. Alex's journey is inspiring and practical. He discusses key strategies, transferable skills, and the importance of networking in a way that is immediately applicable. Whether you're aspiring to break into the data field or considering a career shift, Alex's story offers valuable insights and actionable advice.

🤝 Connect with Alex Sanchez on Linkedin

🧙‍♂️ Ace the Interview with Confidence

⁠

⁠📩 Get my weekly email with helpful data career tips⁠

⁠📊 Come to my next free “How to Land Your First Data Job” training⁠

⁠🏫 Check out my 10-week data analytics bootcamp

Timestamps:

(04:31) Initial Challenges and Adaptability (17:06) Landing the Job: Key Strategies (24:31) The Hidden Job Market (29:04) The Importance of Networking (32:27) Transitioning from Teaching to Data Analytics

Connect with Avery:

📺 Subscribe on YouTube

🎙Listen to My Podcast

👔 Connect with me on LinkedIn

📸 Instagram

🎵 TikTok 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