Podcasts about data transformation

Embark on an insightful exploration of the hospitality sector, powered by data-driven analysis. Cindi Howson and Hyatt's data trailblazer, Ray Boyle (Vice President, Data and Analytics), are charting a course through Hyatt's data innovation. Witness how Hyatt's four-pillar data strategy is revolutionizing everything from employee empowerment to guest personalization and operational efficiency. Discover how Hyatt is democratizing data with self-service tools and pioneering an AI-powered frontier to redefine the very essence of hospitality!Key Moments: Data as an Asset (08:26): Ray emphasizes the importance of shifting the organizational mindset to view data not as a cost center, but as a critical asset. He discusses how data should be cared for, invested in, and stored like any other valuable asset, with the expectation of generating value for the business.  Hyatt's Data Strategy Pillars (13:00): Ray outlines the four key pillars that form the foundation of Hyatt's data strategy. These pillars include cultivating people and building a data-driven culture, personalizing the guest and customer experience in a high-trust environment, operating with excellence by ensuring operational efficiency and information consistency, and growing with intent by integrating new businesses and data flows.  Key Milestones in Hyatt's Data Transformation (16:42): Ray details the significant milestones in Hyatt's data transformation journey. These include clarifying the data strategy, establishing the data and AI operating model, building data governance capabilities, modernizing the data platform and infrastructure, expanding data assets, and releasing new services like personalization and forecasting.  Data Democratization and Data Fluency (23:00): Ray explains Hyatt's strong emphasis on self-service analytics to empower users across the organization. He discusses the importance of data accessibility, trustworthiness, and usability, as well as the potential of generative AI to further democratize data access and insights.  This includes building a data community to facilitate knowledge sharing and learning, as well as providing tooling and guidance to business organizations to effectively roll out analytics within their domains.  AI's Impact and Collaboration (31:35): Ray explores the transformative impact of AI on businesses and its role in fostering tighter collaboration between business and technology teams. He discusses how AI is driving the need for reimagined workflows and how it's changing the way data is used and delivered across the organization.Key Quotes:“ThoughtSpot has been a key partner of ours on that journey. We just roll the data into the cloud, and we're working to publish our assets, sales, finance, loyalty, revenue, search, and marketing into that infrastructure so that there's just a growing base of information that everybody can use in the self-service context.” - Raymond Boyle"Velocity is something you build over time. It's how I think about the operating model around data, ensuring everyone plays their role and develops the necessary skills. To me, velocity increases as you establish the operating model and you have the business, technology, and data organizations, along with governance and security, all participating effectively. - Raymond Boyle"When you think about the business outcomes and how people are beginning to consider AI's potential in that transformation, I believe AI is becoming a more significant factor every quarter." - Raymond BoyleMentionsThe Four V's of Big Data, Including VelocityDalva, By Jim HarrisonMinnesota Timberwolves' SuccessGuest Bio Ray Boyle (current Vice President, Data and Analytics at Hyatt) has enjoyed a distinguished career spanning several industries and roles across consulting, software, analytics, and data leadership. His notable roles include leading strategic planning, research, and analytics for Walmart's Sam's Club division; serving as Vice President of Walmart Global Customer Insights and Analytics; Vice President of Walmart's Global Data and Analytics Platform; Vice President leading FICO's global retail and CPG practice; and Executive Vice President heading IRI's Global Shopper Analytics and Services team.Since 2019, Ray has served as Vice President, Data and Analytics at Hyatt. Aligned with Hyatt's purpose — to care for people so they can be their best — his ambition is to elevate and scale that care through data-driven decisions and automation that benefit guests, customers, owners, and colleagues.Guest Bio Ray Boyle (current Vice President, Data and Analytics at Hyatt) has enjoyed a distinguished career spanning several industries and roles across consulting, software, analytics, and data leadership. His notable roles include leading strategic planning, research, and analytics for Walmart's Sam's Club division; serving as Vice President of Walmart Global Customer Insights and Analytics; Vice President of Walmart's Global Data and Analytics Platform; Vice President leading FICO's global retail and CPG practice; and Executive Vice President heading IRI's Global Shopper Analytics and Services team.Since 2019, Ray has served as Vice President, Data and Analytics at Hyatt. Aligned with Hyatt's purpose — to care for people so they can be their best — his ambition is to elevate and scale that care through data-driven decisions and automation that benefit guests, customers, owners, and colleagues. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

At Gartner Data & Analytics Summit in Orlando, I spoke with Raj Bains, CEO of Prophecy on The Ravit Show, about why they're back at Gartner, the key data challenges they're solving, and how AI is playing a bigger role in modern data transformation. We also discussed their latest funding, real-world customer use cases, and what to expect from their panel discussion with Sanjeev Mohan on Wednesday: "The Impact of GenAI on Data Teams."#GartnerDA #GenAI #GartnerOrlando2025 #theravitshow

In Episode 15, of Season 5 of Driven by Data: The Podcast, Kyle Winterbottom is joined by Heather Wade, Data Transformation Director at Johnson Matthey, where they discuss research findings that attempt to understand what happens when risks outweigh benefits, which includes; Learnings from taking all the best bits from consumer data into a different industry The reason for undertaking a DBA Why there's so much research written about the benefits of data but very little about the risksUnderstanding the different types of risks your organisation facesDelivering a piece of research about how firms tackle data related risksWhy there's much more risk than the traditional cyber or privacy risks Focusing on qualitative research as opposed to quantitative How the research uncovered the differences in the CDO/Data Leader role Whether the CDO tenure forces us to focus on value and benefits as opposed to risk Discussing the risks of inaction as opposed to selling benefits How the people and culture element of teams pose a large threat The lack of difference between regulated versus unregulated industriesHow firms should approach the balance between benefits and risk Measuring potential risks Why the biggest risks could be the lack of clarity around what a data function exists to do Managing grey areas of risk (like sustainability) Thanks to our sponsor, Data Literacy Academy.Data Literacy Academy is leading the way in transforming enterprise workforces with data literacy across the organisation, through a combination of change management and education. In today's data-centric world, being data literate is no longer a luxury, it's a necessity.If you want successful data product adoption, and to keep driving innovation within your business, you need to start with data literacy first.At Data Literacy Academy, we don't just teach data skills. We empower individuals and teams to think critically, analyse effectively, and make decisions confidently based on data. We're bridging the gap between business and data teams, so they can all work towards aligned outcomes.From those taking their first steps in data literacy to seasoned experts looking to fine-tune their skills, our data experts provide tailored classes for every stage. But it's not just learning tracks that we offer. We embed a deep data culture shift through a transformative change management programme.We take a people-first approach, working closely with your executive team to win the hearts and minds. We know this will drive the company-wide impact that data teams want to achieve.Get in touch and find out how you can unlock the full potential of data in your organisation. Learn more at www.dl-academy.com.

Points of Interest01:00 – 01:45 – Guest Introduction: Marcel welcomes back his co-founder, Ben Zittlau, highlighting his expertise in data operations and agency growth strategies.03:35 – 05:50 – Scaling Data Operations: Lessons from Jobber: Ben shares insights from his experience at Jobber, detailing the challenges of building and scaling a data operations team in a fast-growing company.08:43 – 13:14 – Why Agencies Struggle to Get Insights from Their Data: Discussion on how agencies collect vast amounts of data across multiple tools but fail to derive meaningful insights due to fragmentation and inconsistency.13:15 – 16:05 – The Myth of a “Single Source of Truth: Marcel and Ben challenge the common belief that pushing all data into a single platform solves reporting issues, highlighting the reality of messy operational data.19:26 – 21:48 – The Limitations of All-in-One Software Solutions: Exploring why all-in-one agency management tools often fail to deliver on their promise of seamless reporting and data integration.24:43 – 27:44 – The Hidden Costs of Locking into a Single Platform: Discussion on how agencies become “trapped” by software providers, making it difficult to switch tools without major operational disruptions.30:29 – 35:53 – How to Integrate Data Without Sacrificing Flexibility: A deep dive into the challenges of stitching data from various tools while maintaining adaptability and historical accuracy.35:54 – 41:14 – Accuracy vs. Precision: Why Clean Data is a Myth: Why agencies should focus on broader trends instead of pursuing impossible data perfection, and how to handle data inconsistencies effectively.41:15 – 44:15 – A Modern Data Approach: Extract, Transform, Load (ETL): Introduction to the ETL process, which allows agencies to clean and transform data before reporting, improving reliability and flexibility.50:14 – 52:00 – Lessons from Finance: What Agencies Can Learn from Accounting: Marcel compares data operations to bookkeeping, explaining how structured financial workflows can serve as a model for better agency data management.Show NotesConnect with Ben via LinkedInFree ToolkitParakeeto Foundations CourseLove the PodcastLeave us a review here.

Once you have your data stored in OneLake, you'll be ready to start transforming it to improve it's usability, accuracy, and efficiency.    In this episode of the podcast, Belinda Allen takes us on a delightful journey through Data Flows, Power Query, Azure Data Factory (Pipelines), and discusses the merits of shortcuts. We also learn about a handy way to manually upload a table if you have some static data you need to update.  There are many tools and techniques that can be used for data ingestion and transformations. And while some of these options we discuss will be up to individual preference, there are pros and cons to each. One of the blessings and curses of Fabric is that there are many ways of achieving the same result, so what you choose may depend on the nature of the data you have and your goals, but might also be dictated by personal experience.  We hope you enjoyed this conversation with Belinda on ingesting and transforming data in Microsoft Fabric. If you have questions or comments, please send them our way. We would love to answer your questions on a future episode. Leave us a comment and some love ❤️on LinkedIn, X, Facebook, or Instagram. The show notes for today's episode can be found at Episode 284: The Four-Letter Word ETL - Data Movement. Have fun on the SQL Trail!

The Future of AI and Data in 2025:  There's a lot of excitement and prototyping around AI. "People are excited that they can ask questions and get real answers instead of getting the proverbial 10 blue links. I think people see the promise of things like that. How do you make that work at scale? How do you make that work in a reliable way, and how do you make it work in a way that's not expensive?" Ramaswamy shares a recent experience in India, describing the cost-consciousness of customers and the focus on making data accessible and valuable.Shifting Priorities for Snowflake Customers:  What do customers want? Ramaswamy identifies goals including empowering business users with data faster and unlocking the value of unstructured information. In response, Snowflake is helping customers access and utilize historical information and build hybrid systems. He notes that SQL enables easier data transformations and extractions.Cultural Change and AI Adoption: Cultural change is required for companies to handle AI. Even within Snowflake, adopting AI has been a challenging process. Snowflake's engineering and sales teams are beginning to use AI tools like Copilot and Cortex, but there's a need for ongoing practice to fully integrate AI into daily operations. The Promise of AI and Data Platforms: AI has the potential to free up significant time for employees, allowing them to focus on higher-value tasks. Snowflake aspires to make data platforms simple and accessible, allowing users to perform complex tasks with ease. Its consumption model has several benefits and aligns it with business goals of making more money or spending less. Companies like Siemens are using chatbots to improve efficiency and access to information.Competition and Innovation in the Data Market: Competition, Ramaswamy says, drives innovation and improves products. It only strengthens Snowflake, which stands apart due to its unified platform, tight integration, and ease of use. "If you create a chatbot with Snowflake, it will automatically obey all of the access control rules that you have set up. It will automatically obey all of the data masking that your administrator has set up." Ramaswamy discusses the challenges of balancing innovation with maintaining simplicity and efficiency in Snowflake's product offerings.Mega Trends and Challenges for Snowflake: Ramaswamy sees two mega trends for 2025: the rise of interoperable data formats and the acceleration of AI. Snowflake is well-positioned to take advantage of these trends through its end-to-end offerings and data engineering capabilities. However, there are challenges to maintaining data quality and speed while innovating rapidly. AI tools like agents must drive real utility and value for customers.Data Literacy Initiative: One Million Minds Plus One: Snowflake has a new initiative aimed at improving data literacy. Data can be difficult to interpret, and the right skills are necessary to do so. Snowflake is spearheading an ambitious program to educate over a million people in data literacy, free of charge. The initiative includes teaching concepts like data engineering, data analytics, and using BI tools and notebooks for interactive analysis.Excitement for 2025: Sridhar expresses excitement about the potential for AI and data to drive real business transformations in 2025. Snowflake will continue to play a vital role in helping customers become truly data-driven organizations. Navigating growth, competition, and innovation while maintaining simplicity and value for customers is key.

A major data skills initiative to help thousands of non-IT Irish professionals to become data literate and keep pace with the digital revolution has been launched, supported by Technology Ireland Digital Skillnet in collaboration with Microsoft. Data SMART is aimed at equipping professionals across Ireland who do not work in IT-based roles with the confidence and essential data skills - or data literacy - that they need to thrive in today's workplace. Created by the Technology Ireland Digital Skillnet in association with the Analytics Institute and Microsoft, thousands of non-tech workers can now ensure that their skills are current and relevant to a digital future. Studies show that those staff that are familiar with core data skills will progress faster in their careers. Furthermore, research confirms that companies making data-driven decisions make faster market progress and revenue. Data SMART will allow working professionals to acquire data literacy competence in a flexible, low-cost way during a 12-week learning window that combines online tutorials with hands-on, interactive workshops. Participants will acquire skills in Data Collection and Cleaning, Data Transformation and Interpretation, Data Visualisation and Storytelling, as well as Ethics, Privacy, Security and Data Governance. A Data SMART Foundation Certificate in Data Skills will be awarded by the Analytics Institute and Microsoft to recognise participants' new data skills. Commenting at the launch of Data SMART in Microsoft HQ in Leopardstown, Dublin yesterday, Maire Hunt, Network Director of Technology Ireland Digital Skillnet, said: "Data SMART is the most ambitious national upskilling opportunity for companies to adopt essential data skills that has been created to date. Our ambition is to train over 10,000 professionals from all backgrounds and across all sectors over the next three years." Lorcan Malone, Analytics Institute CEO, said: "Already multiple companies have signed up for the Data SMART Initiative with companies able to train large numbers of staff in a very flexible, low-cost manner, with minimum impact on the flow of work. This will boost careers national and internationally, as well as enabling leaders to adopt a data driven approach to decisions in an increasing challenging commercial landscape." Microsoft, a world leader in technology, sees this initiative as a chance to equip Irish workers with practical data knowledge and provide a stepping stone to better skills and brighter career prospects. Microsoft Ireland Head of Future Skilling, Dr Kevin Marshall, said: "Microsoft Ireland is committed to providing upskilling opportunities for all. As organisations engage with the AI revolution, data analysis skills are fundamental. This initiative will equip individuals with crucial skills for effective data navigation and informed decision-making. We are pleased to collaborate with the Technolofgy Ireland Digital Skillnet and the Analytics Institute on this important programme." How to Apply for the Data SMART Initiative: Data SMART is open to workers in the Republic of Ireland with part-funding available from Technology Ireland Digital Skillnet. Spaces are limited, so interested professionals are encouraged to apply soon. For more information and to sign up, visit www.datasmart.digitalskillnet.ie More about Irish Tech News Irish Tech News are Ireland's No. 1 Online Tech Publication and often Ireland's No.1 Tech Podcast too. You can find hundreds of fantastic previous episodes and subscribe using whatever platform you like via our Anchor.fm page here: https://anchor.fm/irish-tech-news If you'd like to be featured in an upcoming Podcast email us at Simon@IrishTechNews.ie now to discuss. Irish Tech News have a range of services available to help promote your business. Why not drop us a line at Info@IrishTechNews.ie now to find out more about how we can help you reach our audience. You can also find and follow us on Twitter, LinkedIn, Facebook, Inst...

Nine months into his role as Snowflake's CEO, Sridhar Ramaswamy sat down for a candid discussion on the journey so far & what's ahead.He uncovered details about his transition to Snowflake CEO, the operational changes he's put in place to accelerate product development, and his bold predictions for AI's future—along with how he's guiding a vision for Snowflake to fit into that landscape.Sridhar also reflects on how his leadership has evolved, from leading a major Google unit to founding Neeva (acquired by Salesforce) and now running Snowflake. [0:00] Intro[0:56] Sridhar's Journey to Snowflake[3:37] Snowflake's AI Vision and Cortex AI[10:35] Innovative Uses of AI at Snowflake[15:49] Challenges and Future of AI in Business[21:27] Snowflake's Long-Term AI Aspirations[25:04] Predictable and Unpredictable Aspects of AI[25:20] Voice Interfaces and Data Transformation[26:20] Autonomous Agents and AI Predictions[27:01] Market Consolidation in AI[29:54] Journey into AI and Early Experiences[31:21] The Evolution of AI Models[32:06] Building Neeva and AI Search Engines[36:16] Leadership and Product Development at Snowflake[40:45] Setting Goals and Accountability[43:25] Leadership Style and Cultural Foundations[47:25] Career Advice and Finding Your Path  Executive Producer: Rashad AssirProducer: Leah ClapperMixing and editing: Sam Dewees and JR Bohannon Check out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA

What's up everyone, today we have the pleasure of sitting down with Danny Lambert, Director of Marketing Operations at dbt Labs. Summary: Marketers often feel like they're battling a dragon when it comes to integrating data. We're overwhelmed by technical jargon, stuck with outdated methods, and facing roadblocks from data teams. Danny walks us through his journey of cautiously entering the data world and the role dbt can play for marketing teams. By learning just enough SQL, knowing what tools you need to get started with and leaning on dbt's tools, you can start small and gradually build a warehouse-first martech stack. The reward is more control over your data, flexibility to deploy personalized campaigns independently, and a competitive edge that no pre-packaged solution can match.About DanielDanny started his career at an event solutions company where he wore several different marketing hats including getting his first taste of marketing automation  He then worked in marketing ops at IZEA, at marketplace that connects brands with influencers before having a short stint at McGaw.io one of the leading martech and analytics agenciesHe then moved over to healtech at CareCloud where he led Demandgen and ABMHe then transitioned to Rev.com the popular transcription company where he started in marketing ops, then demand gen before being promoted to Director of Integrated MarketingAnd today Dan is Director of Marketing Operations at dbt Labs, the creators of the most popular software for data transformation used by data engineers at more than 20k companiesNavigating the Disconnect Between Marketers and Data TeamsMany marketers struggle to engage with data teams because they feel worlds apart. Danny points out that it's a lot like the early days of marketing's relationship with product teams. Before product-led growth (PLG) became a buzzword, marketers and product teams operated in separate silos. It took a concerted effort to break that wall, and the same shift is needed with data. Marketers often find the mechanics of data engineering and warehousing intimidating, and for good reason—they weren't trained for it. But it doesn't have to be that way.Danny recounts his time at CareCloud, where he was exposed to the concept of a data warehouse. The idea was gaining traction, and he attended a Snowflake event to grasp the essentials. After an hour of slides and schemas, he walked out just as confused as when he walked in. The issue wasn't the information; it was the delivery. Marketers need to see things in action. Theoretical talks don't cut it—practical, straightforward tutorials that walk you through the steps are what marketers crave. Installing tools like dbt and seeing data move can make it all click. It's the difference between hearing about a new tool and actually feeling it work in your hands.There's also a major gap in educational resources that cater to marketers. As Danny highlights, marketing professionals who want to embrace data often get lost in the flood of courses and jargon-heavy materials. It's a jungle out there—marketers want concise, actionable guidance, not a deep dive into tech theory. Without the right content, many opt to stay in their lane, using tools and methods they already know. It feels safer, especially when they're under pressure to perform quickly.Danny points out that this pressure to ramp up fast can discourage experimentation with a warehouse-first approach. New roles often come with tight timelines, and there's a tendency to lean on old habits. Shifting to something like data warehousing means slowing down, learning the ropes, and building enough belief in the new approach to back it up internally. But if you've spent years doing things differently, it's hard to develop the conviction needed to push for change. Confidence comes from exposure and understanding, but without that, the warehouse-first idea feels too foreign to champion.Key takeaway: Marketers often shy away from data teams because they lack practical, accessible education and feel pressured to stick with familiar methods. Building confidence through hands-on learning and real-world examples is crucial for integrating data and marketing in a meaningful way.Overcoming Barriers to Data Literacy in MarketingMany marketers hesitate to engage deeply with data, often because they don't see it as central to their roles. Danny explains that for most, data feels like a secondary tool—something meant to assist rather than dominate their day-to-day work. The challenge is that the pathway to becoming data-savvy isn't straightforward. Even among those who've made the leap, each person's journey looks different. Some take online courses, like those on Codecademy, learning SQL from scratch. Others find mentors who guide them through the maze of data management, or they happen to work in environments where they can lean on a data specialist nearby. But there's no universal roadmap, which makes the process feel daunting.Danny believes that the lack of a clear, predictable path to mastering data is one of the biggest hurdles marketers face. With so many options available—some technical, others more hands-on—marketers often struggle to identify which approach will actually get them the skills they need. For those with limited time, this uncertainty can be a dealbreaker. Without knowing if the investment will pay off, it's easier to focus on other areas of marketing that feel more familiar and essential. Danny points out that while resources like Udemy are improving the situation, marketers still need a straightforward, reliable way to become proficient in data.Another critical factor is the perceived opportunity cost. Marketers are often juggling multiple responsibilities, from staying up-to-date with industry trends to managing campaigns. For many, the idea of dedicating time to learning data—an area they may feel they have minimal expertise in—feels like too large a barrier. Why spend time learning about data warehousing when there are immediate, pressing marketing concepts to master? This fear of committing time and energy to an unfamiliar, complex area keeps many from taking the first step.Danny emphasizes that while the accessibility of learning tools is improving, there's still a significant gap. Even for those who want to upskill, the fear of the unknown and the lack of a guided pathway can make it feel like an insurmountable challenge. Until marketers can see a clear, accessible way to develop these skills, many will remain hesitant to dive into data, choosing to stick to familiar ground instead.Key takeaway: Marketers often shy away from learning data skills due to a lack of accessible, consistent learning paths and the fear of time investment without guaranteed outcomes. Creating structured, easy-to-follow resources is crucial to making data literacy a viable option for busy professionals.Unlocking the Full Potential of Data with dbtDanny describes the transformation dbt brings to the data landscape, making it accessible not just to engineers but also to marketing ops and other non-engineering teams. In the past, accessing and manipulating data was a highly specialized skill, often requiring a marketer to rely heavily on a single engineer. As Danny puts it, you needed to build a relationship with this “one person in a closet somewhere” to get any insight or change implemented. This old approach made data access exclusive, slow, and frustrating for teams trying to move fast.With dbt, Danny explains, the dynamics shift dramatically. It creates different roles and permission levels for everyone interacting with data, enabling a self-service model for marketers and operat...

MILANO (ITALPRESS) - La Customer Experience nel mondo Pharma Retail è stata al centro di un evento organizzato a Milano da Jakala, leader europeo nella data-transformation e partner di riferimento per l'innovazione delle aziende del settore Pharma & Life Sciences. Dedicato ai professionisti del settore, l'incontro ha offerto un momento di riflessione sulla rilevanza crescente della Customer Experience nel mondo Pharma e Consumer Health, un tema sempre più centrale per distinguersi in un mercato caratterizzato da rapidi cambiamenti. xh7/trl/gsl

MILANO (ITALPRESS) - La Customer Experience nel mondo Pharma Retail è stata al centro di un evento organizzato a Milano da Jakala, leader europeo nella data-transformation e partner di riferimento per l'innovazione delle aziende del settore Pharma & Life Sciences. Dedicato ai professionisti del settore, l'incontro ha offerto un momento di riflessione sulla rilevanza crescente della Customer Experience nel mondo Pharma e Consumer Health, un tema sempre più centrale per distinguersi in un mercato caratterizzato da rapidi cambiamenti. xh7/trl/gsl

Lukas Schulte is Co-Founder and CEO of SDF Labs (Semantic Data Fabric), the data transformation layer and query engine platform. They're an open core company powered by the Apache Data Fusion query engine. SDF Labs has raised $9M from investors including RTP Global and Two Sigma Ventures. In this episode, we dig into the complications and pain points with the Modern Data Stack, shifting left with data (ie. moving more over to the client), competing with DBT by adding a query engine, why building in Rust was important, why their CLI is closed source, the importance of a strong partner strategy as a data company & more!

Insight, energy and experience – three elements that shine through in this latest Digital Workplace Impact episode, as Corinne Goldberg, an AI data transformation expert at Google, takes the spotlight. In her first podcast ‘appearance', Corinne talks about her dual passions for digital transformation and fitness; both areas that guide people to achieve elevated performance by inspiring the pushing of boundaries. Corinne herself is entering a new career chapter. She's working with enterprise customers to modernize technology infrastructures through AI services, helping to realize opportunities via cloud technology and AI products. Host Nancy Goebel draws on this new chapter and on Corinne's past experiences to lead us through a fascinating conversation which covers three pillars of digital transformation: productivity, operational resilience and competitive differentiation. The discussion also stresses the importance of fostering a data-driven culture within organizations, using what Corinne calls the ‘ARC' principles of accessibility, reliability and creativity. With tantalizing future-gazing around digital transformation, analytics and AI, along with some topical advice for data transformation teams today, there's something for all. So, listen now for fascinating insights from a Google data transformation expert. (Show notes, links and transcript for this episode.) Guest speaker:Corinne Goldberg,AI Data Transformation Expert at Google Hosted on Acast. See acast.com/privacy for more information.

What is data transformation and why is it important? I had a great chat Pravin Darbare and Maciej Szpakowski on The Ravit Show at the Data + AI Summit by Databricks . Here are the key points we discussed: -- Maciej explained the basics of data transformation and its importance -- We delved into the challenges organizations face when transforming data on Databricks or other cloud data platforms -- Pravin outlined the options organizations have for transforming data and the productivity results they should expect with modern approaches -- Maciej shared insights on what Prophecy does and how it addresses the challenges Pravin spoke about -- We discussed the launch of the Data Transformation Copilot for Databricks and what it means for Prophecy's customers -- Both shared their excitement about the future of data transformation. Stay tuned for more insights from the Summit! #data #ai #dataaisummit #prophecy #theravitshow

Curious about Data Transformation? I had a blast chatting with Raj Bains, Founder & CEO, Prophecy on The Ravit Show at the Data + AI Summit. Here's what we discussed -- -- The importance of the Data Transformation Copilot -- What the copilot for data should look like? -- How Prophecy is implementing the Data Transformation Copilot? -- Examples of its impact on organizations -- What's next for Prophecy? Stay tuned for more updates and insights from the summit! #data #ai #datatransformation #prophecy #theravitshow

Microsoft Fabric seamlessly integrates with generative AI to enhance data-driven decision-making across your organization. It unifies data management and analysis, allowing for real-time insights and actions. With Real Time Intelligence, keeping grounding data for large language models (LLMs) up-to-date is simplified. This ensures that generative AI responses are based on the most current information, enhancing the relevance and accuracy of outputs. Microsoft Fabric also infuses generative AI experiences throughout its platform, with tools like Copilot in Fabric and Azure AI Studio enabling easy connection of unified data to sophisticated AI models.   ► QUICK LINKS: 00:00 - Unify data with Microsoft Fabric 00:35 - Unified data storage & real-time analysis 01:08 - Security with Microsoft Purview 01:25 - Real-Time Intelligence 02:05 - Integration with Azure AI Studio   ► Link References This is Part 3 of 3 in our series on leveraging generative AI. Watch our playlist at https://aka.ms/GenAIwithAzureDBs   ► Unfamiliar with Microsoft Mechanics?  As Microsoft's official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft. • Subscribe to our YouTube: https://www.youtube.com/c/MicrosoftMechanicsSeries • Talk with other IT Pros, join us on the Microsoft Tech Community: https://techcommunity.microsoft.com/t5/microsoft-mechanics-blog/bg-p/MicrosoftMechanicsBlog • Watch or listen from anywhere, subscribe to our podcast: https://microsoftmechanics.libsyn.com/podcast   ► Keep getting this insider knowledge, join us on social: • Follow us on Twitter: https://twitter.com/MSFTMechanics  • Share knowledge on LinkedIn: https://www.linkedin.com/company/microsoft-mechanics/ • Enjoy us on Instagram: https://www.instagram.com/msftmechanics/ • Loosen up with us on TikTok: https://www.tiktok.com/@msftmechanics

In this episode, Amir talks with JD Hannick, an Executive Advisor to Fortune 500 and private equity firms with a deep background in consumer data from Foot Locker, Adobe, and Booz Allen Hamilton. JD discusses the transformation of consumer data, the role of AI and machine learning, and the importance of first-party data. They also explore how private equity firms use data for efficiency and growth in changing economic conditions and the future role of generative AI in enhancing customer experiences. Highlights: 01:28 Consumer Experience and Industry Insights 03:11 AI and Machine Learning in Consumer Industries 06:00 The Role of First-Party Data 10:51 Private Equity and Data Initiatives 13:51 Future of Gen AI in Consumer Industries Guest: JD Hannick is a senior executive known for building high-performing, engaged teams in next-generation data analytics, AI, machine learning, generative AI, personalization, and enhanced customer experience. With experience across various global Fortune 100 organizations, JD helps these companies, as well as private equity firms and their portfolio companies, transform into data-driven businesses. He is sought after for forming strategic initiatives, building investment cases, and realizing value creation through innovative data use. https://www.linkedin.com/in/hannick/ --- Thank you so much for checking out this episode of The Tech Trek. We would appreciate it if you would take a minute to rate and review us on your favorite podcast player. Want to learn more about us? Head over at https://www.elevano.com Have questions or want to cover specific topics with our future guests? Please message me at https://www.linkedin.com/in/amirbormand (Amir Bormand)

Quickly spot real-time indicators of issues as they unfold, without the need to poll or manually monitor changes in your data and without writing a single line of code. That's what the new Real-Time Intelligence service in Microsoft Fabric is all about. It extends Microsoft Fabric to the world of streaming data across your IoT and operational systems. Whether you are a data analyst or business user, you can easily explore high-granularity, high-volume data and spot issues before they impact your business. And as a Data engineer, you can more easily track system level changes across your data estate to manage and improve your pipelines. Courtney Berg, from the Microsoft Fabric product team, joins Jeremy Chapman to explore all of the updates, explain how Real-Time Intelligence adds to what was possible with Data Activator and Microsoft Synapse Real-Time Analytics and demonstrates how this would work to derive insights and take actions automatically in a scenario with multiple live data streams across different data types. ► QUICK LINKS: 00:00 - Real-Time Intelligence 00:54 - How it's different 02:07 - Eventstream and Real-Time Hub 03:27 - Synapse Real-Time Analytics 04:03 - See it in action 05:25 - Use Copilot to stay updated 06:28 - Filter data and set up alerts 08:25 - Sophisticated Logic and Data Integration 09:48 - Data integration to Real-Time Hub 11:23 - Workflow automation 12:18 - System events 13:30 - Additional areas of use for Real-Time Intelligence 14:02 - Wrap up   ► Link References Check out Real-Time Intelligence at https://aka.ms/RealTimeIntelligence For all things Microsoft Fabric, go to https://microsoft.com/fabric ► Unfamiliar with Microsoft Mechanics?  As Microsoft's official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft. • Subscribe to our YouTube: https://www.youtube.com/c/MicrosoftMechanicsSeries • Talk with other IT Pros, join us on the Microsoft Tech Community: https://techcommunity.microsoft.com/t5/microsoft-mechanics-blog/bg-p/MicrosoftMechanicsBlog • Watch or listen from anywhere, subscribe to our podcast: https://microsoftmechanics.libsyn.com/podcast   ► Keep getting this insider knowledge, join us on social: • Follow us on Twitter: https://twitter.com/MSFTMechanics  • Share knowledge on LinkedIn: https://www.linkedin.com/company/microsoft-mechanics/ • Enjoy us on Instagram: https://www.instagram.com/msftmechanics/ • Loosen up with us on TikTok: https://www.tiktok.com/@msftmechanics  

In a time where Data Transformation has never been more important, have you integrated your CRM platform with your analytics platform? Theo Bennett, Analytics Evangelist, joins the podcast to talk about the importance of doing so. To talk with our team anytime on analytics and digital marketing, send an email to info@morevisibility.com 

In the bustling landscape of entrepreneurship, where dreams collide with reality, and visionaries transform industries, few narratives resonate as profoundly as that of Armon Petrossian. From his humble beginnings in Portland, Oregon, to the pulsating heart of Silicon Valley, Armon's journey is not just a tale of success but a testament to the power of perseverance, innovation, and the pursuit of a bold vision.

In this episode, host John Walter and HumanFirst CEO, Gregory Whiteside discuss the following topics: Evolution of Customer Support Technologies: Gregory Whiteside discusses the transformation in customer support through the integration of AI and large language models.Agent Augmentation and Live Chat Platforms: The podcast delves into how HumanFirst initially focused on agent augmentation, integrating AI with live chat platforms like ZenDesk and Intercom to enhance agent performance.Natural Language Understanding (NLU): There's a significant focus on NLU's role in AI, particularly how it classifies text data into various labels or intents based on customer interactions.Custom Taxonomies in AI: The episode emphasizes the importance of developing custom taxonomies to tailor AI solutions to specific business realities and customer conversations.Data Transformation for AI Training: The discussion includes how unstructured data is transformed into high-quality training data for natural language understanding.Prompt Engineering in AI: Gregory talks about the use of prompt engineering to leverage large language models more effectively in AI applications.Impact of Large Language Models like GPT-3 and GPT-4: The podcast covers the effects these models have had on the industry and how they're shaping the future of AI in customer support.AI's Influence on Outsourcing and BPOs: The episode touches upon how AI and automation are increasingly being integrated into Business Process Outsourcing (BPOs) and the outsourcing sector.Additional Resources:Connect with Gregory Whiteside on LinkedIn: https://www.linkedin.com/in/gregory-whiteside/Discover more about HumanFirst: https://www.humanfirst.ai/Connect with John Walter on LinkedIn: https://www.linkedin.com/in/jowalter/

Let's talk data transformation and what makes a greatseller with Co-founder and CEO of Coalesce,  ArmonPetrossian. As more companies move to the cloud theweakness in data transformation is becoming profound.01:15 Meet Armon Petrossian02:21 The problem in being data driven04:28 Introducing Coalesce07:53 Automation defined09:56 The ETL to ELT shift11:54 Typical customer use case12:55 The Coalesce differentiator16:18 Proof of value24:39 Column metadata25:40 Data transformation inhibitors28:17 The future31:52 What makes a great sellerLinkedIn: https://www.linkedin.com/in/armonpetrossian/Website:https://coalesce.io/Want to be featured as a guest on Making Data Simple?Reach out to us at almartintalksdata@gmail.com and tellus why you should be next. The Making Data SimplePodcast is hosted by Al Martin, WW VP Technical Sales,IBM, where we explore trending technologies, businessinnovation, and leadership ... while keeping it simple &fun.Want to be featured as a guest on Making Data Simple? Reach out to us at almartintalksdata@gmail.com and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.Want to be featured as a guest on Making Data Simple? Reach out to us at almartintalksdata@gmail.com and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Let's talk data transformation and what makes a greatseller with Co-founder and CEO of Coalesce,  ArmonPetrossian. As more companies move to the cloud theweakness in data transformation is becoming profound.01:15 Meet Armon Petrossian02:21 The problem in being data driven04:28 Introducing Coalesce07:53 Automation defined09:56 The ETL to ELT shift11:54 Typical customer use case12:55 The Coalesce differentiator16:18 Proof of value24:39 Column metadata25:40 Data transformation inhibitors28:17 The future31:52 What makes a great sellerLinkedIn: https://www.linkedin.com/in/armonpetrossian/Website:https://coalesce.io/Want to be featured as a guest on Making Data Simple?Reach out to us at almartintalksdata@gmail.com and tellus why you should be next. The Making Data SimplePodcast is hosted by Al Martin, WW VP Technical Sales,IBM, where we explore trending technologies, businessinnovation, and leadership ... while keeping it simple &fun.Want to be featured as a guest on Making Data Simple? Reach out to us at almartintalksdata@gmail.com and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.Want to be featured as a guest on Making Data Simple? Reach out to us at almartintalksdata@gmail.com and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Please Rate and Review us on your podcast app of choice!Get involved with Data Mesh Understanding's free community roundtables and introductions: https://landing.datameshunderstanding.com/If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see hereEpisode list and links to all available episode transcripts here.Provided as a free resource by Data Mesh Understanding. Get in touch with Scott on LinkedIn.Transcript for this episode (link) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here and their Data Mesh for Dummies e-book (info gated) here.Nailya's LinkedIn: https://www.linkedin.com/in/nailya-sabirzyanova-5b724310b/In this episode, Scott interviewed Nailya Sabirzyanova, Digitalization Manager at DHL and a PhD Candidate around data architecture and data driven transformation. To be clear, she was only representing her own views on the episode.Some key takeaways/thoughts from Nailya's point of view:When it came to microservices and digital transformation, we aligned our application and business architectures. Now, we have to align our application, business, and data architectures if we want to really move towards being data-driven.To do data transformation well, you must align it to your application architecture transformation. Otherwise, you have two things transforming simultaneously but not in conjunction.It's crucial to involve business counterparts in your data architectural transformation. They know the business architecture best and the data architecture is there to best serve the business. That is a prerequisite to enable continuous business value-generation from the transformation.Re a transformation, ask two simple questions to your stakeholders: What should this transformation enable? How should we enable it? It will give them a chance to share their pain points and their ideas on how to address them. The business stakeholders know their business problems better than the data people

After 150 years in the business, Sun Life U.S. has developed a keen digital strategy that integrates technology and business operations while boosting client engagement and revitalizing its approach to healthcare connectivity, benefits management, and client satisfaction. Paula Bartgis, Chief Information Officer at Sun Life U.S., is leading the company in this transformation and joins Life Accelerated to discuss developing the Sun Life Onboard platform and leveraging AI in call centers to digitally overhaul the life insurance sector. Listen in to learn how Paula Bargis and Sun Life U.S. are demystifying the process of a digital-first ethos in their life insurance endeavors.   Key Takeaways: A client-centered digital transformation strategy can increase efficiency and satisfaction. Transformational leadership values flexibility, team empowerment, and the creation of a fail-fast culture pivotal for sustaining long-term digital innovation. Digital initiatives positively impact organizational growth, from improved client engagement and sales to enhanced employee satisfaction and development.   Timestamps: [00:00] Introduction to the episode [00:12] Paula's career span [01:38] Background on Sun Life U.S. [05:41] Digital transformation within Sun Life U.S. [08:32] The importance of methodology [11:49] How Sun Life U.S. measures transformational value [14:25] When Paula joined Sun Life U.S. and how different it was back then [17:43] The significance of Sun Life U.S. Onboard [21:33] Providing a safe environment for your organization to grow [22:48] Paula's perspective on project management [26:05] Learning as a team and allowing room for mistakes within company culture [27:42] Shifting employees as needed to upskill [30:51] Closing   Resources: Connect with Paula on LinkedIn: https://www.linkedin.com/in/paulabartgis/  Learn more about Sun Life U.S.: https://www.sunlife.com/us/en/

Please Rate and Review us on your podcast app of choice!Get involved with Data Mesh Understanding's free community roundtables and introductions: https://landing.datameshunderstanding.com/If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see hereEpisode list and links to all available episode transcripts here.Provided as a free resource by Data Mesh Understanding. Get in touch with Scott on LinkedIn.Transcript for this episode (link) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here and their Data Mesh for Dummies e-book (info gated) here.Stefan's LinkedIn: https://www.linkedin.com/in/stefan-zima-650229b7/In this episode, Scott interviewed Stefan Zima, Data Transformation Lead at RBI (Raiffeisen Bank International AG). To be clear, he was only representing his own views on the episode.Some key takeaways/thoughts from Stefan's point of view:No one has data mesh all figured out. Go talk to each other. But also don't be ashamed that you are running into challenges. So is everyone else. Data mesh implementers also need to share more of the anti-patterns they are finding.Agile transformation really focuses a lot on communication and transparency. Both are very crucial to really any successful transformation initiative. Humans struggle with uncertainty and change so giving them a lot of information especially about the why prevents unnecessary pushback. Relatedly, there are many things we can take from Agile transformation practices to apply to data/data mesh transformation. It's not a copy/paste but there's still much that is very relevant with some tweaks.Many organizations are still focusing on technology-led transformation, whether data or digital in general. You must also change the mindset and organizational approaches if you want to be successful.In banking, the rise of fintechs (financial technology companies) has made it clear that being nimble and quickly acting on data is crucial. Being data driven is required to remain competitive.Data mesh can mean far less friction in getting to serving use cases. Instead of fighting against the data protection office, they are involved from the start. That time to market is especially crucial in banking now.If you can, look to make your data sharing policies and approaches generic enough to only create friction when there truly is something different that should be examined further.If you really want to be 'data-driven', if you really want to be a data company, you have to find and address the friction points in your data processes. Stop trying to simply get better at processes that...

Better use of data is key to more effective government. Across government, teams are doing fascinating work with data. But those projects don't get the attention they deserve. Data Bites aims to change that. This event was the 46th in our series, where the speakers presented their work in an exciting, quickfire format. Each speaker had eight minutes, followed by eight minutes of questions from the audience. This month's speakers were: Prateek Buch, Head of Collective Intelligence at Policy Lab, on their approach to crowdsourcing knowledge Craig Campbell, High Streets Data Service Manager at the Greater London Authority, on adaptive re-use of data for adaptive re-use of high streets Caroline Kempner, Head of Data Transformation at the Department for Education, on automated daily school attendance data Becky Tinsley, Deputy Director of ONS Local and Coherence, and Emma Hickman, Deputy Director of Subnational Statistics and Analysis at the Office for National Statistics, on ONS's work on subnational statistics and the ONS Local service The event was chaired by Gavin Freeguard, Associate at the Institute for Government.

In Episode 50, of Season 3, of Driven by Data: The Podcast, Kyle Winterbottom is joined by Joseph George, CEO at Dufrain, where they discuss the seven sins of data transformation, which includes; The differences between being an employee in a big corporate to running a business The realities of the Data Science hype cycle in 2017/18Why the good work is only done in pockets and silosResearch that suggests that only 27% of organisations are ready to achieve benefits from AIWhy they commissioned their ‘State of Data' Report and some of the key findings Why most organisations believe they're significantly more data mature than they actually are Why data is important but not urgent How most organisations can't tie their D&A work to any increase in profit /revenue The reality of how organisations are still using data within an organisationHow organisations are suffering from the “streetlight effect”Why most data and analytics projects still don't deliver the desired benefits How most of the blockers are non-technical The seven sins of data transformation Why data is an afterthought in most transformation programmes The lack of good programme/project management on most data and analytics initiatives The importance of understanding what success and ‘completed' is for different stakeholdersWhy many agile projects aren't actually agileThe three lenses of collaboration and why the lack of it negatively impacts many D&A projects Why everyone needs to think and act commercially and speak in business language Why it all ties back to revenueWhy we all have to look in the mirror The importance of being able to articulate the benefit of any data initiative Why the CIO is being chosen over the CDO for having responsibility for AIYou can find The State of Data Report here; https://www.dufrain.co.uk/

Nadia Carta shares her journey of leaving her small island home in Sardinia, Italy and breaking free from the limitations imposed by her religion on this episode of The Life Shift Podcast. The conversation explores themes of self-discovery, manifesting-dreams, and embracing intuition.Takeaways:Leaving Restrictive Environments: Nadia's story highlights the courage it takes to break free from restrictive environments and pursue a life aligned with personal values and aspirations. Her journey serves as an inspiration for listeners who may be seeking their path outside of societal expectations.The Power of Manifestation: Nadia discusses the concept of manifesting dreams and the importance of envisioning a desired future. Through her personal experiences and transformation, she demonstrates the power of channeling thoughts, efforts, and energy toward goals through traditional business planning or personal intuition.Embracing Intuition and Self-Discovery: Nadia shares her journey of self-discovery and embracing her intuition, which led her to understand her true desires and make life-altering decisions. Her story encourages listeners to listen to their inner selves, trust their instincts, and explore new possibilities.Nadia Carta was born and raised in Sardinia, Italy. She escaped a restrictive religious community and pursued a Master's in Marketing, Brand Strategy, and Management from IULM in Milan. Nadia's career led her to Google, where she currently serves as the Head of Data Transformation in the US. She is also a certified member of the International Coaching Federation and actively volunteers for non-profit organizations. Passionate about leadership, Nadia co-created 99 Humans, a platform focused on humanizing leadership. In 2022, she launched Spark Your Zeal, a lifestyle platform and movement to inspire personal and professional transformation. Nadia resides in Manhattan, New York, with her life partner and two daughters.Nadia's vibrant spirit and passion inspire those around her.Links:http://www.nadiacarta.com/listhttp://www.99humans.com/Linkedin profile: https://www.linkedin.com/in/nadiacarta/Instagram: NadiaCarta in NYC (@nadiacarta)Connect with "The Life Shift" podcast:Instagram: http://www.instagram.com/thelifeshiftpodcastFacebook: http://www.facebook.com/thelifeshiftpodcastYouTube: https://bit.ly/thelifeshift_youtubeTwitter: http://www.twitter.com/thelifeshiftpodLinkedIn: https://www.linkedin.com/company/thelifeshiftpodcastWebsite: http://www.thelifeshiftpodcast.com/ Hosted on Acast. See acast.com/privacy for more information.

Yannick Misteli is the head of engineering for the go-to-market domain at Roche, a $250 billion multinational pharmaceutical and diagnostics company.  Roche was an early supporter of dbt Cloud, and Yannick helped move his team of 120+ engineers to a modern data stack. He always finds a way to push the boundaries to make a large company founded in 1896 incredibly modern and innovative. We wanted to know more about the "how" of the work—the people, process, and technology.  Read more about Roche's data journey here: https://docs.getdbt.com/blog/dbt-squared

In this episode, best-selling author and leadership coach Nils Vinje interviews Nadia Carta, Head of Data Transformation at Google. Nadia is a powerhouse of inspiration, known for her ability to spark zeal in others. In our conversation, Nadia shared three key lessons from her 14-year tenure at Google: curiosity, authenticity, and aiming for big things. She believes that curiosity opens doors to new environments and genuine connections. Authenticity and vulnerability, she says, are the building blocks of trust. And aiming for the impossible? That's what drives her to tackle challenging projects. But it's not all about success. Nadia opened up about a recent experience of not getting a role she had prepared for. Despite the initial disappointment, she used this as an opportunity for self-reflection and growth. This led her to launch the "Spark Your Zeal" movement, write a book, and start her own podcast on leadership. Nadia's story is a testament to the power of resilience and the beauty of turning setbacks into stepping stones. She believes that by fully putting herself out there, she is giving others permission to do the same. This approach to business, which she calls "business with grace," focuses not only on numbers and plans, but also on building a next generation of humans who find fulfillment in meaningful work. We also discussed the importance of being the CEO of your own career and taking charge of personal growth and development. Nadia emphasized the value of having a coach and engaging in daily practices like journaling and mindfulness meditation for self-awareness. I hope Nadia's story inspires you as much as it inspired me. Remember, in life, there are only three answers: yes, not yet, and something better is coming. So, keep sparking your zeal and pursuing your dreams! Podcast Highlights: 3:41 - Lessons learned from 14 years at Google Nadia reflects on her 14-year tenure at Google, highlighting the importance of curiosity, authenticity, vulnerability, and aiming for big things in her leadership style. 8:19 - Dealing with rejection and learning from it Nadia shares her experience of not getting a role she had prepared for, discussing the initial disappointment and the lessons she learned from the experience. 10:36 - The power of "something better" Nadia reflects on her rejection in a job interview and how it led her to discover the concept of "something better" in life. 13:35 - The gift of rejection Nadia explains how the rejection she faced allowed her to pause, ask herself tough questions, and give birth to her biggest dreams. 20:31 - The importance of putting oneself out there Nadia shares how putting herself out there fully has given others permission to bring themselves more and has created a sense of belonging and thriving in the corporate world. 21:49 - Business with grace and creating an inclusive environment Nadia discusses how she inspires her clients by not only focusing on tech and ads, but also on building a better future and creating a next generation of humans who fulfill themselves while doing meaningful work. 24:12 - Being the CEO of your career and leading oneself first Neil and Nadia talk about the importance of being in charge of one's career, figuring things out, and leading oneself first in order to have a meaningful life and make a positive impact on the world. 30:29 - The importance of having a coach Having a coach can provide external perspective and keep you honest with yourself. 31:51 - The power of journaling and mindfulness meditation Journaling and mindfulness meditation can bring awareness and help align with your true desires. Connect with Nadia on Linkedin.

In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms Data, Dataset, Big Data, DIKUW Pyramid, explain how these terms relate to AI and why it's important to know about them. Show Notes: FREE Intro to CPMAI mini course CPMAI Training and Certification AI Glossary AI Glossary Series – DevOps, Machine Learning Operations (ML Ops) AI Glossary Series – Automated Machine Learning (AutoML) AI Glossary Series – Data Preparation, Data Cleaning, Data Splitting, Data Multiplication, Data Transformation AI Glossary Series – Data Augmentation, Data Labeling, Bounding box, Sensor fusion AI Glossary Series – Data, Dataset, Big Data, DIKUW Pyramid Continue reading AI Today Podcast: AI Glossary Series – Data Warehouse, Data Lake, Extract Transform Load (ETL) at Cognilytica.

In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms Data, Dataset, Big Data, DIKUW Pyramid, explain how these terms relate to AI and why it's important to know about them. Show Notes: FREE Intro to CPMAI mini course CPMAI Training and Certification AI Glossary AI Glossary Series – DevOps, Machine Learning Operations (ML Ops) AI Glossary Series – Automated Machine Learning (AutoML) AI Glossary Series – Data Preparation, Data Cleaning, Data Splitting, Data Multiplication, Data Transformation AI Glossary Series – Data Augmentation, Data Labeling, Bounding box, Sensor fusion AI Glossary Series – Data, Dataset, Big Data, DIKUW Pyramid Continue reading AI Today Podcast: AI Glossary Series – Structured Data, Unstructured Data, Semi-structured Data at Cognilytica.

In this episode, we dive into the entrepreneurial journey of Colin Dougherty, the founder behind the groundbreaking data analytics tool, Zenlytic. Colin shares his roller-coaster ride from college failures to founding successful companies, highlighting the critical lessons learned along the way. His story is one of relentless determination and a willingness to bet it all on his dreams.Colin reveals the inspiration behind Zenlytic, a platform designed to make data analysis accessible to everyone, even those without a technical background. He discusses the fusion of AI, specifically Chat GPT, with their proprietary semantic layer, creating an accurate and user-friendly data interaction tool.The conversation delves into the importance of company culture at Zenlytic, where a small, tight-knit team of experts collaborates with global contractors to achieve their ambitious goals. Colin emphasizes the power of patience when dealing with language barriers, which can yield incredible results.Learn how Zenlytic uses its own tool to streamline data processing and communication within their organization, allowing them to save time and resources while staying agile and efficient. Colin also shares compelling success stories from their clients, showcasing how Zenlytic has transformed their data analysis processes.Key Takeaways: Colin's journey from college failures to successful entrepreneurship. The birth of Zenlytic and the vision behind making data analysis accessible. The fusion of AI and a semantic layer for 100% accurate data interaction. The power of patience and global collaboration in building a strong team. Streamlining data processing with Zenlytic's self-serve analytics tool. Real-world success stories from Zenlytic's satisfied clients.Whether you're a data enthusiast, an aspiring entrepreneur, or a business leader seeking efficient data solutions, this conversation offers invaluable insights.

This week on the MongoDB Podcast, we interview Nikhil Smotra at .local Live NYC. Nikhil explains how Dataworkz combines AI, data integration, and transformation to simplify customer profiling. He also discusses how Dataworkz uses MongoDB's flexible data model for language models for seamless data handling.Listen for:[01:04 - 01:32] What Dataworks is about[01:44 - 02:42] What Dataworkz is doing differently[03:27 - 05:08] Simplifying a Customer 360[05:33 - 07:16] How MongoDB and Dataworkz work together[10:37 - 12:08] What Nikhil would build as a developer[12:32 - 14:47] Reducing the friction between data consumers and data producers[15:39 - 18:39] What you need to know about Dataworkz

What reports do you absolutely need?    Companies transitioning from a different system into BC/NAV have built many impressive reports with Excel already, but we all know maintaining them can be a bit tricky, not to mention it can bog down the system.    The magic is, you can build a single, comprehensive report within that data warehouse. This report will present you with a complete view of the data you need.    Learn more in this new episode of

In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms V's of Big Data, Data Volume, Exabyte / Petabyte / Yottabyte / Zettabyte, Data Variety, Data Velocity, Data Veracity, explain how these terms relate to AI and why it's important to know about them. Show Notes: FREE Intro to CPMAI mini course CPMAI Training and Certification AI Glossary AI Glossary Series – DevOps, Machine Learning Operations (ML Ops) AI Glossary Series – Automated Machine Learning (AutoML) AI Glossary Series – Data Preparation, Data Cleaning, Data Splitting, Data Multiplication, Data Transformation AI Glossary Series – Data Augmentation, Data Labeling, Bounding box, Sensor fusion AI Glossary Series – Data, Dataset, Big Data, DIKUW Pyramid Continue reading AI Today Podcast: AI Glossary Series – V's of Big Data, Data Volume, Exabyte / Petabyte / Yottabyte / Zettabyte, Data Variety, Data Velocity, Data Veracity at Cognilytica.

In this episode, Carlos Peralta, the Director of Data Engineering and Cloud Platform at Moderna Therapeutics, joins Amir Bormand to discuss data transformation and its role as a growth driver. He also highlights his responsibilities as the Director of Cloud Data Platform, which include data strategy, acquisition, and organization. Tune in to learn more about aligning company needs, ROI and overcoming challenges in delivering short-term and long-term data solutions. Highlights: [00:02:34] Data transformation and growth. [00:03:40] Cloud infrastructure and services. [00:09:09] Offensive capabilities of data. [00:12:30] Enabling business stakeholders with data. [00:16:28] New trends in technology. [00:19:34] Handling urgent requests outside the roadmap. [00:24:33] Cloud transformation challenges. [00:25:36] Skill transfer to the cloud. Carlos Peralta has built, acquired, and scaled technology companies from inception to successful acquisitions by publicly-traded large enterprise companies as a strategic and cloud data platform enthusiast. Throughout his 18+ year career, he has managed, developed, and launched state-of-the-art cloud data platforms, including the Moderna Cloud Data Platform that supports multiple studies today. Additionally, Mr. Peralta has been selected to present multiple times at AWS Reinvent on Data Management, Data Platform, AWS Data Exchange, and ETL tools and how they speed, improve, centralize, and standardize data acquisition across the pharma space. Carlos Peralta holds a BS In Computer Science from the EAN University in Colombia and recently completed an Executive HBS Program at Harvard focused on Data Enablement / AI. He has extensive experience in strategic partnership development, distributed systems, AWS Services, data centers, and cloud infrastructure operations at scale.Outside of work, Carlos enjoys playing golf, world traveling, practicing CrossFit, and spending time with his wife.  His profile can be found at https://www.linkedin.com/in/carlos-a-peralta/ --- Thank you so much for checking out this episode of The Tech Trek, and we would appreciate it if you would take a minute to rate and review us on your favorite podcast player. Want to learn more about us? Head over at https://www.elevano.com Have questions or want to cover specific topics with our future guests? Please message me at https://www.linkedin.com/in/amirbormand (Amir Bormand)

Data is the heart of AI. So, therefore doing things associated with your data is going to be critical for AI projects. This includes Data Preparation, Data Cleaning, Data Splitting, Data Multiplication, and Data Transformation. In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms above and explain why they are important for AI projects. Continue reading AI Today Podcast: AI Glossary Series – Data Preparation, Data Cleaning, Data Splitting, Data Multiplication, Data Transformation at Cognilytica.

Please Rate and Review us on your podcast app of choice!Get involved with Data Mesh Understanding's free community roundtables and introductions: https://landing.datameshunderstanding.com/If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see hereEpisode list and links to all available episode transcripts here.Provided as a free resource by Data Mesh Understanding. Get in touch with Scott on LinkedIn if you want to chat data mesh.Transcript for this episode (link) provided by Starburst. See their Data Mesh Summit recordings here and their great data mesh resource center here. You can download their Data Mesh for Dummies e-book (info gated) here.Benny's LinkedIn: https://www.linkedin.com/in/bennybenford/Benny's Substack: https://www.datent.com/p/elevating-data-to-a-profession-whyIn this episode, Scott interviewed Benny Benford, the former CDO at Jaguar Land Rover (JLR) and who is currently building out a community around data transformation. To be clear, he was only representing his own views on the episode.Some key takeaways/thoughts from Benny's point of view:!Controversial!: As a data leader, if you want to be effective, you will have to focus much more on what role data work plays in the organization rather than the data work itself. It's about partnering and communication, not SQL and LLMs. That can be a hard lesson but it's crucial to being successful.?Controversial?: It's easy to try - and common to want - to separate data culture and organizational culture but it doesn't really work. "Data culture is … an aspect of your organizational culture that you're trying to build and create."A guidepost for your transformation journey: "If you want to go fast, go alone; if you want to go far, go together."The data team can be leaders in transforming the organization but they must do it in collaboration, not just going out as pioneers 'to set an example' or something. Absolutely partner with your transformation org if you have one.When you align your data culture transformation to the overall organization transformation messaging, you aren't fighting for attention to a different message. You are using the...

Looking to explore the latest trends and advancements in the world of technology and network with some of the brightest minds in the industry? Gain insights on topics like cybersecurity, local approaches to inclusive tech leadership, gaming technology, data, space technology and more. Register using the promotional code for The Analytics Show listeners - ACSTAS at the ACS QLD State Tech Summit registration page to save $125 off the event ticket. Is it possible to bridge the gap between the technical and the business teams? How do you engage the technical side with the business side, getting them to champion themselves and think about the real purpose of their work? And how do you spark a data-driven culture within the business teams? In this episode, Michaela will share her insights on bringing all teams together to drive success and collaboration. Meet Michaela Shea Michaela's Role as an Data Strategy Leader at Bendigo and Adelaide Bank Michaela Shea is the Head of Data Strategy and Engagement at Bendigo and Adelaide Bank. They are one of Australia's biggest banks, with more than 7,000 employees helping over 1.9 million customers achieve their financial goals. Bendigo's vision is to be Australia's bank of choice, and they believe their success is driven by their purpose – to feed into the prosperity of our customers and communities, not off it. ​ Michaela's Other Work in Data Analytics Michaela is dedicated to helping organisations not only survive but thrive in the face of digital disruption by utilising data, AI, and technology through data-driven, technological, and cultural strategies. She has extensive experience in financial services, utilities, and the public sector, having led and built teams to drive three comprehensive organisation-wide transformations. Prior to her current role at Bendigo, Michaela served as the Senior Manager of Data Governance at RSL Queensland, where she implemented formal data governance. Additionally, she has held several other senior leadership positions, including Senior Manager of Data and Analytics Transformation at AGL Energy, Program Change Manager of Data Transformation at the National Australia Bank, and Manager of Change & Communications at Suncorp Group. Culture, Teams, and Mindsets In this exclusive analytics podcast episode, Michaela shares: Her triathlon experiences and how it translated into her current work Her experience transferring across companies and how it has translated into her current work Her current role as the Head of Data Strategy and Engagement at Bendigo and Adelaide Bank Common ways businesses have failed to achieve a data-driven culture Tricks for getting the technical team to participate in the business side Sparking ideas within business leaders regarding advanced analytics Bringing together different teams within an organisation Building a solution while keeping the business engaged Overcoming the 'all bark and no bite' of agile How to help engineers champion their work and sell themselves Borrowing from the mindset of selling first, building later to get leaders more invested If you are an advanced analytics professional looking to venture into other sides of the business or a leader looking to learn more about the technical side, this is the episode you do not want to miss.  --- Send in a voice message: https://podcasters.spotify.com/pod/show/analyticsshow/message

Mike Gustafson is the President of Search Discovery, a data transformation company that helps organizations transform by executing data strategies to achieve desired business outcomes. As a leader and senior executive, he has experience leading professional services and technology teams. Mike has also created and implemented solutions for multiple industries including nonprofits, consumer products, and financial services. Before Search Discovery, he held various partner roles at Rosetta. In this episode… In the era of digital marketing and advertising, data privacy is a growing concern, and companies must recognize the implications of data collection to comply with emerging regulations. But a data privacy compliance survey of 300 businesses reveals that in some industries, approximately 93% of these companies lack restrictions around data collection. So how can you safeguard consumer data? According to data analytics expert Mike Gustafson, many organizations lack an adequate understanding of the data they've gathered. Acknowledging privacy regulations requires developing a proactive data collection strategy that addresses objectives for usage, variety, and management. Businesses should only gather relevant information to personalize and streamline the customer experience, so holistic privacy programs involving the entire organization are essential.  In this episode of She Said Privacy/He Said Security, Jodi and Justin Daniels chat with Mike Gustafson, President of Search Discovery, about transforming how corporations collect and utilize consumer data. Mike shares why companies should consider end-to-end data transformation, the challenges of data privacy compliance, and how companies respond to Google Analytics regulations.

In this episode, we are joined by Matthew Scullion, the CEO of Matillion, a cloud data integration software company. Join us as we dive into an intriguing conversation about ETL and ELT - their differences, how to choose which one to use, and why making the wrong choice can lead to a waste of IT budgets. Matthew also sheds light on the challenges caused by the ever-increasing volume and complexity of data and data sources, and shares his insights on how to overcome these barriers. We also discuss the risks of information gaps caused by slow and inefficient data processes and how to bridge these gaps. Furthermore, we explore the best approach for data teams in using a mix of no code/high code/own code and how to determine which one is best suited for specific circumstances in terms of cost savings and efficiency. Tune in for an enlightening conversation on all things data integration with Matthew Scullion!

dbt is known as being part of the Modern Data Stack for ELT processes. Being in the MDS, dbt Labs believes in having the best of breed for every part of the stack. Oftentimes folks are using an EL tool like Fivetran to pull data from the database into the warehouse, then using dbt to manage the transformations in the warehouse. Analysts can then build dashboards on top of that data, or execute tests.It's possible for an analyst to adapt this process for use with a microservice application using Apache Kafka® and the same method to pull batch data out of each and every database; however, in this episode, Amy Chen (Partner Engineering Manager, dbt Labs) tells Kris about a better way forward for analysts willing to adopt the streaming mindset: Reusable pipelines using dbt models that immediately pull events into the warehouse and materialize as views by default.dbt Labs is the company that makes and maintains dbt. dbt Core is the open-source data transformation framework that allows data teams to operate with software engineering's best practices. dbt Cloud is the fastest and most reliable way to deploy dbt. Inside the world of event streaming, there is a push to expand data access beyond the programmers writing the code, and towards everyone involved in the business. Over at dbt Labs they're attempting something of the reverse— to get data analysts to adopt the best practices of software engineers, and more recently, of streaming programmers. They're improving the process of building data pipelines while empowering businesses to bring more contributors into the analytics process, with an easy to deploy, easy to maintain platform. It offers version control to analysts who traditionally don't have access to git, along with the ability to easily automate testing, all in the same place.In this episode, Kris and Amy explore:How to revolutionize testing for analysts with two of dbt's core functionalitiesWhat streaming in a batch-based analytics world should look likeWhat can be done to improve workflowsHow to democratize access to data for everyone in the businessEPISODE LINKSLearn more about dbt labsAn Analytics Engineer's Guide to StreamingPanel discussion: If Streaming Is the Answer, Why Are We Still Doing Batch?All Current 2022 sessions and slidesWatch the video version of this podcastKris Jenkins' TwitterStreaming Audio Playlist Join the Confluent CommunityLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Intro to Event-Driven Microservices with ConfluentUse PODCAST100 to get an additional $100 of free Confluent Cloud usage (details)   

Let's talk data transformation and what makes a greatseller with Co-founder and CEO of Coalesce,  ArmonPetrossian. As more companies move to the cloud theweakness in data transformation is becoming profound.01:15 Meet Armon Petrossian02:21 The problem in being data driven04:28 Introducing Coalesce07:53 Automation defined09:56 The ETL to ELT shift11:54 Typical customer use case12:55 The Coalesce differentiator16:18 Proof of value24:39 Column metadata25:40 Data transformation inhibitors28:17 The future31:52 What makes a great sellerLinkedIn: https://www.linkedin.com/in/armonpetrossian/Website: https://coalesce.io/Want to be featured as a guest on Making Data Simple?Reach out to us at almartintalksdata@gmail.com and tellus why you should be next. The Making Data SimplePodcast is hosted by Al Martin, WW VP Technical Sales,IBM, where we explore trending technologies, businessinnovation, and leadership ... while keeping it simple &fun.

In this episode, Rob Phillips talks about making an impact through data transformation at a large public company. Key takeaways: Becoming a more data-driven company Using data to predict what's going to happen What does that mean to the company? How far down the maturity path do you look to build your roadmap Benefits of moving quickly to prove your MVP Focus on testing out new capabilities Give the team a problem to solve and allow them to figure out how How do you compete and attract the type of talent you need Innovation garage and teams share About today's guest: see below ⬇️ Rob Phillips was the Director of Technology at CarMax. He was responsible for creating their Master Data Management, Data Science, and Machine Learning technologies, enabling CarMax to continue to be the leader in both in-person and digital car buying experiences. Before CarMax, he was responsible for all applications and data at a healthcare company in Atlanta. Rob is currently the Vice President of Software Engineering at UDig in Richmond, Virginia. When not at work, Rob would prefer to spend his time with his family sailing the Chesapeake Bay. He holds a B.S. in IT Management from the University of Richmond and studied Computer Science at Virginia Tech. LinkedIn: https://www.linkedin.com/in/rophilli/ ___ Thank you so much for checking out this episode of The Tech Trek, and we would appreciate it if you would take a minute to rate and review us on your favorite podcast player. Want to learn more about us? Head over at https://www.elevano.com Have questions or want to cover specific topics with our future guests? Please message me at https://www.linkedin.com/in/amirbormand (Amir Bormand)

The Key to Improving Tech Outcomes: Project Management E33 From managing budgets to meeting deliverables in a timely manner, project managers play a critical role in helping organizations improve outcomes and maintain successful partnerships. So, how can tech companies leverage project management to reimagine how they produce deliverables for their clients? The December 2022 episode […]

Teresa Tung is joined by two data mesh experts, Justin Borgman, Starburst - Chairman & CEO, and Bryan Aller, Comcast - Director, Software Development & Engineering, to discuss what's new about data virtualization with data mesh. They share their experiences which is driving a new interest in data virtualization. Listen now.                

While securing the support of senior executives is a major hurdle of implementing a data transformation program, it's often one of the earliest and easiest hurdles to overcome in comparison to the overall program itself. Leading a data transformation program requires thorough planning, organization-wide collaboration, careful execution, robust testing, and so much more. Vanessa Gonzalez is the Senior Director of Data and Analytics for ML & AI at Transamerica. Vanessa has experience in data transformation, leadership, and strategic direction for Data Science and Data Governance teams, and is an experienced senior data manager. Vanessa joins the show to share how she is helping to lead Transamerica's Data Transformation program. In this episode, we discuss the biggest challenges Transamerica has faced throughout the process, the most important factors to making any large-scale transformation successful, how to collaborate with other departments, how Vanessa structures her team, the key skills data scientists need to be successful, and much more. Check out this month's events: https://www.datacamp.com/data-driven-organizations-2022

The data transformation is a long journey for most companies. So how should we best engage teams and individuals in the data transformation journey, help them grow the data literacy, culture, etc.? Let's address this important topic with Nicolas Averseng, Founder and CEO of YOOI.