Podcasts about Data quality

Norbert Kremer, Ph.D., cloud solution architect and TDWI faculty member, joins host Andrew Miller to discuss data quality for AI - including the difference between data quality for AI and BI, the importance of training data for AI models, and challenges with unstructured data. Please visit Data Quality for AI for more information on Norbert's course at TDWI San Diego. ____________ More information: ·       TDWI Conferences: https://bit.ly/3XqBhGH ·       TDWI Modern Data Leader's Summits: https://bit.ly/4902fuu ·       TDWI Virtual Summits: https://bit.ly/31HJ2xr ·       Seminars: https://bit.ly/3WxQPr4 ·       More Speaking of Data Episodes: https://bit.ly/3JsQPWo Follow Us on: ·       LinkedIn - https://bit.ly/42zCZZB ·       Facebook - https://bit.ly/49uej7j ·       Instagram - https://bit.ly/3HM8x57 ·       X - https://bit.ly/3SsYu9P

Elliot Shmukler (Co-Founder and CEO at Anomalo) talks about the impact of data quality on AI, how unstructured data can be improved, and how monitoring of data lakes can help prevent model drift and give organizations confidence with predictable results.SHOW: 945SHOW TRANSCRIPT: The Cloudcast #945 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET CLOUD NEWS OF THE WEEK:  http://bit.ly/cloudcast-cnotwNEW TO CLOUD? CHECK OUT OUR OTHER PODCAST:  "CLOUDCAST BASICS"SPONSORS:[DoIT] Visit doit.com (that's d-o-i-t.com) to unlock intent-aware FinOps at scale with DoiT Cloud Intelligence.[VASION] Vasion Print eliminates the need for print servers by enabling secure, cloud-based printing from any device, anywhere. Get a custom demo to see the difference for yourself.[FCTR] Try FCTR.io (that's F-C-T-R dot io) free for 60 days. Modern security demands modern solutions. Check out Fctr's Tako AI, the first AI agent for Okta, on their websiteSHOW NOTES:Anomalo websiteThe Cloudcast #598 - Data QualitySnowflake invests in AnomaloTopic 1 - Elliot, welcome back! It's hard to believe it has been 3 years since we spoke! Give everyone a brief introduction.Topic 2 - Here's the problem I see when it comes to AI adoption today. There isn't an “off the shelf” AI model with an organization's data built in; that's impossible. So, you must bring this data, often unstructured, to the model, often with mixed results. Do you agree?Topic 3 - I see data quality in two ways… the quality of the data before ingestion is one way, we want the data to be clean going in. But, we also need a way to detect, mitigate, and do a root cause analysis for quality checks along the way, correct? Give everyone an idea of what this life cycle looks like.Topic 4 - What are you seeing as the barriers to adoption? Is it the tools, the models, the need for RAG pipelines, the lack of data scientists, and AIOps?Topic 5 - We have this crossroads where proprietary data makes an organization unique, but exposing that unique data puts the organization at risk. How much of a factor does this play, and how do you advise organizations around this complex intersectionTopic 6 - There is always this concept of predictable results. This answer should be consistent and repeatable. We've seen things like model/data drift and hallucinations hinder this concept, leading to a lack of confidence in the results. How do you advise organizations to tackle this lifecycle management and predictability over time?Topic 7 - If listeners want to get started and learn more, what's the best way to get started?FEEDBACK?Email: show at the cloudcast dot netBluesky: @cloudcastpod.bsky.socialTwitter/X: @cloudcastpodInstagram: @cloudcastpodTikTok: @cloudcastpod

High Culture Hiring – so nennt Medizinal­cannabis-Scale-up enua sein kompromissloses Recruiting-System. Deshalb habe ich mit CEO Albert Schwarzmeier gesprochen, um herauszufinden, wie er in nur zwei Jahren von 15 auf 51 Mitarbeitende gewachsen ist – und zwar profitabel, ganz ohne Burn-rate.   Zunächst einmal: Schnelles Wachstum gelingt nur mit den richtigen Leuten. Darum definiert enua jede Rolle glasklar nach Purpose, Impact, KPIs und Culture Fit, bevor eine Anzeige live geht. Dann öffnet sich der Funnel aus LinkedIn-Posts, StepStone-Ads und einem starken Empfehlungs­programm, das bereits 30 % aller Neueinstellungen liefert.   Außerdem punktet das Team mit Tempo. HR meldet sich innerhalb von 48 Stunden im Video­call. Danach folgt der Hiring-Manager noch in derselben Woche. Schließlich trifft jede Bewerberin oder jeder Bewerber spätestens am siebten Tag eine Geschäfts­führerin oder einen Geschäfts­führer. Fällt ein Daumen im Panel nach unten, endet der Prozess sofort – daher spart enua Zeit, Geld und Nerven.   Das Konzept endet natürlich nicht mit der Unterschrift. Somit führt das Onboarding neue Kolleg:innen in den ersten 30 Tagen gezielt durch Sales, Supply Chain, Data & Quality. Dadurch verstehen sie das hoch regulierte Produkt blitzschnell und liefern schon in Woche vier erste Quick Wins. Das Ergebnis: nahezu keine Kündigungen in der Probezeit.   Zugleich bleibt Recruiting Chefsache. Albert veröffentlicht wöchentlich drei LinkedIn-Beiträge, misst Employer-Branding-KPIs und gibt jede Einstellung persönlich frei. Auf diese Weise schützt er die Kultur, obwohl das Team rasant wächst.   Hinzu kommt ein konsequentes Reporting. Jede Woche prüft das People-Team Time-to-Hire, Funnel-Conversion und Cost-per-Hire. Sobald ein Ziel verfehlt wird, reagiert enua sofort – sei es mit neuen Sourcing-Quellen oder angepassten Interview­fragen.   Mein Fazit: High Culture Hiring ist ein echter Wachstumsbooster. Wer seine Kultur messbar macht und jede Einstellung an klaren Kriterien ausrichtet, gewinnt den War for Talent – sogar in Nischen wie Medizinal­cannabis.   Daher solltest du jetzt reinhören, wenn du dein Recruiting auf High-Speed und High-Quality bringen willst. In der Episode bekommst du Alberts komplette Checkliste zum Nachbauen.  

“Data quality was the number one obstacle to AI success […]. It's like Groundhog Day: the biggest problem in data warehousing was data quality […] and now in AI it's still data quality.”

Host Charlie Beetson speaks with Edward Broadhurst (Senior Business Risk Manager, Nordea) and Rodrigo Lopes D. (Head of Department – Financial Sponsors, DNB) about the critical role data quality plays in the fight against financial crime. They explore how accurate, reliable information supports compliance, enhances fraud detection, and strengthens risk assessment. This episode offers actionable insights for professionals focused on anti-money laundering, data governance, and financial crime prevention across regulated industries and banking environments.

In the data and AI space, action cannot wait. And, neither can celebration! Tune into the anniversary special of Bringing Data and AI To Life as hosts, Amy Horowitz, GVP Solutions Sales and Business Development at Informatica and Nick Dobbins, VP and Worldwide Field CTO, Informatica, along with special appearances from the podcast team - Rudra Ray, Rameez Ghouz, Stephanie Rogers and Gary Loste, celebrate one year of cutting through data and AI complexity. Together, they reflect on key insights and transformative discussions that have shaped the industry landscape, including AI governance challenges, the importance of trusted data for scaling AI initiatives, and practical strategies for modernizing data stacks. As you listen in, continue to cut through the chaos of data and AI and walk the path of clarity with us!

In “CHAINge, AI, and the Future of Freight”, Joe Lynch and Bart De Muynck, an industry expert and thought leader with over 30 years of supply chain and logistics experience across the globe, discuss how artificial intelligence and emerging technologies are reshaping the logistics landscape and what the future holds for global freight. About Bart A. De Muynck Bart De Muynck stands as an accomplished industry expert and thought leader, boasting over three decades of global supply chain and logistics experience. His distinguished career includes significant roles at major international companies such as EY, GE Capital, Penske Logistics, PepsiCo, and various tech firms. Notably, he spent eight years as a VP of Research at Gartner and recently served as Chief Industry Officer at project44. A highly sought-after speaker, Bart currently advises multiple companies and industry organizations in logistics. He chairs ASCM's CHAINge conference in Europe and North America, is the Vice Chair for Transformfest 2025, and is a member of the Forbes Technology Council, SCLA, WEF, and CSCMP's Executive Inner Circle. Born in Belgium, he now resides with his family in Texas, USA. Through Bart De Muynck LLC, he helps organizations navigate the complex technological landscape, tailoring solutions and mitigating challenges to drive operational efficiency and manage risk. His new thought leadership website, Better Supply Chains, curates high-quality content focused on leveraging technology to create more efficient, inclusive, and equitable supply chains, ultimately aiming for both better supply chains and improved individual lives. About Better Supply Chains Better Supply Chains is a new thought leadership website curated by former Gartner analyst and Industry Expert Bart De Muynck, with the primary goal of centralizing high-quality content designed to help companies significantly improve their supply chain operations. The platform is dedicated to enhancing connections by fostering networks that unite shippers, logistics service providers (LSPs), technology providers, and investors. Furthermore, it aims to empower commerce by delivering insightful information that enables businesses to refine and automate their trade processes. Crucially, Better Supply Chains also seeks to improve lives by examining the positive impacts of logistics technology (LogTech) on labor, talent development, and sustainability efforts within the industry. By actively highlighting how emerging technologies can be seamlessly integrated into supply chain organizations, processes, and people, the website strives to make supply chains more efficient, inclusive, and equitable, ultimately contributing to better operational outcomes and improved individual well-being. Key Takeaways: CHAINge, AI, and the Future of Freight In “CHAINge, AI, and the Future of Freight”, Joe Lynch and Bart De Muynck, an industry expert and thought leader with over 30 years of supply chain and logistics experience across the globe, discuss how artificial intelligence and emerging technologies are reshaping the logistics landscape and what the future holds for global freight. AI as an Enabler, Not a Replacer: A central theme is that AI's true power in supply chains lies in augmenting human intelligence rather than replacing it. Bart emphasizes that AI can automate mundane tasks, freeing up human workers for higher-value activities requiring critical thinking, problem-solving, and creativity, leading to increased productivity and improved decision-making. Focus on Practical AI Applications: The podcast will likely highlight that the effective implementation and absorption of existing technologies, particularly AI and advanced analytics, are the next big transformation in logistics. Bart's focus through "Better Supply Chains" is on practical, high-quality content that helps companies refine and automate their trade processes using proven technological solutions, not just speculative future tech. The Interconnectedness of Supply Chain Elements: Bart's extensive background and the mission of "Better Supply Chains" underscore the importance of fostering networks that unite shippers, logistics service providers (LSPs), technology providers, and investors. The "CHAINge" conference he chairs also emphasizes collaboration and interconnectivity to build more efficient and sustainable supply chains. Addressing Key Industry Challenges through Technology: The episode will touch upon how logistics technology (LogTech) can positively impact labor, talent development, and sustainability. Bart's work aims to integrate emerging technologies into supply chain organizations, processes, and people to create more efficient, inclusive, and equitable supply chains. Data Quality is Paramount for AI Success: Bart stresses that while data is the "unsung hero" in supply chain resilience and decision-making, the quality of that data is crucial. Bad or irrelevant data can be a "bad actor," highlighting the need for high-quality, real-time, and predictive insights to get ahead of disruptions. Building Resilient and Agile Supply Chains: Given Bart's expertise and the current landscape, the discussion will likely emphasize the need for supply chains to be agile and resilient in the face of geopolitical factors, trade disputes, and market uncertainties. Technology, including AI, plays a vital role in enhancing visibility, optimizing logistics, and improving decision-making in turbulent times. Investing in Talent Development alongside Technology: Bart believes that talent is a significant constraint in supply chains. The podcast will likely highlight the importance of not only embracing technology but also investing in talent development and fostering a culture of collaboration to fully leverage the transformative potential of AI and other innovations. Learn More About CHAINge, AI, and the Future of Freight Bart | LinkedIn Better Supply Chains | Linkedin Better Supply Chains CHAINge conference The Connective Tissue of the Supply Chain with Bart A. De Muynck The Logistics of Logistics Podcast If you enjoy the podcast, please leave a positive review, subscribe, and share it with your friends and colleagues. The Logistics of Logistics Podcast: Google, Apple, Castbox, Spotify, Stitcher, PlayerFM, Tunein, Podbean, Owltail, Libsyn, Overcast Check out The Logistics of Logistics on Youtube

What's up everyone, today we have the pleasure of sitting down with Tiankai Feng, Data & AI Strategy Director at Thoughtworks and Author of Humanizing Data Strategy. (00:00) - Intro (01:06) - In This Episode (03:18) - How Data and Marketing Create a Symbiotic Relationship (06:00) - If Data Governance Is the Jedi Council, Marketing Ops Is the Rebel Alliance (08:26) - How to Organize Data Teams and Improve Marketing Collaboration (14:49) - Handling Healthy Data Conflicts Without Crushing Creativity (25:23) - How to Use Shadowing to Fix Broken Marketing Alignment (36:44) - The Comeback of Data Quality (43:20) - How Natural Language BI Tools Change Data Analyst Work (46:50) - How Composable Data Management Works in Marketing (53:30) - How to Use Authentic Communication to Build Influence in Marketing Ops (56:40) - Happiness Summary: Data governance feels like the Jedi Council, steady with its rules, while marketing ops moves like the Rebel Alliance, quick to adapt when perfect data never arrives. Tiankai believes progress comes from blending discipline with curiosity, bringing data in early as a partner, not a critic. He's seen teams thrive when they pick trade-offs upfront, document how everyone fits together, and take ownership of clean, reliable inputs instead of trusting AI to fix sloppy work later. Even the best tools still need humans to design the logic behind the scenes. When teams care about context and build real relationships, data becomes the backbone that keeps marketing strong under pressure.About TiankaiTiankai Feng is Director of Data & AI Strategy at Thoughtworks, where he leads global service offerings spanning data governance, AI strategy, and modernization initiatives. He is the author of Humanizing Data Strategy – Leading Data with the Head and the Heart, and serves on the Education Advisory Board at DataQG. Previously, Tiankai spent over six years at Adidas as Senior Director of Product Data Governance, shaping data practices across global teams. He is also Head of Marketing at DAMA Germany, helping grow the country's leading data management community. Earlier in his career, Tiankai worked as a senior consultant with TD Reply, advising major brands on digital strategy and performance. Recognized as a top data product thought leader, he is passionate about bridging the gap between technical excellence and human-centered data cultures.How Data and Marketing Create a Symbiotic RelationshipIt is interesting to consider how many data professionals started their careers by obsessing over why advertising can make people feel something. Tiankai shared that he studied campaigns as a kid and felt driven to decode the hidden mechanics behind each message. He called it the science behind the feeling. He wanted to understand why a phrase could trigger a decision and what evidence proved it actually worked.When he chose his degree, he blended marketing with database systems because he believed data could ground creative work in reality. He wanted a way to measure the effectiveness of ideas instead of relying on gut reactions. That decision led him into marketing analytics, where he learned to balance instinct with structured evidence. He described this period as the moment he first saw every click, conversion, and impression as a trail of signals pointing to what people valued most.Tiankai shared that many companies separate marketing from data in ways that weaken both. He believes that every creative idea grows stronger when it gets tested by proof. He said, “You have a lot of thoughts and gut feelings, but what if you could actually rely on proof to make better decisions?” He still asks this question whenever he evaluates a strategy or decides how to communicate the value of a data project.He also applies marketing principles inside his own teams. He treats internal projects like product launches and focuses on storytelling as much as reporting. He learned that evidence alone rarely convinces stakeholders. People respond when data feels relevant and easy to act on. He credits this mindset to his early work in brand campaigns, which taught him that information becomes meaningful when it connects to someone's goals and emotions.“By heart, I'm still a marketer,” he said. “Even now, I'm applying what I learned in marketing to convince stakeholders to work with me.”This blend of skills helps teams create strategies that people believe in and understand. When marketing and data share the same goals, campaigns feel both credible and inspiring.Key takeaway: Blending marketing analytics with creative thinking lets you challenge assumptions and build strategies that people trust. When you share data work, present it like a product launch. Frame the message in relatable stories, make the numbers clear, and show how the information supports better decisions. That way you can help teams act with confidence and prove the impact of their ideas.If Data Governance Is the Jedi Council, Marketing Ops Is the Rebel AllianceIt is interesting to consider how marketing teams keep borrowing Star Wars metaphors to make sense of the work. Tiankai described clean, governed data as the Jedi Council, the calm authority that brings order and discipline. He shared that marketing operations always felt more like the Rebel Alliance, a team of underdogs improvising bold plans and building strategies out of whatever they could find in the hangar.In those early years, nobody had a clear guidebook. Teams cobbled together workflows, tested ideas with half-finished data, and celebrated any dashboard that did not explode during a quarterly review. Tiankai remembered feeling like every small win was a victory against the Empire of bad processes. This scrappy environment fueled creativity, but it also came with plenty of late nights and occasional panic.Today, marketing ops feels more settled. > “There's more experience and more best practices to be shared,” he said. Teams now have detailed frameworks, polished documentation, and tools that mostly work the way they promise. That way you can spend less time guessing and more time refining campaigns that drive results. You can treat the Jedi Council as a helpful ally rather than an unreachable ideal.Tiankai still believes good operators keep a bit of rebel spirit. Even the best-governed data will sometimes contradict reality on the ground. When those moments happen, it helps to trust your instincts and build something that makes sense for your business, not just the standard playbook. The Jedi Council can provide discipline, but someone still has to step into the hangar and fly the mission.Marketing operations has grown up, but it never lost the urge to experiment. The work feels rewarding when you blend clear frameworks with your own curiosity and a willingness to bend the rules when the stakes demand it.Key takeaway: Data governance acts like a steady Jedi Council, giving your marketing operations clarity, trust, and a strong backbone. To get the most from it, combine those proven systems with the resourcefulness of a rebel team. Stay ready to challenge assumptions, tweak the plan, and follow your judgment when data alone does not tell the full story. That way you can build workflows that are disciplined enough to scale and flexible enough to handle reality without falling apart.How to Organize Data Teams and Improve Marketing CollaborationIt is interesting to consider how data ownership used to feel like an afterthought in early SaaS companies. Tiankai remembered scraping together metrics by hand, jumping between marketing dashboards and...

Are healthcare providers' contact details as accurate as you think? In this podcast hosted by Chenny Solaiyappan, Curatus Chief Revenue Officer Jarrod Mandozzi speaks on the critical challenge of provider data management in healthcare. Jarrod shares insights from his extensive experience in Medicare and Medicaid programs, revealing how inaccurate provider information impacts patient care, health plan operations, and industry efficiency.

Today, we're joined Marne Martin, the CEO of Emburse whose innovative travel and expense solutions power forward-thinking organizations. We talk about:Building fast-moving & scalable businesses that can lastHow to finance and grow profitable companies to reach an exitThe challenges of finding a competitive edge as GenAI accelerates innovationTesting monetizing AI alongside conventional SaaS monetization

Bonnie Tinder is the founder and CEO of Raven Intelligence, an independent B2B peer review site that amplifies the voice of the customer. She focuses on software customers, consulting partners, and software vendors and helps identify the best partners for their needs. In this episode, Bonnie shares insights from a recent Salesforce event, exploring how AI agents, data clouds, and robotics are reshaping customer experience, software implementation, and enterprise transformation.Episode 52 | AI Agents in ActionThe Big Themes:Campaigns Are Out, Conversations Are In: Marketing is undergoing a radical transformation. Gone are the days of mass email blasts and no-reply addresses. Instead, AI is ushering in a new era of real-time, personalized engagement. Salesforce is leaning into this shift with tools that replace one-way campaigns with dynamic conversations. AI agents now tailor interactions based on behavior, preferences, and real-time context, fostering true customer intimacy at scale.Unified Data Is the Bedrock of Smart AI: No AI strategy can succeed without clean, connected data. Salesforce's Data Cloud addresses what SAP calls the “swivel chair problem” — when teams toggle between disconnected systems to piece together a customer story. AI agents can't operate effectively if data is fragmented or siloed. That's why Salesforce is investing in tools that unify sales, marketing, support, and financial data, giving AI a full-picture view of the customer journey.AI Agents Are Already Delivering Real Results: AI isn't theoretical anymore — it's working in the wild. Bonnie pointed out two standout cases: University of Chicago Medicine and Ford Pro. In healthcare, Agentforce transformed an outdated, frustrating appointment system into a streamlined digital process, improving both efficiency and patient experience. At Ford, AI agents guide customers to ideal vehicle matches with minimal input, keeping users on-site and increasing conversion.The Big Quote: “I think that buyers are looking more at the execution and fit of software, as opposed to the software brand itself. And I would say that that is a shift in the last year or so, especially now with the advent of AI and just the rapid pace that everything is moving so, less on brand, more about how are you going to offer me the complete solution and break down silos of data?” More from Bonnie Tinder:Connect with Bonnie on LinkedIn or send a message via her Acceleration Economy Analyst page. Visit Cloud Wars for more.

In this episode of Alter Everything, we chat with Alex Patrushev, Head of Product at Nebius. We discuss the gaps organizations face between data and business impact, strategies to bridge these gaps, and the role of AI in these processes. Alex explains Nebius' mission to make AI accessible, the challenges of building data centers and software from scratch, and innovative solutions like their data center in Finland. The conversation also covers key components for effectively bridging data and business impact, such as project selection, stakeholder communication, team skills, data quality, and tech stack.Panelists: Alexander Patrushev, Head of Product for AI/ML @ NebiusMegan Bowers, Sr. Content Manager @ Alteryx - @MeganBowers, LinkedInShow notes: NebiusData Version Control Interested in sharing your feedback with the Alter Everything team? Take our feedback survey here!This episode was produced by Megan Bowers, Mike Cusic, and Matt Rotundo. Special thanks to Andy Uttley for the theme music.

In this discussion, Cristina Flaschen, CEO of Pandium, speaks with Kayleen Standridge, Senior Technical Product Manager at Stellar Cyber, about the unique challenges of building integrations within cybersecurity platforms.Kayleen's Background and Approach to IntegrationsKayleen shares her journey from technical support and professional services roles at companies like ClickUp and Podium to leading integration strategy at Stellar Cyber. She highlights how her early customer-facing experience shaped her approach to solving complex integration problems that deliver real value to users.Integration Challenges in CybersecurityStellar Cyber's focus is on unifying security data from a wide array of systems, enabling faster threat detection and response. Kayleen explains that one of the biggest hurdles is accessing highly varied and often locked-down customer environments—many of which rely on legacy or on-premise systems. To address this, her team has developed a streamlined intake and testing process, often simulating data based on API documentation when direct access isn't feasible. This ensures smoother development and troubleshooting, even when working with restricted or inconsistent data sources.Data Quality, Compliance, and SecurityData variability is a constant challenge, especially when logs and records are generated or entered in different formats. Kayleen describes how Stellar Cyber uses log parsers and regex-based solutions to handle these inconsistencies. She also emphasizes the heightened compliance requirements in cybersecurity, such as strict credential management and the need to support on-premise deployments for customers concerned with data sovereignty.Learning Curve and Industry NuancesTransitioning into cybersecurity brought a steep learning curve, particularly with industry-specific terminology and compliance standards. Kayleen notes that company-provided certifications and hands-on experience were key to ramping up quickly. While some integration challenges are unique to security, many - like supporting legacy systems - are common across SaaS industries.The Future of Integrations: Democratization and AILooking ahead, Kayleen sees integrations becoming increasingly democratized and customer-driven, with more plug-and-play options and integration platforms empowering non-technical users. She points to the rise of AI-driven orchestration tools, such as BlinkOps, that enable users to build workflows and integrations with minimal technical knowledge. This shift raises new questions around governance and permissioning, as it becomes easier for non-engineers to connect sensitive systems.Product Strategy and PartnershipsKayleen outlines how Stellar Cyber balances customer-driven integration requests with strategic initiatives, such as building bi-directional integrations with cornerstone platforms like ServiceNow. Partnerships play a crucial role in driving mutual value and go-to-market opportunities, especially with large ecosystem players.Advice for Aspiring Integration PMsKayleen encourages aspiring integration product managers to stay curious, focus on customer problems, and prioritize simplicity in integration design—even if it means taking on more complexity internally. She stresses that the most impactful integrations are those that are easy for customers to deploy and manage, ultimately solving real-world challenges.

Bridging the Gap Between AI and Business Data // MLOps Podcast #325 with Deepti Srivastava, Founder and CEO at Snow Leopard.Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletter// AbstractI'm sure the MLOps community is probably aware – it's tough to make AI work in enterprises for many reasons, from data silos, data privacy and security concerns, to going from POCs to production applications. But one of the biggest challenges facing businesses today, that I particularly care about, is how to unlock the true potential of AI by leveraging a company's operational business data. At Snow Leopard, we aim to bridge the gap between AI systems and critical business data that is locked away in databases, data warehouses, and other API-based systems, so enterprises can use live business data from any data source – whether it's database, warehouse, or APIs – in real time and on demand, natively. In this interview, I'd like to cover Snow Leopard's intelligent data retrieval approach that can leverage business data directly and on-demand to make AI work.// BioDeepti is the founder and CEO of Snow Leopard AI, a platform that helps teams build AI apps using their live business data, on-demand. She has nearly 2 decades of experience in data platforms and infrastructure.As Head of Product at Observable, Deepti led the 0→1 product and GTM strategy in the crowded data analytics market. Before that, Deepti was the founding PM for Google Spanner, growing it to thousands of internal customers (Ads, PlayStore, Gmail, etc.), before launching it externally as a seminal cloud database service. Deepti started her career as a distributed systems engineer in the RAC database kernel at Oracle.// Related LinksWebsite: https://www.snowleopard.ai/AI SQL Data Analyst // Donné Stevenson - https://youtu.be/hwgoNmyCGhQ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Deepti on LinkedIn: /thedeepti/Timestamps:[00:00] Deepti's preferred coffee[00:49] MLflow vs Kubeflow Debate[04:58] GenAI Data Integration Challenges[09:02] GenAI Sidecar Spicy Takes[14:07] Troubleshooting LLM Hallucinations[19:03] AI Overengineering and Hype[25:06] Self-Serve Analytics Governance[33:29] Dashboards vs Data Quality[37:06] Agent Database Context Control[43:00] LLM as Orchestrator[47:34] Tool Call Ownership Clarification[51:45] MCP Server Challenges[56:52] Wrap up

Cribl's Field CISO Ed Bailey discusses how customers can manage the quality and quantity of data by providing intelligent controls between data sources and destinations.Topics Include:Cribl company name originCompany helps organizations screen data to find valuable insightsEd Bailey was Cribl's first customer back in 2018Data growth of 25% yearly created seven-figure cost increasesCEOs and CIOs complained about explosive data storage costsUsers demanded more data while budgets remained constrainedBailey discovered Cribl through a random Facebook advertisementCribl Stream sits between data sources and destinationsNo new agents required, uses existing infrastructure connectionsReduced data growth from 28% to 8% within yearDevelopment cycles shortened from six weeks to two weeksBailey managed global security and telemetry data systemsOperated large Splunk instance across forty different countriesTeam spent time collecting data instead of extracting valueCribl provided consistent data control plane for operationsSmart engineers could focus on machine learning solutionsMigrated from terrible SIEM to better security platformData strategy should focus on business requirements firstNot all data has the same business valueTier one: Critical data goes to expensive platformsTier two: Important data stored in cheaper lakesTier three: Compliance data in low-cost object storageSIEM costs around one dollar per gigabyte storedData lakes cost twelve to eighteen cents per gigabyteObject storage costs fractions of pennies per gigabyteAWS partnership provides scalable infrastructure for rapid growthEC2, EKS, and S3 are heavily utilized servicesCribl Search finds data directly in object storageAvoids costly data movement for search and analysisParticipants:Edward Bailey – Field CISO, CriblSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Send us a textFrom 2020-2023, DECAL received $2B+ in federal relief funds, fueling initiatives for Georgia's child care providers, workforce & families. Dive into the impact w/ insights from DECAL & Child Trends. Joining us to talk about COVID-19 relief funding and to help us follow the money is an impressive panel of guests: Shayna Funke, DECAL Director of Research Partnerships and Business Supports; Rob O'Callaghan, DECAL Director of Institutional Research and Data Quality; and from Child Trends, Dr. Dale Richards, Research Scholar and Dr. Rachel Abenavoli, Research Scientist.  Support the show

What You're Going to Learn in This Video:In this episode, we reveal the top-performing Meta Ads strategies for eCommerce in 2025. If you're running an online store or managing ads for eCommerce brands, these are the tactics you can't afford to miss.

Physician executive Jay Anders discusses his article, "Health care's data problem: the real obstacle to AI success." Jay asserts that the transformative potential of artificial intelligence in health care is fundamentally dependent on the quality of the underlying clinical data. He explains that while tools like large language models and conversational AI show promise in synthesizing information and easing documentation, their reliability is compromised when fed with data from repositories often filled with inconsistencies, errors, and gaps. This can lead to an "increased workload paradox," where clinicians spend more time verifying and correcting AI-generated outputs, and a failure to produce the structured data vital for regulatory compliance, quality metrics, and analytics. Jay emphasizes that the "garbage in, garbage out" principle severely hampers interoperability and contributes to significant financial and clinical risks, including medical errors and inefficient workflows. To counter this, he advocates for robust data validation and normalization, enhancement of clinical terminologies, and the use of AI paired with evidence-based algorithms to rectify historical data issues, stressing that establishing trusted data sources is paramount before AI can truly revolutionize health care delivery. Our presenting sponsor is Microsoft Dragon Copilot. Want to streamline your clinical documentation and take advantage of customizations that put you in control? What about the ability to surface information right at the point of care or automate tasks with just a click? Now, you can. Microsoft Dragon Copilot, your AI assistant for clinical workflow, is transforming how clinicians work. Offering an extensible AI workspace and a single, integrated platform, Dragon Copilot can help you unlock new levels of efficiency. Plus, it's backed by a proven track record and decades of clinical expertise and it's part of Microsoft Cloud for Healthcare–and it's built on a foundation of trust. Ease your administrative burdens and stay focused on what matters most with Dragon Copilot, your AI assistant for clinical workflow. VISIT SPONSOR → https://aka.ms/kevinmd SUBSCRIBE TO THE PODCAST → https://www.kevinmd.com/podcast RECOMMENDED BY KEVINMD → https://www.kevinmd.com/recommended

In this episode of Alter Everything, we chat with Eric Soden and JT Morris from Alteryx partner Capitalize about the practical applications and limitations of generative AI. They discuss ideal use cases for large language models, the importance of balancing generative AI with traditional analytics techniques, and strategies for scaling AI capabilities in enterprise environments. Eric and JT also share real-world examples and insights into achieving productivity gains and ROI with generative AI, along with the importance of maintaining data quality and explicability in AI processes.Panelists: JT Morris, Senior Manager, Advanced Analytics Practice Lead @ Capitalize, @JTMorris, LinkedInEric Soden, Co-founder and Managing Partner @ Capitalize, @esoden, LinkedInMegan Bowers, Sr. Content Manager @ Alteryx - @MeganBowers, LinkedInShow notes: Capitalize AnalyticsAlteryx Partners - Solution ProvidersEric's LinkedIn posts on Gen AI + AlteryxCapitalize Webinar: Alteryx +GenAI: 5 Real-World Use Cases Explained Interested in sharing your feedback with the Alter Everything team? Take our feedback survey here!This episode was produced by Megan Bowers, Mike Cusic, and Matt Rotundo. Special thanks to Andy Uttley for the theme music and Mike Cusic for the for our album artwork.

In this episode of the Don't Panic, It's Just Data podcast, Kevin Petrie, VP of Research at BARC, is joined by Nidhi Ram, Vice President of Global Services Strategy and Operational Excellence at Precisely. The duo explore the idea of focusing on data modernisation and improving accessibility rather than constantly implementing new technologies.Both Petrie and Ram highlight the importance of traditional mainframes, especially in modern data strategies. They delve into how companies can combine cloud tools with data in order to handle diverse data systems. This removes the need for replacing the mainframe, keeping the data accessible for all users.Going into the critical role of data quality and governance — especially in the age of AI — Ram emphasises that “garbage in, garbage out” has never been more relevant, as AI outputs are only as good as the data feeding the model.What's needed is a more comprehensive approach to data integration, quality, governance, and enrichment that helps ensure data is always ready for confident business decisions.Listen to this latest episode to learn how Precisely's Data Integrity Suite provides a comprehensive approach to data modernisation.TakeawaysData modernisation is about accessibility, not technology.The mainframe continues to play a crucial role in data strategies.High-quality data is essential for successful AI initiatives.Data governance is critical to comply with regulations and ensure data quality.Cloud solutions offer flexibility, but on-premise systems provide control.Companies need to adapt to a heterogeneous data environment.Integrating people and processes is key to successful data programs.Future data roles will require broad functional knowledge rather than deep technical skills.Chapters00:00 Introduction to Data Modernization02:56 Understanding Data Modernization06:01 Challenges in Data Modernization08:53 The Role of AI in Data Strategies11:56 Data Quality and Governance15:08 Cloud vs On-Premise Data Solutions18:14 Adapting to Diverse Data Environments20:47 Advice for Modernizing Data Strategies23:58 The Importance of People and Process27:07 Future Skills for Data TeamsAbout PreciselyPrecisely is a leading global data integrity provider, ensuring organisations have accurate, consistent, and contextual data. In today's data-driven world, where businesses rely on information for critical decisions, data integrity is crucial. Precisely offers a comprehensive portfolio of solutions designed to transform raw data into a reliable asset.Trusted by over 12,000 organisations in more than 100 countries, Precisely plays a critical role in helping businesses effectively leverage their data. By providing reliable software and strategic services, Precisely empowers organisations to confidently embark on their AI, automation, and analytics initiatives. High-quality, trustworthy data is the bedrock for successful AI models, efficient automation processes, and insightful analytics. Without it, these initiatives risk delivering inaccurate results and misleading insights. Precisely's commitment to data integrity allows businesses to make confident decisions and achieve strategic objectives.

In this episode of Identity at the Center, hosts Jeff Steadman and Jim McDonald are joined by Jerome Thorstenson, IAM Architect with Salling Group, live from EIC 2025 in Berlin! Jerome shares his insights on B2B identity, the challenges of managing access for a complex supply chain, and the importance of an identity-first approach.Discover how Salling Group, operating major labels like Target and Starbucks, handles identity for thousands of employees and external partners. Jerome dives into the complexities of balancing security, user experience, and the practicalities of implementing IGA and ABAC.From navigating the challenges of data quality and high employee turnover to the nuances of transitioning between IGA systems, this episode offers valuable insights for identity practitioners.Chapter Timestamps:00:00:00 - B2B Identity Challenges00:02:14 - Welcome to Identity at the Center from EIC 202500:04:14 - Jerome's Journey into Identity00:05:19 - Salling Group Overview00:06:57 - Securing B2B - Jerome's Presentation00:10:54 - Controlling Access in B2B00:11:41 - Identity as a Product00:14:51 - The Role of the IAM Practitioner00:16:31 - ABAC as a Game Changer00:21:00 - Language Considerations in a European Context00:22:33 - Employee Turnover Challenges00:25:07 - IGA Implementation Insights00:29:28 - Identity Fabric Discussion00:31:21 - Jerome's Caribbean Background00:34:06 - Wrap-up and Contact InformationConnect with Jerome: https://www.linkedin.com/in/jetdk/Connect with us on LinkedIn:Jim McDonald: https://www.linkedin.com/in/jimmcdonaldpmp/Jeff Steadman: https://www.linkedin.com/in/jeffsteadman/Visit the show on the web at http://idacpodcast.comKeywords:IDAC, Identity at the Center, Jeff Steadman, Jim McDonald, EIC 2025, B2B Identity, Identity First Security, IAM, Identity and Access Management, Supply Chain Security, IGA, ABAC, Attribute-Based Access Control, Role-Based Access Control, Identity Fabric, Digital Identity, Cybersecurity, Data Quality, Employee Turnover, Caribbean

Today, we're joined by Ted Elliott, Chief Executive Officer of Copado, the leader in AI-powered DevOps for business applications. We talk about:Impacts of AI agents over the next 5 yearsTed's AI-generated Dr. Seuss book based on walks with his dogThe power of small data with AI, despite many believing more data is the answerThe challenge of being disciplined to enter only good dataGaming out SaaS company ideas with AI, such as a virtual venture capitalist

Peter Seeberg talks to Thomas Dhollander, Co-founder & CPO at Timeseer.AI about Trusted IIoT Data as the Key to Proactive Operations.

Episode SummaryThis episode dives into the critical role of data quality in unlocking the true potential of AI for ABM success. Daryn Smith shares how organizations can move from AI hype to AI readiness, focusing on bridging data silos and using AI effectively. The discussion explores practical steps to improving data management, leveraging AI tools to streamline processes, and why leadership plays a pivotal role in driving change. Daryn also reveals fascinating use cases where AI has made a tangible difference in business operations and marketing strategies.Key TakeawaysAI Readiness is LaggingOnly 8.5% of companies are truly AI-ready, despite executives highly prioritizing AI adoption.Data Quality is Key:70% of respondents prioritize data quality over AI, revealing that unstructured or siloed data hampers AI effectiveness.Leadership's Role in AI SuccessLeaders must champion AI adoption while being transparent about the need for continuous improvements in data management and AI training.Blind Automation Can Undermine ResultsAI often requires contextual data and meaningful human oversight to produce relevant, impactful recommendations.Practical AI ApplicationsFrom creating AI-driven RFP agents to developing AI tools that emulate senior employees' knowledge, simple yet impactful use cases can revolutionize workflows.Best Moments (01:10) – Daryn's Career Journey : From web developer to CEO, Daryn details how his unique background has shaped his approach to marketing and AI-led transformation.(04:53) – The Growing Importance of AI Readiness : Daryn highlights how data silos and poor data quality hinder AI's potential and shares insights from Hubble Digital's recent research.(10:57) – AI's Role in Revolutionizing ABM : Daryn and Paul discuss how AI-powered tools are automating manual tasks, leading to better account targeting and personalized campaigns.(14:00) – The Disconnect Between Leadership and Reality : A candid discussion on why executives often overestimate their organizations' AI capabilities.(34:06) – AI in Practical Use Cases : Daryn shares how Hubble Digital uses AI agents to streamline RFP responses and retain institutional knowledge.(22:27) – Evolving Data Systems : Tips on keeping CRM and ABM systems agile to adapt to business changes, ensuring they retain their value long term.Tech RecommendationsHubSpot – A platform supporting ABM strategies and AI integration for CRM efficiency.Fathom.ai – A tool for turning unstructured sales and marketing data into actionable insights.Books:Hacking Marketing by Scott Brinker - Agile Practices to Make Marketing Smarter, Faster, and More InnovativeDharmesh Shah - Founder & CTO, HubSpotDave Gerhardt - Founder, Exit Five

In this episode of the Transform Sales Podcast: Sales Software Review Series, Dave Menjura ☁, Marketplace Specialist at CloudTask, interviews Saahil Dhaka, Co-Founder and CEO at Clientell, a customer engagement platform designed to help businesses manage client relationships and enhance communication. The platform targets sales and customer service teams looking to improve client satisfaction and retention through personalized outreach. Saahil shares how Clientell simplifies client communication by automating key processes, reducing admin work by up to 70%, and improving data hygiene. By streamlining workflows and enhancing visibility, Clientell allows teams to focus on more strategic tasks while improving overall operational efficiency. The platform's ability to centralize data and reduce the complexity of tech stacks makes it a powerful solution for businesses seeking to optimize customer engagement and reduce costs. Ideal for sales teams and customer service leaders struggling with manual processes and inefficient systems, Clientell transforms how companies interact with clients, providing a seamless experience that drives retention and growth. Try Clientell here: https://getcloudtask.com/clientell-55dc14 #TransformSales #SalesSoftware #Clientell #CloudTask

Were you in Washington, DC, last week for IIeX? If not, our latest episode of Intellect has you covered! Brian Peterson and Matthew Alexander were in the nation's capital for this year's event and sat down to recap all the trends and topics. Let's dive into what the guys discussed: Data Quality Takes Center Stage One of the most talked-about issues this year was data quality, spurred on by the recent industry indictment. Sessions addressing fraud detection, transparent sampling practices, and respondent experience were standing room only. It's clear that researchers are demanding more transparency and layered approaches to improving data quality, from better survey design, better tools, and even improved respondent experience. AI and Synthetic Data: More Practical Applications and Progress AI continued to dominate the conversation, but the tone has shifted. This year, discussions focused on practical applications of how AI can assist in the research process, with a variety of presenters showing how they had integrated AI into their tools. Synthetic data was also a large topic of conversation, with several presenters showing their latest strides in synthetic data and how they are validating it. Brian and Matthew both agreed that the applications are gaining traction in qualitative research, but based on the presentations, were still cautious about its readiness for quantitative applications. What Worked (and What Didn't) From a Format Perspective Both Brian and Matthew felt Washington, D.C. was a fantastic host city—clean, easy to navigate, and close to the airport. They were also happy that the Greenbook team abandoned the headphone concept that was used in Austin. The only drawback they found was that some sessions suffered from mismatched room sizes, with popular topics overflowing beyond capacity. They speculated that because of the indictment news that broke just a couple of weeks prior, there was increased interest in some topics. Give it a listen and let us know what you think. And hey, if IIeX comes to Cincinnati next year like Brian and Matthew suggested, we'll see you there! Did you miss one of our webinars or want to get some of our whitepapers and reports? You can find it all on our Resources page on our website here. Learn more about your ad choices. Visit megaphone.fm/adchoices

A modern digital healthcare economy is impossible without a robust provider directory, which serves as the foundation for interoperability and crucial processes.  In this episode, Erin Weber, Chief Policy and Research Officer, discusses how CAQH supports provider directories, emphasizing the need for data accuracy and standardization through initiatives like universal group roster templates. She highlights the importance of interoperability and maintaining accurate data to ensure seamless care delivery and billing. Don Rucker, Chief Strategy Officer, talks about modern FHIR APIs and interoperability. He uses the analogy of domain name services on the internet and stresses the need for “computable interoperability” where data can be used in real time to improve care. They explain how the 21st Century Cures Act has impacted healthcare and how legacy systems need to be modernized. Don and Erin stress that this work is crucial for modern healthcare to evolve and deliver improved patient experiences.  Tune in and learn how these key changes are shaping the future of healthcare! Resources: Connect with and follow Erin Weber on LinkedIn. Follow CAQH on LinkedIn and visit their website. Connect with and follow Don Rucker on LinkedIn. Learn more about 1upHealth on their LinkedIn and website. Check out the latest annual CAQH Index Report here.

What if AI could be the key to solving the concrete industry's biggest problem—labor shortages? In this episode of the Concrete Logic Podcast, Seth Tandett sits down with Ramy Sedra, CEO of C60, to discuss how AI is transforming construction. From boosting productivity to making smarter business decisions, they break down how AI can help, but also why it's not a one-size-fits-all solution. Tune in to find out how data, technology, and strategy come together to reshape the future of concrete. Don't miss this essential conversation for anyone looking to stay ahead in the industry! What You'll Discover in This Episode:

Who said data was just an invisible string of numbers? On this episode of Bringing Data and AI to Life our host and GVP of Solution Specialist Sales at Informatica, Amy Horowitz, is joined by Cathy Hackl, CEO and Founder of Future Dynamics. Cathy is a globally recognized futurist, an expert in deep tech and a speaker on AI. This conversation touches on the definition of data itself, and how it goes beyond what you can calculate in a spreadsheet. Cathy talks us through the challenges businesses face when incorporating AI into their practices, as well as the rise of using physical AI to visualise data, reminding us that the quality of your data is critical to the rate of your growth.

“AI is only as powerful as the data behind it. If you don't trust the inputs, you can't trust the outputs and that's where most companies get stuck. It's not enough to have automation or algorithms; you need quality, transparency, and alignment across your go-to-market motion. That's the difference between tech that looks smart and tech that actually drives revenue.” AI is everywhere but without clean data and strategic alignment, it's just noise. In this episode of Revenue Boost: A Marketing Podcast, titled, Smarter Tech, Sharper Targeting: Fueling Revenue with AI, Data Quality, and GTM Alignment, Demandbase CMO Kelly Hopping joins host Kerry Curran to unpack what it really takes to make AI work for B2B revenue growth. From smarter targeting to scaling with efficiency, Kelly shares how enterprise leaders can leverage AI-powered tools only when grounded in high-quality data and a clearly defined ICP. You'll learn why GTM alignment matters more than ever and how to avoid the pitfalls of disconnected tech stacks and generic automation. If you're building or optimizing your go-to-market engine, this episode is your roadmap to doing it smarter.

Randall Balestriero joins the show to discuss some counterintuitive findings in AI. He shares research showing that huge language models, even when started from scratch (randomly initialized) without massive pre-training, can learn specific tasks like sentiment analysis surprisingly well, train stably, and avoid severe overfitting, sometimes matching the performance of costly pre-trained models. This raises questions about when giant pre-training efforts are truly worth it.He also talks about how self-supervised learning (where models learn from data structure itself) and traditional supervised learning (using labeled data) are fundamentally similar, allowing researchers to apply decades of supervised learning theory to improve newer self-supervised methods.Finally, Randall touches on fairness in AI models used for Earth data (like climate prediction), revealing that these models can be biased, performing poorly in specific locations like islands or coastlines even if they seem accurate overall, which has important implications for policy decisions based on this data.SPONSOR MESSAGES:***Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich. Goto https://tufalabs.ai/***TRANSCRIPT + SHOWNOTES:https://www.dropbox.com/scl/fi/n7yev71nsjso71jyjz1fy/RANDALLNEURIPS.pdf?rlkey=0dn4injp1sc4ts8njwf3wfmxv&dl=0TOC:1. Model Training Efficiency and Scale [00:00:00] 1.1 Training Stability of Large Models on Small Datasets [00:04:09] 1.2 Pre-training vs Random Initialization Performance Comparison [00:07:58] 1.3 Task-Specific Models vs General LLMs Efficiency2. Learning Paradigms and Data Distribution [00:10:35] 2.1 Fair Language Model Paradox and Token Frequency Issues [00:12:02] 2.2 Pre-training vs Single-task Learning Spectrum [00:16:04] 2.3 Theoretical Equivalence of Supervised and Self-supervised Learning [00:19:40] 2.4 Self-Supervised Learning and Supervised Learning Relationships [00:21:25] 2.5 SSL Objectives and Heavy-tailed Data Distribution Challenges3. Geographic Representation in ML Systems [00:25:20] 3.1 Geographic Bias in Earth Data Models and Neural Representations [00:28:10] 3.2 Mathematical Limitations and Model Improvements [00:30:24] 3.3 Data Quality and Geographic Bias in ML DatasetsREFS:[00:01:40] Research on training large language models from scratch on small datasets, Randall Balestriero et al.https://openreview.net/forum?id=wYGBWOjq1Q[00:10:35] The Fair Language Model Paradox (2024), Andrea Pinto, Tomer Galanti, Randall Balestrierohttps://arxiv.org/abs/2410.11985[00:12:20] Muppet: Massive Multi-task Representations with Pre-Finetuning (2021), Armen Aghajanyan et al.https://arxiv.org/abs/2101.11038[00:14:30] Dissociating language and thought in large language models (2023), Kyle Mahowald et al.https://arxiv.org/abs/2301.06627[00:16:05] The Birth of Self-Supervised Learning: A Supervised Theory, Randall Balestriero et al.https://openreview.net/forum?id=NhYAjAAdQT[00:21:25] VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning, Adrien Bardes, Jean Ponce, Yann LeCunhttps://arxiv.org/abs/2105.04906[00:25:20] No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data (2025), Daniel Cai, Randall Balestriero, et al.https://arxiv.org/abs/2502.06831[00:33:45] Mark Ibrahim et al.'s work on geographic bias in computer vision datasets, Mark Ibrahimhttps://arxiv.org/pdf/2304.12210

This week on Alter Everything, we chat with Scott Jones and Treyson Marks from DCG Analytics about the history and misconceptions of AI, the importance of data quality, and how Alteryx can serve as a powerful tool for pre-processing AI data. Topics of this episode include the role of humans in auditing AI outputs and the critical need for curated data to ensure trustworthy results. Through real-world use cases, this episode explores how AI can significantly enhance analytics and decision-making processes in various industries.Panelists: Treyson Marks, Managing Partner @ DCG Analytiocs - LinkedInScott Jones, Principal analytics consultant @ DCG Analytics - LinkedInMegan Bowers, Sr. Content Manager @ Alteryx - @MeganBowers, LinkedInShow notes: DCG Analytics Interested in sharing your feedback with the Alter Everything team? Take our feedback survey here!This episode was produced by Megan Bowers, Mike Cusic, and Matt Rotundo. Special thanks to Andy Uttley for the theme music and Mike Cusic for the for our album artwork.

In this episode of Intellicast, host Brian Peterson is joined by Mary Draper and Aron Wilson for a timely and candid discussion about the state of data quality in market research. The team unpacks the recent indictment involving OP4G and Slice MR, accused of orchestrating a $10 million fraud scheme using fabricated survey data. They explore how this scandal highlights long-standing challenges around fraud detection, insider manipulation, and the limitations of current quality safeguards. The conversation also recaps key insights from the recent Insights Association Ignite Data Quality Conference, including the growing push for sample transparency, the nuanced role of ISO certifications, and the critical need for a respondent-first approach to survey design. With perspectives from both supplier and full-service backgrounds, this episode explores how the industry can work together to raise the bar for quality, combat fraud, and ultimately rebuild trust. Whether you're a panel provider, research buyer, or full-service agency, this episode is packed with insights you don't want to miss. Want a better understanding of EMI data quality processes? Click Here Heading to Washington D.C. for IIeX – be sure to connect with Matthew and Brian! Did you miss one of our webinars or want to get some of our whitepapers and reports? You can find it all on our Resources page on our website here. Learn more about your ad choices. Visit megaphone.fm/adchoices

"So you want trusted data, but you want it now? Building this trust really starts with transparency and collaboration. It's not just technology. It's about creating a single governed view of data that is consistent no matter who accesses it, " says Errol Rodericks, Director of Product Marketing at Denodo.In this episode of the 'Don't Panic, It's Just Data' podcast, Shawn Rogers, CEO at BARC US, speaks with Errol Rodericks from Denodo. They explore the crucial link between trusted data and successful AI initiatives. They discuss key factors such as data orchestration, governance, and cost management within complex cloud environments. We've all heard the horror stories – AI projects that fail spectacularly, delivering biased or inaccurate results. But what's the root cause of these failures? More often than not, it's a lack of focus on the data itself. Rodericks emphasises that "AI is only as good as the data it's trained on." This episode explores how organisations can avoid the "garbage in, garbage out" scenario by prioritising data quality, lineage, and responsible AI practices. Learn how to avoid AI failures and discover strategies for building an AI-ready data foundation that ensures trusted, reliable outcomes. Key topics include overcoming data bias, ETL processes, and improving data sharing practices.TakeawaysBad data leads to bad AI outputs.Trust in data is essential for effective AI.Organisations must prioritise data quality and orchestration.Transparency and collaboration are key to building trust in data.Compliance is a responsibility for the entire organisation, not just IT.Agility in accessing data is crucial for AI success.Chapters00:00 The Importance of Data Quality in AI02:57 Building Trust in Data Ecosystems06:11 Navigating Complex Data Landscapes09:11 Top-Down Pressure for AI Strategy11:49 Responsible AI and Data Governance15:08 Challenges in Personalisation and Compliance17:47 The Role of Speed in Data Utilisation20:47 Advice for CFOs on AI InvestmentsAbout DenodoDenodo is a leader in data management. The award-winning Denodo Platform is the leading logical data management platform for transforming data into trustworthy insights and outcomes for all data-related initiatives across the enterprise, including AI and self-service. Denodo's customers in all industries all over the world have delivered trusted AI-ready and business-ready data in a third of the time and with 10x better performance than with lakehouses and other mainstream data platforms alone.

Are companies starting to get concerned that AI isn't meeting expectations? Concern is a part of any new technology adoption curve, but let's explore some areas where expectations might not be meeting results. SHOW: 914SHOW TRANSCRIPT: The Cloudcast #914 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotwCHECK OUT OUR NEW PODCAST: "CLOUDCAST BASICS"SHOW SPONSORS:Try Postman AI Agent Builder TodayCut Enterprise IT Support Costs by 30-50% with US CloudSHOW NOTES:Why AI isn't meeting expectations (The Artificial Intelligence Enterprise)IS THREE YEARS ENOUGH TIME FOR ANY TECHNOLOGY TO TAKE OVER THE WORLD?Costs are still high, and positive ROI is still evolvingThe technology stack and standards are still evolvingEnterprise expectations are being confused with consumer expectationsAI predictions and timelines are overly aggressiveHow is anymore measuring AI success? The AI future is already here, it's just unevenly distributed“AI First” strategies are following “Cloud First” strategies - unevenly and distributedMost people don't like to talk about the augment vs. replace issueMiscellaneous stuff:ChatGPT is the fasting growing tech ever AGI will be here at any momentFor the first 18+ months, it was only an OpenAI + NVIDIA marketAll software development will be done by AIThere will be $1B companies with 1 personGenAI, Frontier Models, Open Source Models, Agents, etc.Deep Seek, MCP, etc. FEEDBACK?Email: show at the cloudcast dot netTwitter/X: @cloudcastpodBlueSky: @cloudcastpod.bsky.socialInstagram: @cloudcastpodTikTok: @cloudcastpod

The GeekNarrator memberships can be joined here: https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinMembership will get you access to member only videos, exclusive notes and monthly 1:1 with me. Here you can see all the member only videos: https://www.youtube.com/playlist?list=UUMO_mGuY4g0mggeUGM6V1osdA------------------------------------------------------------------------------------------------------------------------------------------------------------------About this episode: ------------------------------------------------------------------------------------------------------------------------------------------------------------------In this conversation, Jacopo and Ciro discuss their journey in building Bauplan, a platform designed to simplify data management and enhance developer experience. They explore the challenges faced in data bottlenecks, the integration of development and production environments, and the unique approach of Bauplan using serverless functions and Git-like versioning for data. The discussion also touches on scalability, handling large data workloads, and the critical aspects of reproducibility and compliance in data management. Chapters:00:00 Introduction03:00 The Data Bottleneck: Challenges in Data Management06:14 Bridging Development and Production: The Need for Integration09:06 Serverless Functions and Git for Data17:03 Developer Experience: Reducing Complexity in Data Management19:45 The Role of Functions in Data Pipelines: A New Paradigm23:40 Building Robust Data Solutions: Versioning and Parameters30:13 Optimizing Data Processing: Bauplan Runtime46:46 Understanding Control Planes and Data Management48:51 Ensuring Robustness in Data Pipelines52:38 Data Quality and Testing Mechanisms54:43 Branching and Collaboration in Data Development57:09 Scalability and Resource Management in Data Functions01:01:13 Handling Large Data Workloads and Use Cases01:09:05 Reproducibility and Compliance in Data Management01:16:46 Future Directions in Data Engineering and Use CasesLinks and References:Bauplan website:https://www.bauplanlabs.com

Guest: Viraj Narayanan, CEO of Cornerstone AI

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

A professor, an analytics director, and a podcaster walk into a bar and order a whiskey. Which brand do they order? And how does that data make it's way into a marketing mix model?That's what Simon and Jim wanted to know, so they asked Elea Feit - Associate Dean of Research and Professor of Marketing at Drexel, and Karen Chisholm, Director of Transformation Analytics at Pernod Ricard.Find out the biggest challenge marketers are facing today regarding measurement, and how they're tackling it. Find out what's in store for marketing measurement and MMM in the next 3 years.Grab a drink and have a listen :)▶️ Watch on YouTubeLinks from the show:Marketing Science InstituteThe Advertising Research FoundationElea Feit on LinkedIneleafeit.comKaren Chisholm (email about job opportunities!)00:47 Today's Topic: Marketing Mix Modeling02:10 Introducing the Guests05:07 MSI and ARF Initiative07:52 Survey Insights and Challenges12:20 Measurement Techniques and Strategies16:34 Brand-Level Optimization and Earned Media22:44 Granularity in Marketing Mix Modeling28:54 Understanding Marketing Mix Modeling29:18 The Four Ps and Their Importance30:20 Media Mix Modeling vs. Marketing Mix Modeling32:29 Challenges in Media and Marketing Mix Modeling34:09 Always-On Discounts and Their Impact37:14 Data Quality and Availability Issues40:38 The Future of Marketing Mix Modeling43:08 Industry Perspectives and Best Practices50:28 Open Source Solutions and In-House Modeling54:58 Job Opportunities and Final Thoughts

Last week, in my hotel room just after MicroConf, I got excited about repositioning. I have had some time to think about the steps forward since then, and here's what I've come up with. This week, I dive into what I have already done, what needs to be done next, and where this is going.The blog post: https://tbf.fm/episodes/383-repositioning-podscan-from-monitoring-to-data-platform The podcast episode: https://thebootstrappedfounder.com/repositioning-podscan-from-monitoring-to-data-platform/Check out Podscan, the Podcast database that transcribes every podcast episode out there minutes after it gets released: https://podscan.fmSend me a voicemail on Podline: https://podline.fm/arvidYou'll find my weekly article on my blog: https://thebootstrappedfounder.comPodcast: https://thebootstrappedfounder.com/podcastNewsletter: https://thebootstrappedfounder.com/newsletterMy book Zero to Sold: https://zerotosold.com/My book The Embedded Entrepreneur: https://embeddedentrepreneur.com/My course Find Your Following: https://findyourfollowing.comHere are a few tools I use. Using my affiliate links will support my work at no additional cost to you.- Notion (which I use to organize, write, coordinate, and archive my podcast + newsletter): https://affiliate.notion.so/465mv1536drx- Riverside.fm (that's what I recorded this episode with): https://riverside.fm/?via=arvid- TweetHunter (for speedy scheduling and writing Tweets): http://tweethunter.io/?via=arvid- HypeFury (for massive Twitter analytics and scheduling): https://hypefury.com/?via=arvid60- AudioPen (for taking voice notes and getting amazing summaries): https://audiopen.ai/?aff=PXErZ- Descript (for word-based video editing, subtitles, and clips): https://www.descript.com/?lmref=3cf39Q- ConvertKit (for email lists, newsletters, even finding sponsors): https://convertkit.com?lmref=bN9CZw

This article by Piotr Czarnas, founder of DQOps, outlines a proven, team-based approach to tackling persistent issues like invalid data, delayed reporting, and inconsistent formats. Published at: https://www.eckerson.com/articles/overcoming-the-challenge-of-low-data-quality

In this episode, Amir sits down with Santhosh Kumar, Head of Data at Trepp, to unpack the evolving world of Data as a Product. Data is no longer just a support function—it's becoming a core business driver. Santhosh shares how data teams are embracing a product-oriented approach, aligning closely with business goals while mirroring software engineering practices.If you've ever wondered:How data can be treated like a shippable productWhat mindset shifts data teams needAnd how collaboration between data, product, and tech teams is evolvingThis episode is for you!

2025 is finally upon us and the trends for healthcare marketers continue to evolve. EHR is quickly growing into an industry favorite for engaging HCPs in a contextually relevant location, brands and agencies are embracing Next Best Engagement (NBE) strategies to fine tune their media, and data quality remains an ever-important aspect of a marketer's toolkit. Join Louis Naimoli, who is the VP of programmatic at Haymarket in this candid conversation with MM+M.  Check us out at: mmm-online.com Follow us: YouTube: @MMM-onlineTikTok: @MMMnewsInstagram: @MMMnewsonlineTwitter/X: @MMMnewsLinkedIn: MM+M To read more of the most timely, balanced and original reporting in medical marketing, subscribe here.

Welcome back to Intellicast! Joining Brian Peterson on this jam-packed episode is Gabby Blados. They talk about conferences, data quality, as well as discuss some recent headlines from around the research world. Kicking off the episode, Brian and Gabby talk about the upcoming conferences, including SampleCon and Quirks Chicago. With Gabby attending Quirks, she gives Brian a preview of some of the topics, sessions, and activities she most looks forward to while in Chicago in early April. Next, Brian and Gabby turn their sights to the latest market research news, starting with the launch of the Global Data Quality Initiative updated Data Quality Pledge. Brian and Gabby discuss how they feel this is a step in the right direction to improve overall data quality. They are hesitant, though, since it is a pledge, and there is no one to hold people accountable to its standard other than self-regulation. They both agree that the pledge is probably a step toward some sort of regulation around data quality. In the second data quality story, they discuss the results and key takeaways from the new Data Quality Benchmarking Study released by the Insight Association. Brian and Gabby discuss some of the stats, including some that were somewhat surprising to both of them. You can get your free copy of the Insights Association Data Quality Benchmarking Report here. Next, Brian and Gabby talk about some of the recent headlines from around the market research industry, including ComScore's 2024 results, Glimpse's rebrand to Panoplai, and Disney shutting down FiveThirtyEight. In our final story, they touch on the reports about Kantar potentially looking to sell their Worldpanel/Numerator division. Thanks for listening! If you have headed to Pasadena for SampleCon, be sure to say hello to Kathleen Hock. If you will be in Chicago for Quirk, be sure to connect with Gabby or Abby Synder. Did you miss one of our webinars or want to get some of our whitepapers and reports? You can find it all on our Resources page on our website here. Learn more about your ad choices. Visit megaphone.fm/adchoices

Continuing the last Quality Matters episode, host Andy Reynolds and NCQA Chief Technology Officer, Ed Yurcisin, break down the complexities of the digital transformation in health care quality and explore the importance of high-quality data exchange, particularly in the context of HEDIS reporting and the FHIR interoperability standard. Ed explains how NCQA's work in digital HEDIS measurement not only improves health care quality reporting, but also lays the groundwork for broader industry advancements. By ensuring consistent, standardized data for digital HEDIS, NCQA is setting the stage for better measurement of public health, smoother prior authorization and general data accessibility.The conversation also explores the technical side of digital quality measurement, focusing on Clinical Quality Language (CQL) and the role of HEDIS “engines” in the health care data ecosystem. Ed clarifies the difference between SQL and CQL, and underscores that NCQA's focus is on measures' content, not on building the end-to-end software systems that run measures.Through collaborations like the Digital Quality Implementers Community, NCQA is working to ensure alignment across CQL platforms so everyone is “doing the same math.” Amol Vyas, NCQA Vice President for Interoperability, joins the conversation to explain how a public-private partnership is bringing choice and confidence to the market for CQL engines.Ed reflects on how his international perspective and personal experiences shape his passion for health care data interoperability. He shares how challenges accessing medical records for his family members underscore the need for a seamless, patient-centered health care system. His real-world perspective highlights why creating standardized, high-quality data isn't just a technical challenge, but a crucial factor in helping to ensure better, safer care for all.As the episode wraps, listeners are encouraged to explore NCQA's resources and upcoming events to stay informed on the future of digital quality. Key Quote:“ HEDIS measures are incorporated into government payment programs—for example, Medicare Star Ratings. There's incentive to enable digital HEDIS because it is tied to your CMS Star Ratings and the money a Medicare advantage plan might receive from the government. That's not the case for other important use cases, whether it be public health or prior authorization. So our infrastructure is tied to financial returns incenting organizations to make higher quality data accessible for digital HEDIS. And that means if it's good enough for digital HEDIS, it's been cleansed and analyzed in a way that could be used for public health, could be used for prior authorization—all of these different use cases.”Ed Yurcisin Time Stamps:(02:10) Clearing a Path for Data Quality(05:30) HEDIS “Engines” vs. HEDIS “Calculators”(07:17) Measures' Content vs. Software that Runs Measures(11:18) Digital Quality Implementers Community(19:35) The Need for Data Quality Cuts Close to Home Links:Bulk FHIR Quality Coalition Digital Quality Implementers CommunityNCQA Digital Hub Connect with Ed YurcisinConnect with Amol Vyas 

Send Everyday AI and Jordan a text messageYou might not think about the supply chain every day. But every product you use or service you rely on is 100% impacted by the global supply chain. And AI is completely reshaping how it works. Join us to find out how.Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Ask Jordan and Julian questions on AI and supply chainsUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:1. Role of Generative AI in Supply Chains2. Challenges in the Supply Chain Industry3. Automation and Robotics in Logistics4. Accessibility of AI Solutions5. Data Quality and ManagementTimestamps:00:00 Global Supply Chain Analytics Platform03:56 AI-Driven Procurement Insights09:57 Supply Chain Transparency and Challenges12:37 Meta & Apple Enter Robotics Race17:32 Data Classification Challenges in Industry21:22 Accessible AI: From Chatbots to Agents23:39 Navigating AI Disruption in Product Suites27:02 Data Management and Security EssentialsKeywords:Generative AI, global supply chain, artificial intelligence, machine learning, large language models, data extraction, ERP systems, data classification, supply chain analytics, predictive analytics, scenario planning, robotics, automation, ChatGPT, business impact, ESG compliance, supply chain insights, procurement officer, spend management, minority supplier spend, data quality, predictive insights, scenario analysis, enterprise resource planning, SAP, Oracle, Coders, generative AI applications, supply chain transformation, generative AI impact, technology disruption. Ready for ROI on GenAI? Go to youreverydayai.com/partner

Today, we're talking to Adam Dille from Quantum Metric and Zeba Hasan from Google. We discuss the importance of data quality for interfacing with AI, the most common mistakes we face when building products with AI, and how to get the rest of the company to buy into the advantages AI has to offer. All of this right here, right now, on the Modern CTO Podcast!  To learn more about Quantum Metric, check out their website here: https://www.quantummetric.com/ Produced by ProSeries Media: https://proseriesmedia.com/ For booking inquiries, email booking@proseriesmedia.co

How strong is your: Deliverability, Lead Sourcing, Copywriting, & Data Quality? Join us as Tal Baker-Phillips from Lemlist unpacks the secrets to successful prospecting that can elevate your sales game. Tal sheds light on the critical components of prospecting, such as the ever-evolving art of copywriting.  Each of these elements is vital, and ignoring even one can derail your efforts. Discover cutting-edge email prospecting strategies that prioritize quality and precision over sheer volume. We explore the power of strategic sending practices, such as inbox rotation, and the importance of A/B testing to maintain deliverability while expanding outreach.  You'll learn the significance of focusing on a single problem per email and how value-based calls-to-action can ignite genuine conversations with your prospects. This episode promises to redefine how you connect with your audience and optimize your email communication for maximum impact.  

00:00 Introduction00:37 Our Predictions for GTMOps in 202526:33 GTM or GTFO: Is AI email personalization worth it? 37:01 Q&A: When is data quality good enough?Hear more from us:Subscribe to us on Youtube: https://www.youtube.com/channel/UCN-x5u0G03LWmU0Ds_4zR8wSubscribe to our newsletter here: https://www.cs2marketing.com/revenue-growth-architects#subscribe-to-newsletterFollow Crissy on LinkedIn: https://www.linkedin.com/in/crveteresaunders/Follow Charlie on LinkedIn: https://www.linkedin.com/in/charliesaunders/Follow Xander on LinkedIn: https://www.linkedin.com/in/xanderbroeffle/

How Data Quality Fuels Data Usability Join radio host Jim Tate on this special episode from a recent virtual event with Clinical Architecture CEO Charlie Harp along with interoperability expert Didi Davis from The Sequoia Project as they discuss the challenges of interoperability and usability. If clinical data is the driving force for interoperability, then quality is the gas that fuels the data usability engine. Jim, Charlie, and Didi look at the key challenges around usability. To stream our Station live 24/7 visit www.HealthcareNOWRadio.com or ask your Smart Device to “….Play Healthcare NOW Radio”. Find all of our network podcasts on your favorite podcast platforms and be sure to subscribe and like us. Learn more at www.healthcarenowradio.com/listen