Podcasts about Small data

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

What do candy bars, couches, and car dealerships have in common? For Andrew Glaser, they're all opportunities to understand how real people make decisions — and why most product teams get those decisions wrong.   In this episode, Andrew shares his journey from hedge fund manager to product strategist, and now founder of Swizzle, an AI product built around Jobs to Be Done (JTBD) thinking. He opens up about how false positives, feature bloat, and over-reliance on personas lead teams down the wrong path — and what it really takes to make something customers will hire. We get into the guts of JTBD, from how to know when you've hit causality in an interview, to why understanding tradeoffs is more useful than knowing demographics. Andrew shares practical frameworks and surprising stories — including what Snickers can teach you about product-market fit, why most sofas don't sell, and how Intercom 15x'ed revenue just by reframing how they talked about their product.  Whether you're building software or selling furniture, this conversation will challenge how you think about customer insight — and give you tools to sharpen your product bets. Inside the episode… Why false positives in customer research can wreck a strategy How JTBD helped turn around a billion-dollar furniture retailer The 4 real jobs behind buying a sofa  Snickers vs. Milky Way: A JTBD breakdown of context and tradeoffs What most people get wrong about customer interviews Why personas don't drive decisions — and what actually does How Intercom used JTBD to grow from $5M to $75M Using AI to support high-consideration decisions How to know what your product is allowed to suck at Why survey data without context leads to bad bets     Mentioned in this episode Andrew's Startup Swizzl -  https://swizzl.ai/ Andrew's cofounder Bob Moesta - https://therewiredgroup.com/about/bob-moesta/ Clay Christensen's HBR article: “Know Your Customers' Jobs to Be Done” - https://hbr.org/2016/09/know-your-customers-jobs-to-be-done “Demand-Side Sales” by Bob Moesta - https://www.amazon.com/dp/1544509987/?bestFormat=true&k=demand%20side%20sales%20101&ref_=nb_sb_ss_w_scx-ent-pd-bk-d_de_k0_1_12&crid=8C2BLR9H1HF6&sprefix=demand%20side%20 “Competing Against Luck” by Clayton Christensen - https://www.amazon.com/Clayton-Christensen-Competing-Against-%E3%80%902018%E3%80%91/dp/B07KPWQQY3/ref=sr_1_2 Unlock the full potential of your product team with Integral's player coaches, experts in lean, human-centered design. Visit integral.io/convergence for a free Product Success Lab workshop to gain clarity and confidence in tackling any product design or engineering challenge. Subscribe to the Convergence podcast wherever you get podcasts including video episodes to get updated on the other crucial conversations that we'll post on YouTube at youtube.com/@convergencefmpodcast Learn something? Give us a 5 star review and like the podcast on YouTube. It's how we grow.   Follow the Pod Linkedin: https://www.linkedin.com/company/convergence-podcast/ X: https://twitter.com/podconvergence Instagram: @podconvergence

Hey Encouragers! In this episode... Mitch dives into a critical mindset shift for anyone building their coaching or speaking business: resist the urge to make big decisions based on "small data."

Capitulo 192 de Negocios de Otro Planeta conversando con German Goñi, Socio de Anticiparte, trabaja generando soluciones de negocios basadas en datos y de eso se trata este capitulo, como tomar decisiones informadas o mejores decisiones basadas en datos y no creencias, como pasar de Big Data a Small Data o Smart Data. Si quieres contactar con German te dejo su Linkedin https://www.linkedin.com/in/germangoni/ Si te gusto el podcast suscríbete, estaré haciendo mas como estos, dale me gusta y compártelo para que otros lo puedan disfrutar.

I went to San Francisco for Small Data SF, a conference sponsored by Mother Duck. The premise of the event was that smaller sets of data are both very useful and prevalent. The manifesto speaks to me, as I am a big fan of smaller sets of data for sure. I also think that most of the time we can use less data than we think we need, especially when it's recent data. That often is more relevant and we end up with contorted queries that try to weight new or old data differently to reflect this. Maybe the best line for me is this one: Bigger data has an opportunity cost: Time. Read the rest of Big Data or Small Data

What happens if we (think) we need to find the reliability of something with a small amount of data? What do we do? How do we find a number? How does this number help?

What are the major drivers for building embedded analytics applications? I interviewed Spencer Taylor, Co-Founder of Astrodata, at Small Data SF, and his insights were incredible! We covered some key drivers, starting with data monetization, where companies not only sell data but also enhance their products with analytics, creating stickier experiences for users. Spencer shared how this can open up new revenue streams and reduce churn, making data an even more valuable asset. We also dug into challenges, like finding tools with scalable pricing models and the importance of building proof of value mechanisms. The closer companies can get their data to actionable insights, the more value they can demonstrate to customers. And of course, we talked trends! Spencer is especially excited about the potential of client-side analytics, like the MotherDuck - WASM integration, which promises to make user experiences smoother and more responsive. #data #ai #smalldatasf #theravitshow

Hear it from the best – James Winegar, CEO at CorrDyn, during the Small Data SF conference. We discussed about the use cases of small data, future of small data, why should you start focusing on small data and much more! #data #ai #smalldatasf #theravitshow

I interviewed Hamilton Ulmer, Designing & Building MotherDuck at the Small Data SF! We spoke about how machines are getting powerful, how we are seeing the Small Data becoming more and more important! #data #ai #smalldatasf #theravitshow

How are enterprise leaders using Small Data and why are they excited about it? I had the pleasure of chatting with Chitrang Davé, Global Head of Enterprise Data & Analytics, Edwards Lifesciences. We discussed Small Data, why you should focus on it, and how powerful machines help! Did you miss Small Data SF? You can tune in to on-demand sessions on their website. Link in comments! #data #ai #smalldatasf #theravitshow

I had the pleasure to chat with the CEO & Co-Founder, of Fivetran, George. They recently Surpassed $300M ARR, Driven by Growing AI and Data Movement Demand! He shared some amazing insights on Small Data, customer stories and how does he think about future of small data! Thanks, George for the insights! #data #ai #smalldatasf #theravitshow

What's the next big thing in Small Data? I interview Glauber Costa, Founder & CEO of Turso at Small Data SF, and what a conversation it was! We discussed everything from Turso's mission to the importance of Small Data in today's tech landscape! Glauber also shared insights on his talk at the event, diving deep into how small, efficient models drive impactful results in AI and analytics. And of course, we couldn't miss discussing what's next for Small Data—big things are coming, and Turso is at the forefront! Stay tuned for the full interview to hear more about the future of data from a true innovator! #data #ai #smalldatasf #theravitshow

Why is Small Data important in the world of Big Data? How are powerful machines helping us? I chatted with Jordan Tigani, Co-Founder & CEO of MotherDuck at Small Data SF to learn more about it. Thanks for sharing great insights, Jordan! I enjoyed Small Data SF and all the amazing sessions! #data #ai #smalldatasf #theravitshow

In this episode, Dean speaks with Jeremie Dreyfuss, Head of AI Research and Development at Intel, about the evolving role of AI in the enterprise. Jeremie shares insights into scaling machine learning solutions, the challenges of building AI infrastructure, and the future of AI-driven innovation in large organizations. Learn how enterprises are leveraging AI for efficiency, the latest advancements in AI research, and the strategies for staying competitive in a rapidly changing landscape. Join our Discord community: https://discord.gg/tEYvqxwhah --- Timestamps: 00:00 Introduction and Overview 00:55 Challenges of Data Collection and Infrastructure 05:00 Optimizing Test Recommendations 14:42 Tips for Deploying Entire ML Pipelines 21:19 The Impact of Large Language Models (LLMs) 25:30 How to Decide About LLM Investment in the Enterprise 29:29 Evaluating Models and Using Synthetic Data 35:34 Choosing the Right Tools for ML and LLM Projects 45:21 The Beauty of Small Data in Machine Learning 48:22 Recommendations for the Audience ➡️ Jeremie Dreyfuss on LinkedIn – https://www.linkedin.com/in/jeremie-dreyfuss/

I spoke Ryan Boyd

I had the pleasure of interviewing Wes McKinney, Creator of Pandas, a name well-known in the data world through his work on the Pandas Project and his book, Python for Data Analysis. Wes is now at Posit PBC, and during our conversation at Small Data SF, we covered several key topics around the evolving data landscape! Wes shared his thoughts on the significance of Small Data, why it's a compelling topic right now, and what “Retooling for a Smaller Data Era” means for the industry. We also dove into the challenges and potential benefits of shifting from Big Data to Small Data, and discussed whether this trend represents the next big movement in data. Curious about Apache Arrow and what's next for Wes? Check out our interview where Wes gives some great insights into the future of data tooling. #data #ai #smalldatasf2024 #theravitshow

A founding engineer on Google BigQuery and now at the helm of MotherDuck, Jordan Tigani challenges the decade-long dominance of Big Data and introduces a compelling alternative that could change how companies handle data. Jordan discusses why Big Data technologies are an overkill for most companies, how MotherDuck and DuckDB offer fast analytical queries, and lessons learned as a technical founder building his first startup. Watch the episode with Tomasz Tunguz: https://youtu.be/gU6dGmZzmvI Website - https://motherduck.com Twitter - https://x.com/motherduck Jordan Tigani LinkedIn - https://www.linkedin.com/in/jordantigani Twitter - https://x.com/jrdntgn FIRSTMARK Website - https://firstmark.com Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ Twitter - https://twitter.com/mattturck (00:00) Intro (00:56) What is the Small Data? (06:56) Marketing strategy of MotherDuck (08:39) Processing Small Data with Big Data stack (15:30) DuckDB (17:21) Creation of DuckDB (18:48) Founding story of MotherDuck (24:08) MotherDuck's community (25:25) MotherDuck of today ($100M raised) (33:15) Why MotherDuck and DuckDB are so fast? (39:08) The limitations and the future of MotherDuck's platform (39:49) Small Models (42:37) Small Data and the Modern Data Stack (46:47) Making things simpler with a shift from Big Data to Small Data (50:04) Jordan Tigani's entrepreneurial journey (58:31) Outro

At the Small Data conference recently, one of the talks looked at hardware advances. It was interesting to see a data perspective on hardware changes, as many of us only worry about the results of hardware: can I get my data quickly? In or out, most of us are more often worried about performance than specs. However, today I thought it might be fun to look at a few changes and numbers to get an idea of how our hardware has changed, in the march towards dealing with more and more data. Big data anyone? In thinking about disks, I saw a chart that looked at the changes from HDD (hard disk drives) to SDD (solid state drives) to NVMe (Nonvolatile Memory Express). These show read speeds going through the list from 80MB/S to 200MB/s to 5000+MB/s. That's a dramatic change, and not one only in high-end arrays. There are off-the-shelf drives you can put in a desktop that read this fast. If you think about some of the early IBM drives, which read at 8800b/s. Growth in disk speed, inside the timeline of our careers, has grown by a few orders of magnitude in read speed. Read the rest of The Vast Expansions of Hardware

What makes MotherDuck and DuckDB a game-changer for data analytics? Join us as we sit down with Jacob Matson, a renowned expert in SQL Server, dbt, and Excel, who recently became a developer advocate at MotherDuck. During this episode, Jacob shares his compelling journey to MotherDuck, driven by his frequent use of DuckDB for solving data challenges. We explore the unique attributes of DuckDB, comparing it to SQLite for analytics, and uncover its architectural benefits, such as utilizing multi-core machines for parallel query execution. Jacob also sheds light on how MotherDuck is pushing the envelope with their innovative concept of multiplayer analytics.Our discussion takes a deep dive into MotherDuck's innovative tenancy model and how it impacts database workloads, highlighting the use of DuckDB format in Wasm for enhanced data visualization. Jacob explains how this approach offers significant compression and faster query performance, making data visualization more interactive. We also touch on the potential and limitations of replacing traditional BI tools with Mosaic, and where MotherDuck stands in the modern data stack landscape, especially for organizations that don't require the scale of BigQuery or Snowflake. Plus, get a sneak peek into the upcoming Small Data Conference in San Francisco on September 23rd, where we'll explore how small data solutions can address significant problems without relying on big data. Don't miss this episode packed with insights on DuckDB and MotherDuck innovations!Small Data SF Signup  Discount Code: MATSON100What's New In Data is a data thought leadership series hosted by John Kutay who leads data and products at Striim. What's New In Data hosts industry practitioners to discuss latest trends, common patterns for real world data patterns, and analytics success stories.

In this talk, Douglas emphasizes that data, when treated as a valuable asset rather than a byproduct, can drive immense value for organizations. Data's unique qualities—its ability to be used repeatedly without depletion and to generate more data—can transform businesses, especially when leveraged strategically through innovative technologies and proper asset management. Douglas advocates for organizations to move beyond just reporting and compliance, and instead focus on using data to generate actionable insights that improve outcomes and create new value streams.Originator of the "3 Vs" of big data: Volume, Velocity, and Variety. Douglas LaneyAdvisor. Speaker. Author. Instructor.https://www.douglasblaney.com/Doug Laney is a best-selling author and recognized authority on data and analytics strategy. He advises senior IT, business and data leaders on data monetization and valuation, data management and governance, external data strategies, analytics best practices, and establishing data and analytics organizations. Doug's book, Infonomics: How to Monetize, Manage, and Measure Information for Competitive Advantage, was selected by CIO Magazine as the “Must-Read Book of the Year” and a “Top 5 Books for Business Leaders and Tech Innovators.”​Currently, the Data & Analytics Strategy Innovation Fellow with the consulting firm West Monroe, Doug previously held the position of Distinguished Analyst with Gartner's Chief Data Officer research and advisory team and was a three-time Gartner annual thought leadership award recipient.In addition, Doug launched and managed the Deloitte Analytics Institute, is a Forbes contributing writer and has been published in the Wall Street Journal and the Financial Times among other journals. Doug has guest-lectured at major business schools around the world and is a visiting professor with the University of Illinois Gies College of Business where he teaches Infonomics and Business Analytics Executive Overview courses, which also are available online via Coursera.He also co-chairs the annual MIT CDO/IQ Symposium, is a visiting professor at Carnegie Mellon University's Heinz College, is a member of the World Economic Forum's data exchange initiative, a member of the American Economic Association, and sits on various technology company advisory boards.Follow and connect with Doug via Twitter @Doug_Laney and LinkedIn. #infonomicsSubscribe to our podcast, and leave a reviewConnect with us on Instagram, FaceBook, Twitter , and LinkedInhttps://eima-inc.com/lets-talk-small-data@letstalksmalldatapodMusic credit: Yung Kartz

Jordan Tigani is back to chat about why small data is awesome, data lakehouses, DuckDB, AI, and much more. Motherduck: https://motherduck.com/ LinkedIn: https://www.linkedin.com/in/jordantigani/ Twitter: https://twitter.com/jrdntgn?lang=en

In this episode of Unplugged, hosts Phil Saboa and Ed Fuentes are joined by Darren Anderson, a lecturer of industrial automation and mechatronics at South Eastern Regional College. With over 28 years of industry experience, Darren shares his journey from being an electrical maintenance engineer to an educator. The conversation covers his hands-on experience with PLCs, microcontrollers, and IoT, as well as how he incorporates these technologies into his curriculum. Darren also discusses the growing adoption of Industry 4.0 and IoT in local businesses and the importance of apprenticeships in bridging the skills gap. 00:00 Introduction to Unplugged: An IIoT Podcast 00:30 Meet Darren Anderson: Background and Experience 02:15 The Drive Behind Learning and Understanding the "Why" 05:42 Strategy and People Before Technology 08:01 Upcoming Learning Goals: Ignition, Python, and SQL 11:23 Continuous Learning and Industry Adaptability 15:09 Challenges and Adaptations During the COVID-19 Pandemic 18:45 Teaching and Sparking Interest in Advanced Technology 21:56 The Role of Apprenticeships in Addressing Skills Gaps 23:40 Engaging Students in IIoT and Industry Practices 27:05 Personal Journey in Home Automation 31:22 Understanding Energy Usage and Temperature Impact 34:15 Challenges and Successes in IoT Projects 38:12 Factory Tours: BMW Motorrad and Spirit AeroSystems 41:08 Digital Transformation in Local Industries 45:01 The Importance of Small Data and Operator Knowledge 48:25 First Encounter with "Industry 4" and IoT 50:13 Influencing Students with Hands-On IoT Projects 54:04 Legacy Knowledge in Industry 4.0 57:20 Becoming an Industry Evangelist 59:05 Follow Darren Anderson on LinkedIn and Final Thoughts 60:20 Subscribe to Unplugged: An IIoT Podcast Connect with Darren on LinkedIn: https://www.linkedin.com/in/darren-anderson-401408b2/ Connect with Phil on LinkedIn: https://www.linkedin.com/in/phil-seboa/ Connect with Ed on LinkedIn: https://www.linkedin.com/in/ed-fuentes-2046121a/ About Industry Sage Media: Industry Sage Media is your backstage pass to industry experts and the conversations that are shaping the future of the manufacturing industry. Learn more at: http://www.industrysagemedia.com

Rob Harmon joins us to chat about small data, being efficient, data modeling, and much more.

Yosef Miller, with over a decade of experience in healthcare data and operational analytics, discusses the significance of operational analytics in healthcare, distinguishing it from traditional data analytics. With a robust background in team dynamics, data-driven decision-making, and project management, Yosef excels at leveraging technology to streamline operations and enhance organizational efficiency. In this conversation, he underscores the importance of data governance and the collaboration between teams to strengthen organizational data strategies. Offering advice for newcomers, he emphasizes the need for diverse skill sets, including proficiency in MS Excel and SQL. He also addresses challenges such as navigating organizational structures and the undervaluation of data analytics roles."If you don't make an effort to promote yourself and make yourself known and useful within the organization, you could easily fall by the wayside." – Yosef MillerBio Yosef Miller is the Director of Financial Operations and Analytics at Optum, bringing over a decade of experience in information technology and operational analytics to his role. With a strong background in team dynamics, data-driven decision-making, and project management, Yosef excels in leveraging technology to streamline operations and enhance organizational efficiency. He holds a degree in Management Information Systems from Yeshiva University and is known for his strategic thinking and ability to implement innovative solutions that drive business growth. His expertise spans various industries, with a particular focus on improving IT infrastructure and optimizing performance through advanced analytics.Subscribe to our podcast, and leave a reviewConnect with us on Instagram, FaceBook, Twitter , and LinkedInhttps://eima-inc.com/lets-talk-small-data@letstalksmalldatapodMusic credit: Yung Kartz

Tune in to this insightful and heartfelt conversation with Paulina, a graduate student in Healthcare Administration at Hofstra University, New York, and a passionate healthcare data analytics enthusiast. We touched on the crucial role of front desk staff in healthcare, the power of data analytics in identifying patterns and improving mental health services, and the transformative potential of AI. Paulina shared her valuable lessons learned, thoughts on overcoming imposter syndrome, and the importance of mentorship and self-care.Paulina Palencia-Catalanhttps://www.linkedin.com/in/paulina-palencia-catalan-b1b2b82a4/paulina.palencianpt@gmail.comppalenciacatalan1@pride.hofstra.eduBio Paulina Palencia-Catalan is a graduate student at Hofstra University, where she is pursuing a master's in Healthcare Administration. She earned her bachelor's degree in Psychology from Stony Brook University. Currently, she works at Northwell Health as a Patient Account Representative. Paulina's primary interests in the healthcare field are data analytics and public health, and she is eager to explore the intersection between these two areas. She believes there is always room for improvement. With new data arriving every day, the key questions she focuses on are: What can be done with it? What patterns can be identified to enhance patient experience and benefit the community?Subscribe to our podcast, and leave a reviewConnect with us on Instagram, FaceBook, Twitter , and LinkedInhttps://eima-inc.com/lets-talk-small-data@letstalksmalldatapodMusic credit: Yung Kartz

Kim's inspiring journey from a direct support professional to a dedicated nurse highlights passion, determination, and the importance of mentorship. Along the way, an appreciation for the power of data analytics and its impact has emerged.  Her diverse healthcare experiences and academic achievements showcase resilience and commitment. Listen to learn how her story exemplifies the impactful role of data analytics in improving patient care and outcomes in healthcare."Deciding to become a nurse was challenging but incredibly rewarding. I embraced every opportunity to grow. My journey taught me that dedication and support can lead to meaningful impacts in patient care." – Kim LeakKimberly Leak BSN, RNInfection PreventionistKimberly Leak has been a registered nurse for over 4 years and resides in New York City. She began her registered nursing career during the COVID pandemic, where she had to simultaneously learn crisis nursing and the basic nursing skills of a new graduate. After a year as a staff nurse in Suffolk County, NY, Kimberly embarked upon travel nursing, completing assignments at University of Virginia, as well as assignments in Spokane, WA; Columbia, SC and Buffalo NY. She briefly worked at Mt. Sinai Hospital in New York City, but resigned due to unsafe and substandard working conditions, which ultimately culminated in a nurses' strike. During her course as a travel nurse, Kimberly noticed disparities amongst the electronic medical records utilized at her various assignments. This is what ultimately sparked her interest in health informatics. She has experience with various electronic medical records systems. She currently is a nursing home infection preventionist—a job that requires a lot of data collection and analyzation.  Kimberly wants to use her experience in the nursing home to improve resident outcomes via improved health informatics in the long term care setting. Subscribe to our podcast, and leave a reviewConnect with us on Instagram, FaceBook, Twitter , and LinkedInhttps://eima-inc.com/lets-talk-small-data@letstalksmalldatapodMusic credit: Yung Kartz

Jasper Gold of 1 Crown Office Row joins Lucy McCann to explore “small data” claims, where data and personal injury law intersect. Law Pod UK is published by 1 Crown Office Row. Supporting articles are published on the UK Human Rights Blog. Follow and interact with the podcast team on Twitter.

The massive uncontrollable variable that is the weather can create challenges when benchmarking and comparing agronomic decisions over large geographies. Antara Agronomy is trying to overcome the problem with weather by taking a “small data-first” approach with Antara Insights — an agronomy benchmarking program that was recognized with a runner-up award in the Ag Days’... Read More

Chapter 1 What's Small Data Book by Martin LindstromThe Small Data Book written by Martin Lindstrom is a marketing book that focuses on the importance of gathering and analyzing small-scale, real-time data to understand consumer behavior and make strategic business decisions. Lindstrom argues that traditional big data analytics often overlook the emotional and cultural aspects of consumer behavior, which can be better understood through small data.In the book, Lindstrom shares his experiences as a brand consultant, providing case studies and personal anecdotes to illustrate the value of small data. He highlights the significance of observing people's daily lives, conducting in-depth interviews, and immersing oneself in local cultures to uncover valuable insights. Lindstrom emphasizes that small data helps companies understand the "why" behind consumer behavior and uncover hidden desires and needs, which can ultimately lead to successful marketing campaigns and product innovation.The Small Data Book also explores the ethical implications of data collection and privacy concerns in today's digital world. Lindstrom discusses the need for responsible data collection practices and the importance of gaining consumers' trust.Overall, the book advocates for the power of small data in understanding consumer behavior and offers practical advice on how companies can effectively use this approach to gain a competitive edge in the market.Chapter 2 Is Small Data Book A Good Book"Small Data: The Tiny Clues That Uncover Huge Trends" by Martin Lindstrom has generally received positive reviews and is recommended by many readers. The book explores the power of observation and the importance of small details in understanding consumer behavior and market trends. It offers practical insights and real-life examples to highlight the significance of "small data" in contrast to big data. Ultimately, whether the book is good or not depends on your personal interests and the specific knowledge you seek. Reading reviews, summaries, or sample chapters may help you assess if it aligns with what you are looking for.Chapter 3 Small Data Book by Martin Lindstrom SummaryThe Small Data Book by Martin Lindstrom is a guidebook that explores the power and importance of small data in today's world. In the book, Lindstrom argues that while big data is often seen as the key to understanding consumer behavior, it is actually small data that holds the key to unlocking valuable insights.Lindstrom defines small data as seemingly insignificant observations or clues that can provide crucial insights into consumer behavior. He shares numerous examples from his own experiences as a marketing consultant, highlighting how small data has helped him uncover hidden patterns and understand consumer preferences on a deeper level.The book also delves into Lindstrom's journey around the world, where he visits different communities and observes their behaviors and habits. He emphasizes the importance of immersing oneself in the lives of consumers in order to truly understand their needs and desires.Throughout the book, Lindstrom provides practical tips on how businesses and individuals can utilize small data to improve their decision-making processes. He emphasizes the importance of looking beyond the numbers and focusing on the human element in order to make meaningful connections with consumers.Overall, The Small Data Book offers a refreshing perspective on data analysis and challenges the prevailing belief that big data is the ultimate solution. With its engaging anecdotes and actionable insights, the book encourages readers to pay attention to the small details that can make a big difference in understanding consumer...

Join the exploration of language models as Stability AI introduces FreeWilly, advanced models trained on small, synthetic data sets. Uncover the significance and potential applications of this groundbreaking release. Stay informed and be part of the conversation shaping the future of AI—tune in now! AI Box Waitlist: https://AIBox.ai/Join our ChatGPT Community: ⁠https://www.facebook.com/groups/739308654562189/⁠Follow me on Twitter: ⁠https://twitter.com/jaeden_ai⁠

Experience innovation in language models as Stability AI launches FreeWilly, advanced models trained on small, synthetic data sets. Delve into the potential applications and groundbreaking implications of this release. Stay connected with the AI community—listen now and stay informed! AI Box Waitlist: https://AIBox.ai/Join our ChatGPT Community: ⁠https://www.facebook.com/groups/739308654562189/⁠Follow me on Twitter: ⁠https://twitter.com/jaeden_ai⁠

Prof. Dr. Hannes Mühleisen is a creator of the DuckDB database management system and Co-founder and CEO of DuckDB Labs. Jordan is co-founder and chief duck-herder at MotherDuck, a startup building a serverless analytics platform based on DuckDB. MLOps podcast #202 with Hannes Mühleisen, Co-Founder & CEO of DuckDB Labs and Jordan Tigani, Chief Duck-Herder at MotherDuck, Small Data, Big Impact: The Story Behind DuckDB. // Abstract Navigate the intricacies of data management with Jordan Tagani and Hannes Mühleisen, the creative geniuses behind DuckDB and MotherDuck. This deep dive unravels the game-changing principles behind DuckDB's creation, tackling the prevailing wisdom to passionately fill the gap for smaller data set management. Let's also discover MotherDuck's unique focus on providing an unprecedented developer experience and its innovative edge in visualization and data delivery. This episode is teeming with enlightening discussions about managing community feedback, funding, and future possibilities that should not be missed for any tech enthusiasts and data management practitioners. // Bio Hannes Mühleisen Prof. Dr. Hannes Mühleisen is a creator of the DuckDB database management system and Co-founder and CEO of DuckDB Labs, a consulting company providing services around DuckDB. Hannes is also Professor of Data Engineering at Radboud Universiteit Nijmegen. His' main interest is analytical data management systems. Jordan Tigani Jordan is co-founder and chief duck-herder at MotherDuck, a startup building a serverless analytics platform based on DuckDB. He spent a decade working on Google BigQuery, as a founding engineer, book author, engineering leader, and product leader. More recently, as SingleStore's Chief Product Officer, Jordan helped them build a cloud-native SaaS business. Jordan has also worked at Microsoft Research, the Windows Kernel team, and at a handful of star-crossed startups. His biggest claim to fame is predicting world cup matches using machine learning with a better record than Paul the Octopus. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Websites: https://duckdb.org/ https://motherduck.com/ ⁠ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Hannes on LinkedIn: https://www.linkedin.com/in/hfmuehleisen/ Connect with Jordan on LinkedIn: https://www.linkedin.com/in/jordantigani/ Timestamps: [00:00] Hannes and Jordan's preferred coffee [01:30] Takeaways [03:43] Swaggers in the house! [07:13] Duck DB's inception [09:38] Jordan's background [12:28] Simplify Developer Experience [17:54] Big Data Shift [26:01] Creation of MotherDuck [30:58] Duck DB and MotherDuck Partnership [31:57] Incentive Alignment Concerns [37:46] Building an incredible developer experience [43:38] User Testing Lab [47:18] Setting a higher standard [49:22] The moments before the moment [52:18] Gathering feedback and talking to the community [54:30] MotherDuck Features [1:00:19] Cloud Innovation for MotherDuck [1:02:41] ML Engineers and DuckDB [1:08:03] Wrap up

Explore Stability AI's FreeWilly, a revolutionary language model mastering small, synthetic data. Witness the evolution of AI language understanding! Get on the AI Box Waitlist: https://AIBox.ai/Join our ChatGPT Community: ⁠https://www.facebook.com/groups/739308654562189/⁠Follow me on Twitter: ⁠https://twitter.com/jaeden_ai⁠

Unveil Stability AI's FreeWilly, an innovative language model mastering small, synthetic data, redefining AI language learning paradigms! Get on the AI Box Waitlist: https://AIBox.ai/Join our ChatGPT Community: ⁠https://www.facebook.com/groups/739308654562189/⁠Follow me on Twitter: ⁠https://twitter.com/jaeden_ai⁠

Experience Stability AI's FreeWilly—a breakthrough in language mastery with small, synthetic data. Join the transformation in AI language comprehension! Get on the AI Box Waitlist: https://AIBox.ai/Join our ChatGPT Community: ⁠https://www.facebook.com/groups/739308654562189/⁠Follow me on Twitter: ⁠https://twitter.com/jaeden_ai⁠

As marketers, we all want to know what truly drives the growth of challenger brands. We all want to see massive user growth, successful campaigns, and ultimately, we want marketing to have an impact. So why are these so hard to achieve? In this fast-paced, ever-evolving world of marketing, the volume of information can be overwhelming at times and obsolete at others. It's a daunting and steep learning curve for marketers who are on a mission to change their categories or their entire industries. In this week's episode, we take a deep dive into how to market like the best brands in the world and truly set yourself apart as a challenger brand. Distilling the knowledge of 65 episodes of Scratch and 100s of conversations we've had with the best CMOs in the world from PepsiCo to P&G, Beavertown to Bloom & Wild, we decode the 10 core principles of how to market like the best challenger brands in the world. You'll hear from the likes of Gary Vee, Linda Boff, Rory Sutherland, and Jim Stengel on topics ranging from customer centricity to frameworks for innovation and the ability to understand and utilize data correctly. In this special episode, we cover all the timeless principles you need to be a high-performing modern marketer. Use this episode as a reference guide and drop in and out for the chapters and feel free to check out the full episodes all linked below. Welcome to Challenger Marketing Decoded! 

In this captivating episode, we unveil the game-changing innovation from Stability AI: FreeWilly, advanced language models trained on small, synthetic data sets. Discover how these cutting-edge models are revolutionizing the accessibility of powerful language processing tools. Join us to explore the implications of FreeWilly and its potential to transform the world of language AI, all for free! Get on the AI Box Waitlist: https://AIBox.ai/Join our ChatGPT Community: ⁠https://www.facebook.com/groups/739308654562189/⁠Follow me on Twitter: ⁠https://twitter.com/jaeden_ai⁠

Get ready for an exhilarating episode as we introduce you to Stability AI's groundbreaking creation, FreeWilly. These advanced language models, trained on small, synthetic data sets, are poised to disrupt the world of AI. Join us to explore the exciting potential and real-world applications of FreeWilly, the game-changer in small data language models. Get on the AI Box Waitlist: https://AIBox.ai/Join our ChatGPT Community: ⁠https://www.facebook.com/groups/739308654562189/⁠Follow me on Twitter: ⁠https://twitter.com/jaeden_ai⁠

A motivating and inspiring chat with Joe and Will about their work together to place self-serve ice-cream vending machines in the hospital and their plans for future expansion. The team highlights the importance of technology in improving healthcare workflows. The aim is to provide a premium, convenient, and user-friendly healthy dessert experience that enhances employee engagement and overall patient experience.Disruption and Transformation in Healthcare.Technology and Automation.Joe DobiasDirector, Food & Nutrition ServicesLI Jewish Valley Streamjdobias@northwell.eduChef Joe Dobias is an Executive Chef, Restaurateur, and Culinary Educator, who has spent the last 17 years honing his craft in New York City. Dobias is a Graduate of Cornell University's prestigious school of Hotel Administration and has been employed as an Executive Chef in New York City since the age of 23. His career began at age 13 in a local seafood restaurant in his hometown of Port Jefferson Station, Long Island. It was at that age that Chef Dobias fell in love with the restaurant business for its emphasis on hard work, creativity, and its incessant nature. He opened his first restaurant in 2008, named JoeDoe (his byname) in the East Village of NYC, which was rebranded as Joe & MissesDoe in 2013. Chef Joe Dobias' cooking style can be described as New American with an emphasis on re-working 'Old World' classic recipes and techniques. He has been recognized nationally by The New York Times, The New Yorker, The James Beard Foundation, Travel & Leisure, and Michelin. He has appeared on multiple Television Networks including The Food Network, ABC, NBC, The Blaze, PBS etc. Chef Joe Dobias competed on Food Network's hit culinary competition show 'Chopped' and won $10,000 in 2009. Dobias most recently appeared on Food Network's 'Beat Bobby Flay' culinary series and ABC's The Chew. In addition to Joe & MissesDoe, he was the CEO and proprietor of JAMD Catering & Events Company, which launched in 2011 and included high profile clients such as DirectTV, Google, Tumblr, and Stance. In 2017, Chef Joe Dobias joined Jean-Georges Management along with his wife Jill to operate a multi-unit restaurant on Fire Island. Chef Dobias recently relocated back to his Long Island roots from NYC with his wife Jill and their rescue dogs Dill, Gotti, and Rina.  In 2019, after spending his whole career in hospitality he moved into healthcare as a Director of Food and Dining services for Northwell Health System on Long Island.  Will CheungOwner and Operator, 99spoonsnycwill@99spoonsec.comhttps://99spoonsnyc.com/With a deep-seated passion for helping people, and having studied at SUNY Stonybrook in business management, Will Cheung embarked on a career in the hospitality industry for 25 years.  He started as a bookkeeper while in college. Over the years, Will has had the privilege of ascending and mastering every aspect of hotel operations, with customer satisfaction in mind. He eagerly embraced each opportunity to learn and grow, driven by an innate desire to create joyous moments for guests.From front desk operations to general management, Will immersed himself in every facet of the hotel business. This hands-on experience allowed him to understand the intricacies of delivering unforgettable guest experiences and ensuring their happiness.His journey from Bookkeeper to General Manager in the hotel industry, combined with this new adventure in the world of ice cream, underscores his commitment to making customers happy. He believes that happiness is not just a feeling, it's a mission. And he looks forward to continuing to spread happiness with every endeavor he undertakes.

Due to health-related issues, we are on a temporary hiatus for new episodes. Please enjoy this rerelease of episode 150 with Carlos Saona. eDreams' approach is very unique and interesting because it was essentially all on its own so there are a ton of useful learnings to consider if they are the right fit for your own organizations.Sign up for Data Mesh Understanding's free roundtable and introduction programs here: https://landing.datameshunderstanding.com/Please Rate and Review us on your podcast app of choice!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 / Scott Hirleman. 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.Carlos' LinkedIn: https://www.linkedin.com/in/carlos-saona-vazquez/In this episode, Scott interviewed Carlos Saona, Chief Architect at eDreams ODIGEO.As a caveat before jumping in, Carlos believes it's too hard to say their experience or learnings will apply to everyone or that he necessarily recommends anything they have done specifically but he has learned a lot of very interesting things to date. Keep that perspective in mind when reading this summary.Some key takeaways/thoughts from Carlos' point of view:eDreams' implementation is quite unique in that they were working on it without being in contact with other data mesh implementers for most of the last 3 years - until just recently. So they have learnings from non-typical approaches that are working for them.You should not look to create a single data model upfront. That's part of what has caused such an issue for the data warehouse - it's inflexible and doesn't really end up fitting needs. But you should look to iterate towards that standard model as you learn more and more about your use cases.?Controversial?: Look to push as much of the burden as is reasonable onto the data consumers. That means the stitching between data products, the compute costs of consuming, etc. They get the benefit so they should be taking on the burden. Things like data quality are still on the...

Welcome to another exciting episode of Scratch, where we had  the pleasure of hosting the brilliant Martin Lindstrom, a Marketing Expert and Author hailing from Lindstrom Company. Martin's reputation precedes him, known for his influential works including "Buyology," "Small Data," and "The Ministry of Common Sense."But that's not all; Martin's influence extends far beyond the pages of his books. As the founder and chairman of Lindstrom Company, he leads a pioneering branding and culture transformation firm that operates across five continents and in over 30 countries. Over the years, he has been the go-to consultant for the crème de la crème of Fortune 100 companies, helping them navigate the ever-evolving landscape of branding and culture.Together, Eric and Martin dive into the world of marketing, exploring the delicate balance between creativity and data-driven science. Martin underlines the crucial role of putting consumers at the heart of your strategy to build a powerful brand. He also highlights the hurdles that creativity encounters in today's ever-evolving marketing landscape.Additionally, Martin shares valuable insights into the role of a Chief Marketing Officer (CMO) in the intricate web of today's business world. Throughout the episode, there's a strong emphasis on the profound impact you can make in the world by aligning your work with a greater purpose. We really enjoyed recording this one, and are sure that you'll find Martin's insights and perspectives truly enlightening. Mentioned in the show:

How is computer vision being used to spot autism symptoms much earlier in children? What is augmented cognition? And how can you use AI to make data models work even with small data sets? We will learn those answers and more in this episode with Dr. Sarah Ostadabbas. Dr. Ostadabbas is an associate professor in Electrical and Computer Engineering at Northeastern University, where she is also the director of the Augmented Cognition Laboratory (ACLab), which works at the intersection of computer vision, pattern recognition, and machine learning. Before joining Northeastern, she was a post-doctoral researcher at Georgia Tech and earned her Ph.D. at the University of Texas at Dallas. A renowned expert in the field, her research focuses on the goal of enhancing human information-processing capabilities through the design of adaptive interfaces based on rigorous models using machine learning and computer vision algorithms. With over 100 peer-reviewed publications, Professor Ostadabbas has received recognition and awards from prestigious government agencies such as the National Science Foundation (NSF), the Department of Defense (DoD) as well as several private industries. In 2022, she received an NSF CAREER award to use artificial intelligence for the early detection of autism, which she is working on with Oracle for Research. http://www.oracle.com/research   ---------------------------------------------------------   Episode Transcript:   00;00;00;00 - 00;00;26;15 How are computer vision and contactless techniques spotting signs of autism much earlier in children? What is augmented cognition and how can you use AI to make data models work, even with small datasets? We'll find all that out and more in this episode of Research in Action. Hello and welcome back to Research in Action, brought to you by Oracle for Research.   00;00;26;15 - 00;00;50;10 I'm Mike Stiles, and today we have with us Dr. Sarah Ostadabbas, an Associate Professor in the Electrical & Computer Engineering Department Northeastern University, where she's also director of the Augmented Cognition Laboratory (ACLab), which works at the intersection of computer vision, pattern recognition and machine learning. Before joining Northeastern, she was a postdoctoral researcher at Georgia Tech and got her Ph.D. at the University of Texas at Dallas.   00;00;50;13 - 00;01;24;04 Her research looks at how we can enhance human information processing capabilities by designing adaptive interfaces based on rigorous models using machine learning and computer vision algorithms. With over 100 peer reviewed publications. Professor Ostadabbas has received recognition and awards from government agencies like the National Science Foundation, the Department of Defense and several private industries. In 2022, she received an NSF career award to use AI for early detection of autism, and she's working on that with Oracle for Research.   00;01;24;04 - 00;01;43;26 Dr. Ostadabbas, thank you so much for being with us today. Thanks for having me. I'm excited to be here and feel free to call me Sarah. Well, listeners, get ready because we're going to get all into computer vision, machine learning, augmented cognition and wherever else I can get nosy about. But first, let's hear about you, Sarah, and your background.   00;01;43;26 - 00;02;12;08 Your passion for technology and physics kind of started back in childhood, right? Yes, that's correct. Actually, physics was my favorite subject in middle school and high school. I was so passionate about it that I even went through the whole volume of Fundamentals of Physics by David Halliday and Robert Resnick in I believe it was in 10th year of my high school, and I was seriously considering to pursue the continuous PhD in physics even before graduating from high school.   00;02;12;10 - 00;02;39;09 And alongside my love for physics, I was always also fascinated by technology, especially computers and programing. I started coding in a language called Basic, which some of your audience may not even heard about that. Why I was in middle school and loved it. Data Analytics capabilities of computer and how computers are giving advanced processing power to human no matter where they are.   00;02;39;11 - 00;03;12;14 I was still living in Iran at the time and experiencing technological advances at that time, such as Internet and cell phone, and they were all very much interesting. And fast forward, all of this led me to pursue a natural combination of my interests, which was an electrical and computer engineering degree with a double majoring in biomedical engineering. And now when I look back, it's actually heartwarming to see one that one seemed to be diverse.   00;03;12;14 - 00;03;41;17 Interesting collection of interests now have shaped my academic journey so far. Was it unusual for someone, you know, at your age, at that early age of middle school, to be coding and thinking about technology and physics and looking that far into the future? I was actually going to date if school, middle school and high school at that time was designed for for math and science.   00;03;41;17 - 00;04;06;00 So no, I had a lot of of my classmates going and exploring different science topics. So it wasn't unusual. I mean, it was unusual when I was taking these heavy books to my gathering at parties, at my family, but not at the school. So I'm glad. And it was 200 of us, 200 girls at and now all of us are all around the world.   00;04;06;06 - 00;04;28;02 Most of us have PhDs. And yeah, it wasn't unusual, but it, it was something that I cherish. Yeah, it's great that you had a school that focused on things like that. So let's kick things off with your NSF CAREER Award focused on developing machine learning algorithms towards the early detection of autism. Tell me if I get this wrong.   00;04;28;02 - 00;04;53;08 But this is about using computer vision to predict autism a lot earlier in children. And what does what does that research involve and what does Oracle for Research have to do with it? You're certainly right. As I mentioned, my academic background revolves around electrical and computer engineering, focusing on data processing. And these data sources can be signals, images and videos.   00;04;53;11 - 00;05;21;06 How might a specific focus a work on computer vision began when I joined Northeastern University as an assistant professor in 2016. As you may know and have heard of over the past decade, deep learning models have been driving advancements in many AI topics, including computer vision. But these algorithms often require a large amount of training data. They are very data hungry.   00;05;21;08 - 00;05;48;24 So my National Science Foundation CAREER Award aims to leverage this advancement in computer vision for a specific health related domain that suffera from limited data. And I'm in particularly focusing on detecting autism in infant even before the first birthday. And this is true processing videos that is collected from them when they are doing daily activities, which is not a lot of things that they do.   00;05;49;01 - 00;06;16;13 They are sleeping, playing or eating. And as I mentioned, my algorithm, they are designed to be data deficient because I'm working on the area that the there are not a lot of data due to this privacy and security reason, but adapting these complex networks, these complex neural networks which are which are building blocks of deep learning necessitates powerful computing resources.   00;06;16;20 - 00;06;44;25 And that's where our collaboration with Oracle become highly valuable, allows me to make this model adapted to this specific application. So you have videos, video cameras, monitoring the kids and kind of like an in the wild get capturing of data. And then the computing power is needed to crunch all that video and that pulls out certain patterns that reveal autism earlier.   00;06;44;25 - 00;07;07;14 Is that how it works? Yeah. I mean, you can say that you put that on the simpler words. Yes, exactly. I'm a simple man. No, no, no. I'm just it's a good I mean, it's a good, good way to describe that. Yes, that's correct. So what we do, we actually leverage these computer vision techniques and contactless video processing algorithm to predict autism, as I mentioned, from daily activities.   00;07;07;19 - 00;07;35;17 And these are daily activities captured by commercial video recording messages. Imagine like a baby monitor or even parent's cell phone cameras. Every parent's love to record videos from the day of their child. So they focus on this specific developmental sign. How will that that relates to motor function, which means that relates to infants posture, muscle tone, body symmetry, and they balance and range of movement.   00;07;35;18 - 00;08;04;05 So these are specific markers that actually has been shown to be early visible warning signs of more developmental disorders such as autism. And they appear actually interestingly, long before the core feature of autism that you may have heard of and these are actually very known, such as social or communication difficulties as well as repetitive behavior. So we are focusing on these early signs.   00;08;04;08 - 00;08;29;11 However, currently the standard approach to monitor this motor function is through visits to child doctor, pediatrician and how is it, unfortunately, over half of these visits are missed. You could imagine often due to the lack of transportation, for parents, it's hard to take time off from work and also lack of child care for other other kids set at home.   00;08;29;13 - 00;09;12;29 So half of these visits are missed and a lot of this early sign has been overlooked. So to address this in equitable access to actually to clinical assessment and a lot of practical constraints, we are trying to to make a home based a I guided in monitoring tools that can track early motor function development very unobtrusively, like just a video that is watching like a baby monitor is rolling and then be the process this video on the back end and track this specific developmental sign and hopefully be we help for the early detection of autism.   00;09;13;02 - 00;09;40;15 I want to also point the fact that it's actually important, very important and crucial to have timely detection in the autism case, because early intervention, it's actually shown that is most effective before the age of four. Yet the average age of autism diagnosis is still around four and a half. So we are hoping to make a clear detection tools better intervention outcome.   00;09;40;18 - 00;10;00;06 It's really interesting to me that body symmetry is a hallmark of development. I guess my question is why would that be and how is Body Cemetery being addressed in your research? That's a very good question. So we are as I mentioned, a motor development is very important. If early signs offer any visible sign of something that may not working out right.   00;10;00;09 - 00;10;32;14 So one interesting aspects of motor function that has been identified as an indicator of neurodevelopmental health is body symmetry. You can imagine that symmetrical movements and posture are crucial for supporting independent movements such as sitting, crawling and walking, especially infant. Then an infant is typically developing movement posture. Actually, you start asymmetric and then gradually they become more symmetrical as our sensorimotor coordination develops.   00;10;32;16 - 00;11;05;06 And during the first year of life, infants could go through the various milestones, such as days rolling over, sitting up, standing so little by little watching, and all of these movement progressed from less symmetric to more symmetric movement and then also study, they have been looking at the infant movement. They have a map showing that the position is symmetry in their movement can be indication of disorders like autism.   00;11;05;09 - 00;11;28;09 However, if we want to have motor functional function assessment in infant, especially body symmetry in larger scale for a long period of time, our for health care provider is going to be very expensive. I mean, somehow impossible and very challenging because imagine if you have 10 hours of videos, how long does it take for you to watch that?   00;11;28;09 - 00;11;54;10 10 hours. I mean, it's going to take 10 hours. But what we want to do, we want to have these computer vision tools apply on these videos to automatically evaluate them all to a function and is start having something in home that people can use and start escorting to one of the mutual developmental indicators, escorting them the symmetry.   00;11;54;12 - 00;12;23;06 So the idea is that we are actually using infant pose estimation algorithms that we have already developed in the lab to assess postural asymmetry based on differences in joint angle between opposing the arms, between the left side and right side. So the effect the the difference is more than 45 degrees, which has been suggested by Esposito in this study in 2009, in the we can call it asymmetric.   00;12;23;12 - 00;12;50;15 We have also come up with our own measure, which is a data learned based assessment on using Bayesian assets to collect aggregation that we could actually come up with two different angles. But how that these are all allows us to do to process the beat you automatically. And then the video is called the whole movement of the infants based based on all of this processing symmetric or asymmetry.   00;12;50;15 - 00;13;12;01 And then physicians can look at that and see that it is something alarming or not. And then as the process of the science and research goes on, well, I've talked to enough researchers to know that recruiting is usually a challenge for any experiment. But with this, the target population is children like babies. How did you manage to get your patient population?   00;13;12;01 - 00;13;39;15 Were there any privacy, access or ethical concerns? It's a very good question and also absolutely an important matter. When recruiting for our experiment, we noticed that the challenge of targeting infants subject under the age of one, parents are already overworked, sleep deprived, and imagine asking them to to be part of yet another task. So it's very hard, however, to be able to overcome this this problem.   00;13;39;18 - 00;14;16;20 We leverage the fact that many parents already are using baby monitoring systems, so they just want to wash them. I mean, a lot of these baby monitors, even the one that they call smart, they don't do anything. It's just a trigger. If the mat the baby's crying or they are moving. So we are aiming to develop this normal system that not only allow the parents to observe the child, but also offers this long term monitoring capability to track the child's developmental process and provides alert if some abnormalities are detected.   00;14;16;26 - 00;14;38;14 So this may be a good incentive for for parents to take part in our study. And as one of the points that you mention about the privacy and ethical concern, we have taken several measures to make sure to address these concerns. We are collaborating with health care professional that they are more familiar with to dealing with the human subject.   00;14;38;17 - 00;15;15;14 And also we are working closely with a Northeastern Institutional Review board known as IAB to make sure our data collection protocol has strict security and privacy standard. We make sure that the parents that they are participating in our study are fully informed about the purpose of the research. And also we get they consent to to use some some part of these data for public use and public release for scientific and technological advancement, because a lot of them these days, how to win is shared in other a study can be built on top of that.   00;15;15;14 - 00;15;37;19 So but we make sure that parents are that the parents that they are part of this study, they are they are aware, fully aware of that. And I want to emphasize that our priority is to preserve the privacy and confidentiality of them, the participant to out the whole process, although they are looking and working on very important and impactful research.   00;15;37;19 - 00;16;05;12 QUESTION But this is also very important at the top of our list. Yes, security and privacy data for data that is important. Is that why a tech concern like Oracle Cloud that obsesses over things like privacy and security kind of speeds up the research? That's very good. Good point that you brought up. That's true. As I mentioned, security and privacy of the data, especially in our field based on the sensitive nature of data that we are collecting, is important.   00;16;05;16 - 00;16;50;21 We are working with them with personal health related information. So we required some sort of robust measure to to protect confidentiality and prevent unauthorized access. And working alongside part industry partners like Oracle ensures that we are actually having a huge safeguard on our sensitive information. The team that I am working with, Oracle has this huge expertise in data management and security practices, and this allows us to then when we are storing, processing and analyzing data in a in a protected environment, we can focus on our research objective while having a partner that gives us confidence in the security and privacy of the data that they are handling.   00;16;50;21 - 00;17;22;04 So it's a very useful and necessary collaboration. So your lab Augmented Cognition Laboratory or the A.C. Lab works with Computer Vision and machine learning. How did that lab come to be and what exactly is augmented cognition? This is actually brings back many fond memories for me, I think. Tell you the story behind the name, Why I was interested in physics, computers, math, and even literature.   00;17;22;04 - 00;17;53;11 I mean, this is specific. Interest by itself can be another podcast session, but not now. I always had a vision of becoming a university professor and leading my own research lab. I remember clearly that I wasn't seen earlier for my Ph.D. when I started to look at look for names for my future lab to reflect the into intersection of engineering inspired artificial intelligence because I was farming, doing school and data analytics.   00;17;53;18 - 00;18;28;25 But also I wanted to emphasize the positive impact of A.I. in human life rather than replacing them. So I came up with the name Augmented Cognition. Augmented Cognition. I actually represent the core idea that I have about enhancing human information processing capability through the design of adaptive interfaces guided by A.I. algorithm, especially machine learning and computer vision. This is specific definition is actually opening of my my web page when I started at my my position at Northeastern University.   00;18;28;28 - 00;18;59;00 This also highlights my focus on utilizing these advanced tools to augment human ability, especially in the data processing domain. Imagine what I'm doing here as part of my NSF CAREER and what I want to to give physician parents the power of processing hours and hours of data and then let them to extract the information that is needed to to make sure to make the informed decisions.   00;18;59;02 - 00;19;23;13   I often have this phrase that at the ACLab we use artificial intelligence or AI to do human intelligence amplification or IEEE. So I do more Iot and A.I.. Your work relies a lot on machine learning and computer vision as tools to generate truly augmented intelligence solutions. How do you leverage the recent advancement of AI in your work?   00;19;23;13 - 00;20;02;06 Because you've probably been watching it for years, but for most of the public, this A.I. thing came on like a tidal wave. So how does that get applied to computer vision? That's true. I mean, I it's the main wave, and I believe in my my opinion that the main a wave and also success is started from with the introduction of deep learning in 2012 2015 and the actually expand the recent advancement in AI to tackle challenges in understanding and predicting human behaviors from vision sources.   00;20;02;06 - 00;20;43;22 As I said, images or videos, I am focused my my work focus on representation learning in visual perception problems such as object detection, tracking and action recognition and using all of these these tools, we want to estimate the physical, physiological or even emotional states of the individual under study. So to be able to do a robust estimation, the algorithms that we are developing at the Sea Lab utilizes this concept called Pose, which is a low dimensional embedding that captures the essential information in the state of the human that we are monitoring.   00;20;43;28 - 00;21;10;14 For example, body pose, facial pose. You could imagine that you could from that to you can get body symmetry, you can get the emotional feeling of the the human. So help me that I want to emphasize the fact that many of these human data focus application that I work on belong to this small data domain. But the data collection and labeling are limited or restricted, such as healthcare application or even military application.   00;21;10;21 - 00;21;42;26 So to address the data limitation, my algorithm also integrate explicit domain knowledge into the learning process through the use of a generative AI model. We actually built our genitive AI model that this model, they are all data efficient machine learning while incorporating valuable insight from domain experts. So this allows us to to use less data. But on the other hand, we have all of these backing from from the experts that allows us to to make our model work.   00;21;43;04 - 00;22;18;24 This means collaborating with professionals from various fields such as physicians, psychologists, even physicians and neuroscientists are very much important and ensures the practical relevance of many of the models that we are developing in the lab. I definitely see use cases for improving health care and data analysis and augmentation. But for the clinical space, are you a let's go for it person when it comes to AI or more of a cautious person and there is a responsible way to apply, I think that your question comes from all of these debates happening.   00;22;18;24 - 00;22;43;25 Is AI for good or for bad? I mean, what we do, to be honest as a researcher working at the intersection of AI and health, I have been trying to keep a balanced perspective on this overall impact of AI. I am an optimistic optimist when it comes to the potential benefit of AI for health care, particularly for the data analysis and intelligence augmentation.   00;22;43;25 - 00;23;05;06 As the name of my lab, we then come back. I believe that A.I. has the potential to change the healthcare and improve diagnosis, personalized treatment, enhancing patient care, and expanding access to care, as I mentioned. I mean, you can actually make an air power system at your home and get the monitoring and the diagnosis that that you need.   00;23;05;08 - 00;23;35;10 And it can help clinician to make more accurate and timely decision leading to better outcomes for patient health. There is not that I'm just only say is the best and now we don't need to to think about other aspects. I also approach the use of AI in the clinical space, especially with caution. We have to be concerned and to address this concern related to privacy, security and ethical use.   00;23;35;12 - 00;24;02;29 We have to be transparent and accountable and ensure that a AI system are fair, unbiased and trustworthy. These are useful for on on human subject. So proper validation and rigorous testing are necessary to make sure these models are reliable and robust. Also, it's very essential to involve health care professionals, patient and other a stakeholder in the development process.   00;24;03;05 - 00;24;29;20 It cannot be inside AI sitting the lab and come up with something as okay, this is perfect. Let's so let's put that in every baby monitor around the world. We have to make sure the system is safe. A specific needs in inside the health care domain. So in one sentence, I believe that with responsible development and implementation, AI has the potential to significantly improve improved health care outcome.   00;24;29;22 - 00;24;59;11 And I'm hoping this balance will that of you, especially in the clinical setting, allows us to to work more to make better and stronger and more robust AI model while addressing the concern and challenges that comes with its use in the clinical space. Well, I know based on what you said, and because I cheated and researched you before you came on the show, that you you believe that AI, as long as it's good, should be able to augment our capabilities.   00;24;59;11 - 00;25;24;04 And again, you're saying not replace human capability, but augment capabilities. So as you mentioned, the average age of detection for autism is about four and a half years olds. How much and you mentioned about one year old, that's how much sooner than that you think the research could detect autism. And if you do detect it that much earlier, then what Can we actually improve developmental growth?   00;25;24;06 - 00;25;54;17 So before I proceed, I want to make it clear that I don't have any formal academic training in the health care domain. Power through my extensive collaboration and engagement, I have come to understanding the significance of the early detection in neurodevelopmental conditions such as autism, and also how timely intervention can improve the developmental outcome. So as you mention and that's right, the current average age of autism detection is around four and a half years.   00;25;54;20 - 00;26;27;02 But through our research, we want to aim to significantly reduces this age and we are hoping to make it on the age of one because we are able to detect this specific neurodevelopmental model signs unobtrusively, automatically and long term using our computer vision algorithm. And let's remember that the fact that the brain exhibits its highest level of neural plasticity during the first year of life.   00;26;27;04 - 00;27;14;09 So intervening during this sensitive window can have profound impact on long term. So the sooner that we can catch some of these not neurodevelopmental disorder, then the rehabilitation can start. And also intervention can be much more accurate. Also detecting a system that can track and quantify infant development aside from autism can can be used to detect and test other hypotheses related to a motor function hypothesis that based on my collaboration with other health care professionals related to this, a liberal policy congenital tool to coalesce list out that all of this stuff that has some motor representations, but they are not catch early.   00;27;14;09 - 00;27;43;09 You know, because infants are at home. Parents are especially new. Babies have a lot of work so they they missed a sign and then the number of visit is very limited if not missed. So by advancing the age of detection and enabling early intervention, I am not only hoping to have the individual outcome, but also the whole idea is studying other and testing other hypotheses in their developmental science.   00;27;43;09 - 00;28;12;17 So hopefully that would be a tool that empower researcher, physician parents in the field to study these motor related developmental condition much earlier and less expensive and much more on up to the CV. Well, research does need data for exploration and reproducibility, but a lack of data sharing in the research community is kind of a hot topic. There are several people that just doesn't want our collective knowledge to collect.   00;28;12;20 - 00;28;48;13 So why is data sharing vital to advancing science and getting to new discoveries and treatments? For sure, I'm not among those group that they don't share. I think I believe the data sharing plays a very important role in advancing scientific research. So essential for reproducibility, transparency and collaboration. So by sharing data research, it can not only validate what you have done, reproduce that, but also they can build upon your finding and start building new and new discoveries.   00;28;48;15 - 00;29;14;03 So rather than everybody start from scratch. So sitting on your data and not sharing that, it's I don't see that is a scientific manner. This is very fundamental. We do, we do actually share the data on both the data and code in our lab, in the computer science and engineering field is is known that people share data. They could, but in the medical domain, this data is very protected.   00;29;14;06 - 00;29;44;06 And it's I understand all of their privacy consent. But in our data collection procedure, we make sure that we inform at the participant about the value of data sharing. So we get they consent to share these data is pieces of the video that they are collecting. And then I am hoping that collectively we can add best knowledge, at least address complex challenges related to data specific types of a question that we are addressing.   00;29;44;06 - 00;30;16;28 And ultimately we want to improve human health and well-being well-being and enhance the quality of life for everybody. Do you think some of that reluctance has to do with concerns about intellectual property and researchers thinking about, you know, the marketability of what they're doing? Absolutely. Absolutely. That's the case. But I have a counter argument for that. So this is not 2000 years ago that we we come up with an idea and write it down and then buried so nobody can find it after after us.   00;30;17;00 - 00;30;40;22 So I think by sharing with the acknowledgment of that there the research and who came up with that is important. But if we keep this strain of sharing thoughts, sharing ideas, sharing data, which data nowadays holds a lot of intelligence insight inside that, then we can actually build and everybody get into the training of the is Discovery new discovery.   00;30;40;29 - 00;31;13;07 So if we want to keep that it's possible and then in industry because now the line between industry and academy is not as the strict as before because there are a lot of collaboration happen which we're very much I admire. But yeah, we have to to make sure to acknowledge both sides, industry and academics, to acknowledge their contribution, but then share the data and see and be happy on the growth, be happy about advancing the knowledge and the complex problem cannot be solved if we just keep it to ourselves.   00;31;13;09 - 00;31;41;09 Well, our audience of researchers is pretty bright. So is there anything else you'd kind of like them to know or for them to think about that we haven't touched on yet? Just something that you wish people paid a little bit more attention to. Oh, thanks for asking. Yes, I think that this in this podcast you talk about my research related to the use of AI in computer vision for for autism.   00;31;41;11 - 00;32;07;08 A study, as I said, that I don't have any any health care background. However, in my my lab doesn't only work on the autism patients, we are actually interested in developing computer vision and machine learning solution for a wide range of application dealing with the small data problem. The data, it's the the bread and butter of us because the intelligence, especially in the era of deep learning, it's all hidden in the data.   00;32;07;10 - 00;32;34;07 So I work on the rehabilitation, animal monitoring, even autonomous driving scenarios that is hard to collect. Data is expensive, is dangerous to collect data or is impossible. Sometimes, for example, it's very hard to to collect data from animal in this specific pose or conditions. So that's one thing that's enabling these advancements, especially advancement in computation and machine learning in this small little domain is important.   00;32;34;09 - 00;33;05;00 So rather than to do not be afraid or shy, if you think that, okay, this specific application needs a lot of detail, we don't have that. So let's not use let's abandon all of these advancement that we have because we don't have a lot of data. No, it's possible. And in our lab we are working on that to enable these advancement in the domain that rather than having millions and millions of sample, you have only 100 samples, you have only 20 samples of that in Central and all that.   00;33;05;02 - 00;33;28;04 So in my lab we are looking at the problem time to size. First we want to see that if we can make our machine work with less amount of data as I mentioned earlier, how we can do that, we should actually make research a space for the parameters of the model, make it more constrained by bringing some outside domain knowledge inside the model.   00;33;28;07 - 00;33;47;08 So rather than be say that, look, I don't want to hear anybody else's idea. I just want to look at the data and see what's happening. We only take them. They are data driven models. We are putting in some understanding of about the physics, about this specific phenomenal behind that, about the specific types of movement that we are looking for into the model.   00;33;47;13 - 00;34;15;21 So to make the model work with a less amount of data. On the other hand, we we were thinking about this in digital expanded this data is called synthetic data generation. So we are looking at a lot of simulators, even game engines, to see that if we can use them and make an avatar of infant, for example, fall from the bit better than looking at videos or waiting for infant fall of the bit, we actually see that picture can be simulated.   00;34;15;21 - 00;34;35;10 These data can be simulated driving in a very low trouble stability environment rather than asking actually a driver to go to do that. So these are also use of their simulators and synthetic data generation. So we expand the data as much as we can in the synthetic domain. And also we make our model to work with less amount of data.   00;34;35;16 - 00;34;55;27 So hopefully in future we are not abandoning this specific application and the use of AI in there because we don't have data. And if our audience does want to learn more about you or your research or the lab, is there any way they can do that or get in touch with you? Yes. My email, I'm actually very fast and responding to email.   00;34;56;00 - 00;35;41;19 You can find my email at my web page.  And also you can find me a LinkedIn, send me a message there we we share our news in different platform but yeah the best way contacting me send me an email we do have them also even high schooler at our school right now that I'm talking with you Mike I have three high schooler they are collecting data from an avatar in fact in completely virtual world and they are just we are we want to use that to train our model to detect how intense to reach and grasp.   00;35;41;21 - 00;38;06;24 Gosh, that's great. So, Sarah, thank you so much for being on the show with us today. And to help people find you, I'm just going to spell your last name for them. It's Ostadabbas. So that's the way you can look up Sarah. And if you are interested in how Oracle can simplify and accelerate your research, check out Oracle dot com slash research and join us next time on Research in Action.  

Self-confessed contrarian thinker and provocateur since childhood, Martin Lindstrom is an international branding expert and one of the world's leading authorities on the metaverse. Here he joins Geoff Tuff of Deloitte and Des Dearlove of Thinkers50 in a fascinating conversation about branding as an emotional construct, the power of small data, and lying on a bed of Lego.In the 1990s, Lindstrom pioneered how to build brands on the internet and has since coined terms such as clicks & mortar, contextual marketing, and texting. He has published eight New York Times and Wall Street Journal bestsellers, including Buyology (2008), Small Data (2017), and The Ministry of Common Sense (2021).In 2022, Lindstrom launched the “Engineering our Dreams” project – a $22,000,000 metaverse experiment, with the multi-pronged purposes of understanding human behavior, the role of businesses, brands, work environments, and ethical standards in virtual worlds. Co-founder of several multi-billion-dollar startups, including YellowPages.com and Hitwise, Lindstrom is a Thinkers50 Ranked Thinker, listed by TIME magazine as one of the world's 100 most influential people, and named by LinkedIn as 2021's most influential business thinker in the USA. He is the founder and chairman of Lindstrom Company.This podcast is part of an ongoing series of interviews with executives. The executives' participation in this podcast are solely for educational purposes based on their knowledge of the subject and the views expressed by them are solely their own. This podcast should not be deemed or construed to be for the purpose of soliciting business for any of the companies mentioned, nor does Deloitte advocate or endorse the services or products provided by these companies.

There's a lot of debate on big and small data. For systems and compute, some say "Big Data is Dead", while others challenge this notion. In AI and ML, big tech companies can pour tons of money and data into building massive LLMs, while open source provides compelling "small data" alternatives to the LLM walled gardens.So which is it? Will Big Data reign supreme or will small data become more popular? Matt and I riff on these topics and more.#data #dataengineering #chatgpt #ai #bigdata

Martin Lindstrom is the founder and chairman of Lindstrom Company, a global branding & culture transformation firm, operating across five continents and more than 30 countries. He sits down with Dan Pontefract on Leadership NOW to discuss common sense (or the lack of it) in our organizations, in addition to other threads including corporate culture and all things 'corporate nonsense.' TIME Magazine has named Lindstrom one of the “World's 100 Most Influential People,” and for three years running, Thinkers50, has selected Lindstrom to be among the world's top 50 business thinkers. Among the companies he advises are Burger King, Lowes, Boar's Head, Beverly Hills Hotels, Pepsi, Nestle and Google. Lindstrom is the author of seven books including several New York Times bestsellers that have been translated into 60 languages. The Wall-Street Journal praised his book Brand Sense as “one of the five best marketing books ever published,” and his book Small Data as “revolutionary,” and TIME called his book Buyology “a breakthrough in branding.” His latest book is The Ministry of COMMON SENSE: HOW TO ELIMINATE BUREAUCRATIC RED TAPE, BAD EXCUSES, AND CORPORATE BS. Visit http://www.danpontefract.com for more information about Dan and the Leadership NOW program. Visit https://www.martinlindstrom.com/ for more information about Martin Lindstrom.

Are you leveraging the data in your business to solve important problems? Today I am talking with Colette Grail, who is a former Navy pilot and describes herself as a “data and emerging technology geek.”  She is obsessed with ensuring that individuals, leaders, communities, and countries understand data. During her 30 year career as a U.S. Naval Officer, Colette provided important insights for strategic and tactical decisions.  Based on that experience, she wrote a book on making decisions with Big Data.  Colette's book has just been released – the title is:  The Fallacy of Laying Flat. On today's episode, Colette and I discuss the differences between Small Data and Big Data.  She also explains how to solve Big Problems with Big Data. I'm sure you will enjoy my discussion with Colette and the tips and recommendations she shares for your business. “Data needs to be real time and interactive in order to have relevance and context.” – Colette Grail Today on the Tech Leader Talk podcast: - Important differences between big data and small data - What motivated you to write the book: The Fallacy of Laying Flat? - How are big problems solved with big data? - The 3 Vs of big data - Ideas for visualizing data Colette's Book:  The Fallacy of Laying Flat https://www.amazon.com/dp/B0BPGGF753 Connect with Colette Grail: LinkedIn:  https://www.linkedin.com/in/colettegrail/ Website:  http://whatsthebigdataidea.com/ Thanks for listening! Be sure to get your free copy of Steve's latest book, Cracking the Patent Code, and discover his proven system for identifying and protecting your most valuable inventions. Get the book at https://stevesponseller.com/book.

https://www.patreon.com/datameshradio (Data Mesh Radio Patreon) - get access to interviews well before they are released Episode list and links to all available episode transcripts (most interviews from #32 on) https://docs.google.com/spreadsheets/d/1ZmCIinVgIm0xjIVFpL9jMtCiOlBQ7LbvLmtmb0FKcQc/edit?usp=sharing (here) Provided as a free resource by DataStax https://www.datastax.com/products/datastax-astra?utm_source=DataMeshRadio (AstraDB); George Trujillo's contact info: email (george.trujillo@datastax.com) and https://www.linkedin.com/in/georgetrujillo/ (LinkedIn) Transcript for this episode (https://docs.google.com/document/d/101DSo19l1KiocNNX7JFgbL6X2illb6lLjuZs5dEXXCI/edit?usp=sharing (link)) provided by Starburst. See their Data Mesh Summit recordings https://www.starburst.io/learn/events-webinars/datanova-on-demand/?utm_campaign=starburst-brand&utm_medium=outbound&utm_source=&utm_type=&utm_content=dmradiodnvid&utm_term= (here) and their great data mesh resource center https://www.starburst.io/info/distributed-data-mesh-resource-center/?utm_campaign=starburst-brand&utm_medium=outbound&utm_source=&utm_type=&utm_content=dmradiodmcenter&utm_term= (here). You can download their Data Mesh for Dummies e-book (info gated) https://starburst.io/info/data-mesh-for-dummies/?utm_campaign=starburst-brand&utm_medium=outbound&utm_source=&utm_type=&utm_content=dmradiodnvid&utm_term= (here). Carlos' LinkedIn: https://www.linkedin.com/in/carlos-saona-vazquez/ (https://www.linkedin.com/in/carlos-saona-vazquez/) In this episode, Scott interviewed Carlos Saona, Chief Architect at eDreams ODIGEO. As a caveat before jumping in, Carlos believes it's too hard to say their experience or learnings will apply to everyone or that he necessarily recommends anything they have done specifically but he has learned a lot of very interesting things to date. Keep that perspective in mind when reading this summary. Some key takeaways/thoughts from Carlos' point of view: eDreams' implementation is quite unique in that they were working on it without being in contact with other data mesh implementers for most of the last 3 years - until just recently. So they have learnings from non-typical approaches that are working for them. You should not look to create a single data model upfront. That's part of what has caused such an issue for the data warehouse - it's inflexible and doesn't really end up fitting needs. But you should look to iterate towards that standard model as you learn more and more about your use cases. ?Controversial?: Look to push as much of the burden as is reasonable onto the data consumers. That means the stitching between data products, the compute costs of consuming, etc. They get the benefit so they should be taking on the burden. Things like data quality are still on the shoulders of producers. You should provide default values for your data product SLAs. It makes the discussion between consumers and producers far easier - is the default good enough or not? ?Extremely Controversial?: At eDreams, you cannot publish data in your data product that you are not generating. In derived domains (e.g., customer history), “generate” includes the derived stitching. NOTE: Go about an hour into the interview - not episode - for more specifics. When starting with data mesh, there must be a settling period - consumers must understand that things are subject to change while a new producer really figures things out for the first few weeks to months. You want to avoid duplicating data. But you REALLY want to avoid duplicating business logic. Be careful when selecting your initial data mesh use cases. If the use case requires a very fast time to market, while it has value, you likely won't have the time and space necessary to experiment and learn. You need to find repeatable patterns to scale in data mesh. Hurrying is a way to miss the necessary learning. Look ahead and build...

Today, at our show we have Christopher Nguyen who is a leader in the AI space and co-founder of Aitomatic. You will enjoy the conversation as we geek out with Christopher and cover the following topics: Learn how Chris started his career as a software engineer at HP, worked at Google and now heading Aitomatic. What is Aitomatic and how it's different from other AI players in the market? How human and AI can work together instead of AI replacing humans. How do you think AI will help us scale human productivity? Data is becoming a bigger problem for companies compared to automation. Mostly, it's not about big data but about linking few data sources. How companies can use automation to solve their smaller data problems? What will be the inflection point the world using more AI and Human combination? Where is Aitomatic is heading? Visit www.analyticstodaypodcast.com to listen to more episodes of our show. Leave your feedback on iTunes: https://podcasts.apple.com/us/podcast/analyticstoday-podcast/id1044308732

When we are at a very early stage of the development process, it is very difficult to get big data. In today's episode of the Circuit Breaker Show, we take a look at Big Data to show you how to develop better products. You'll learn why Big Data is not the answer, but only part of the answer.  Bob will talk about design of experiments.  You will discover why it is very important to look at the market in context.  Bob will share the projects they have worked on to help with Big Data. Join us for this riveting discussion. Enjoy! What You'll Learn in this Show: The impact of Big Data on innovation.  Where you can leverage Big Data. Why over-reliance on Big Data is detrimental.  The importance of clustering data rather than segmenting it.  And so much more... Resources: The Rewired Group https://therewiredgroup.com/ (Website) (https://therewiredgroup.com/) Todd Rose | https://www.amazon.com/End-Average-Succeed-Values-Sameness/dp/0062358367 (The End of Average) Thomas Kuhn | https://www.amazon.com/Structure-Scientific-Revolutions-Thomas-Kuhn/dp/0226458083 (The Structure of Scientific Revolutions)

Data only matters when you can use it. So, while many organizations over the last decade have been clamoring for big data at every turn, others are now asking:  How can we do more with less?  Today's guest, Rob Kim, Vice President of Technology Strategy at Presidio joins Raphael Meyerowitz on the show to answer that question — and everything else you should know about the seismic shifts in analytics in recent years. Join us as we discuss: Why Big Data is becoming Small Data Why standardization and simplification are the keys to moving faster The rise of ransomware and what it means for your organization To hear more interviews like this one, subscribe to The Digital Decode Podcast on Apple Podcasts, Spotify, or your preferred podcast platform.