Podcasts about enterprise data

✨ Heads up! This episode features a demonstration of the SnapLogic UI and its AI Agent Creator towards the end. For the full visual experience, check out the video version on the Spotify app! ✨(Episode Summary)Tired of tangled data spread across multiple clouds, on-premise systems, and the edge? In this episode, MongoDB's Shane McAllister sits down with Peter Ngai, Principal Architect at SnapLogic, to explore the future of data integration and management in today's complex tech landscape.Dive into the challenges and solutions surrounding modern data architecture, including:Navigating the complexities of multi-cloud and hybrid cloud environments.The secrets to building flexible, resilient data ecosystems that avoid vendor lock-in.Strategies for seamless data integration and connecting disparate applications using low-code/no-code platforms like SnapLogic.Meeting critical data compliance, security, and sovereignty demands (think GDPR, HIPAA, etc.).How AI is revolutionizing data automation and providing faster access to insights (featuring SnapLogic's Agent Creator).The powerful synergy between SnapLogic and MongoDB, leveraging MongoDB both internally and for customer integrations.Real-world applications, from IoT data processing to simplifying enterprise workflows.Whether you're an IT leader, data engineer, business analyst, or simply curious about cloud strategy, iPaaS solutions, AI in business, or simplifying your data stack, Peter offers invaluable insights into making data connectivity a driver, not a barrier, for innovation.-Keywords: Data Integration, Multi-Cloud, Hybrid Cloud, Edge Computing, SnapLogic, MongoDB, AI, Artificial Intelligence, Data Automation, iPaaS, Low-Code, No-Code, Data Architecture, Data Management, Cloud Data, Enterprise Data, API Integration, Data Compliance, Data Sovereignty, Data Security, Business Automation, ETL, ELT, Tech Stack Simplification, Peter Ngai, Shane McAllister.

Episode OverviewEnterprise data isn't just about governance anymore — it's about growth, agility, and survival.Malcolm Hawker and Apurva Wadodkar of Autodesk dive into what it takes to build a data function that keeps pace with change, sharing hard-won lessons and forward-looking strategies along the way.Episode Links and ResourcesFollow Malcolm Hawker on LinkedInFollow Apurva Wadodkar on LinkedIn

What should you really be asking during your interview as a tech leader? And once you land the role, how do you manage expectations, reduce technical debt, and make meaningful impact fast?In this episode, Justin Nguyen, Technology Director of Enterprise Data & Analytics at Home Depot, shares hard-won insights from his recent leadership transitions. From assessing team maturity to setting realistic AI expectations, we unpack the tactical and strategic moves leaders need to thrive in the first 180 days of a new role.

Capital One has long been recognized as a digital-first financial services company. Now, it's productizing its tech DNA into standalone B2B software. In this episode, Peter High speaks with Ravi Raghu, President of Capital One Software, about how internal cloud and data security capabilities led to the launch of an enterprise SaaS business—starting with offerings like Slingshot and Data Bolt.

Send us a textIn this insightful conversation with Suzanne El-Moursi, co-founder and CEO of BrightHive, Peter and Dave explore how organizations are addressing the growing gap between data volume and analytical capacity. Suzanne reveals that while 90% of the world's data was created in just the last two years, only about 3% of enterprise employees are data professionals, creating a massive bottleneck where business teams must wait in line for insights from central data teams.BrightHive's solution is an "agentic data team in a box" – seven AI agents that work in unison to handle the entire data lifecycle from ingestion to governance to analytics. Unlike typical AI solutions, these agents operate at the metadata layer to ensure quality, compliance, and meaningful insights without replacing human expertise.The conversation covers compelling use cases across industries – from helping resource-constrained organizations extend their analytical capacity to unifying fragmented data landscapes resulting from mergers and acquisitions. Perhaps most striking is Suzanne's vision for measuring AI's impact through what she calls the "delight KPI" – are employees finding their work more fulfilling when augmented by these tools?Key Takeaways:Data fragmentation persists - Organizations struggle with siloed data across systems, especially after mergers, blocking comprehensive analysis.AI augments human intelligence - "A doctor with AI will displace a doctor without AI" - the goal is removing grunt work so humans tackle higher-value analysis.Measure the "delight KPI" - Track how AI improves job satisfaction by enabling more data-informed work without technical bottlenecks.Cultural shift needs technical solutions AND organizational buy-in to overcome skepticism about AI in the workplace.

In this far-ranging interview, Ajay Kapare discusses the goals and collaborative methods of ELLKAY from his perspective as its new President & CEO.  Kapare is one of the most connected people in healthcare IT and so we were excited to learn about what's next for ELLKAY and their partners with him as the new CEO.ELLKAY provides interoperability solutions, planning to be "anything and everything around data and interoperability." Kapare describes the company as an "enterprise data management" partner, knowing how to get data out of silos. He mentioned their LKOpera service, "healthcare data on demand" which is being leveraged by a wide variety of healthcare organizations.Learn more about ELLKAY: https://ellkay.com/Health IT Community: https://www.healthcareittoday.com/

What is Google Agentspace? Find out on this Cloud and Clear episode with SADA's Kelly Wright. Director of Workspace, and Veronica Raulin, Senior Director, Advisory! We're discussing how Google Agentspace uses AI and Google Search to simplify complex tasks and connect your data. Plus, get a Google Cloud Next 2025 preview and learn about SADA's Proof of Value. Join us for more content by liking, sharing, and subscribing!

With enterprises scrambling to bring in artificial intelligence (AI) and large language models (LLMs), many are learning the importance of clean data and strong data strategy.  In this episode, Amna Jamal from IBM joins host Marc LeBlanc to talk about data strategy in the age of AI. Working with clients to help them address their data and AI challenges, Amna shares a unique perspective on the importance of a well-developed data strategy for AI success within enterprises. Covering everything from practical ways to identify the skill sets you need to achieve data and AI success within your organization to the opportunities AI creates for businesses, Amna and Marc underscore the inextricable link between data and AI. 

I had a great conversation with James Lupton, CTO of Cynozure Group, at the DataDriven Conference, where we explored the evolving data landscape, business strategy, and real-world AI use cases.James shared:✅ How Cynozure is helping organizations navigate data challenges✅ Key lessons for businesses implementing AI-driven data strategies✅ His take on AI-driven decision-making and its enterprise impact✅ One underexplored AI use case with massive potentialSome great insights on making data work smarter for businesses!#data #ai #reltio #datadriven #theravitshow

What does it take to invent a foundational AI paradigm — and then build a company to bring it to the enterprise? In this episode of Founded & Funded, Madrona Partner Jon Turow sits down with Douwe Kiela, co-founder and CEO of Contextual AI and the co-inventor of RAG (Retrieval Augmented Generation). They dive into the origins of RAG, its misunderstood role in the enterprise, and how Contextual is redefining what production-grade AI systems can do. Douwe shares what most companies get wrong about RAG, why chunking shouldn't matter, how to think about hallucinations, and what founders need to know in the era of RAG agents. Transcript: https://madrona.com/rag-inventor-talks-agents-grounded-ai-and-enterprise-impact Chapters: (00:00) Introduction  (01:27) The Origin of RAG (04:00) Challenges and Innovations in RAG (09:49) Enterprise Adoption and Use Cases (20:46) Scaling and Innovations at Contextual AI (23:39) The Future of RAG Agents (24:43) Challenges in Enterprise Data  (26:34) Building a Research-Driven Company (27:55) The Intersection of Research and Product (32:10) Advice for Founders and AI Companies (38:14) Understanding and Addressing Hallucinations (40:50) Company Building is Harder Than You'd Think (42:00) The Importance of Evaluation in AI (44:14) Concluding Thoughts

03-19-2025 Elizabeth Thede Learn more about the interview and get additional links here: https://www.usadailychronicles.com/no-to-a-spring-clean-yes-to-a-search-engine-for-enterprise-data/ Subscribe to the best of our content here: https://priceofbusiness.substack.com/ Subscribe to our YouTube channel here: https://www.youtube.com/channel/UCywgbHv7dpiBG2Qswr_ceEQ

This guide provides a step-by-step framework to assess vendors, align priorities, and make informed decisions about enterprise data and analytics tools. Published at: https://www.eckerson.com/articles/the-buyer-s-guide-to-selecting-the-right-enterprise-data-analytics-tool

What if you could redefine enterprise data management with AI—while boosting productivity by 30%? In this episode of Bringing Data and AI to Life, host Amy Horowitz sits down with Gaurav Pathak, VP of Product Management for AI and Metadata at Informatica, to explore how AI is turning data from a manual headache into an enterprise-wide superpower. As a DataIQ 2025 Data and AI Leader of the Year nominee, Gaurav delivers actionable strategies to help CDOs, CIOs, and data architects scale operations, enhance governance, and harness AI-driven automation. Tune in now to embrace innovation—without sacrificing security or trust.

02-19-2025 Elizabeth Thede Learn more about the interview and get additional links here: https://www.usadailychronicles.com/for-the-dark-days-of-winter-lets-focus-on-dark-enterprise-data/ Subscribe to the best of our content here: https://priceofbusiness.substack.com/ Subscribe to our YouTube channel here: https://www.youtube.com/channel/UCywgbHv7dpiBG2Qswr_ceEQ

I keep hearing data product, data strategy, and UX teams often struggle to quantify the value of their work. Whether it's as a team as a whole or on a specific data product initiative, the underlying problem is the same – your contribution is indirect, so it's harder to measure. Even worse, your stakeholders want to know if your work is creating an impact and value, but because you can't easily put numbers on it, valuation spirals into a messy problem.   The messy part of this valuation problem is what today's episode is all about—not math! Value is largely subjective, not objective, and I think this is partly why analytical teams may struggle with this. To improve at how you estimate the value of your data products, you need to leverage other skills—and stop approaching this as a math problem.   As a consulting product designer, estimating value when it's indirect is something that I've dealt with my entire career. It's not a skill learned overnight, and it's one you will need to keep developing over time—but the basic concepts are simple. I hope you'll find some value in applying these along with your other frameworks and tools.    Highlights/ Skip to   Value is subjective, not objective (5:01) Measurability does not necessarily mean valuable (6:36) Businesses are made up of humans. Most b2b stakeholders aren't spending their own money when making business decisions—what does that mean for your work? (9:30) Quantifying a data product's value starts with understanding what is worth measuring in the eye of the beholder(s)—not math calculations (13:44) The more difficult it is to show the value of your product (or team) in numbers, the lower that value is to the stakeholder—initially (16:46) By simply helping a stakeholder to think through how value should be calculated on a data product, you're likely already providing additional value (18:02) Focus on expressing estimated value via a range versus a single number (19:36) Measurement of anything requires that we can observe the phenomenon first—but many stakeholders won't be able to cite these phenomena without [your!] help (22:16) When you are measuring quantitative aspects of value, remember that measurement is not the same as accuracy (precision)—and the precision game can become a trap (25:37) How to measure anything—and why estimates often trump accuracy (31:19) Why you may need to steer the conversation away from ROI calculations in the short term (35:00)   Quotes from Today's Episode Even when you can easily assign a dollar value to the data product you're building, that does not necessarily reflect what your stakeholder actually feels about it—or your team's contribution. So, why do they keep asking you to quantify the value of your work? By actually understanding what a shareholder needs to observe for them to know progress has been made on their initiative or data product, you will be positioned to deliver results they actually care about. While most of the time, you should be able to show some obvious economic value in the work you're doing, you may be getting hounded about this because you're not meeting the often unstated qualitative goals. If you can surface the qualitative goals of your stakeholder, then the perception of the value of your team and its work goes up, and you'll spend less time trying to measure an indirect contribution in quant terms that only has a subjectively right answer. (6:50) The more difficult it is for you to show the monetary value of your data product (or team), the lower that value likely is to the stakeholder. This does not mean the value of your work is “low.” It means it's perceived as low because it cannot be easily quantified in a way that is observable to the person whose judgment matters. By understanding the personal motivations and interests of your stakeholders, you can begin to collaboratively figure out what the correct success metrics should be—and how they'd be measured. By just simply beginning to ask and uncover what they're trying to measure, you can start to increase your contributions' perceived value. (17:01) Think about expressing “indirect value” as a range, not a precise single value. It's much easier to refine your estimate (if necessary) once a range has been defined, and you only need to get precise enough for your stakeholder to make a decision with the information. How much time should you spend refining your measurement of the value? Potentially little to none—if the “better math” isn't going to change anyone's mind or decision.  Spending more time to measure a data product's value more accurately takes you away from doing actual product work—and if there isn't much obvious value to the work, maybe the work—not the measurement of the work—needs to change. (19:49) Smart leaders know that deriving a simple calculation of indirect contributions is complex—otherwise, the topic wouldn't keep coming up. There is a “why” behind why they're asking, and when you understand the “why,” you'll be better positioned to deliver the value they actually seek, using valuation measurements that are “just enough” in their precision. What do you think it says to a stakeholder if you're spending an inordinate amount of time simply trying to calculate and explain the value of your data product? (23:22) Many organizations for years have invested in things that don't always have a short term ROI.  They know that ROI takes time, and they can't really measure what it's worth along the way. Examples include investments in company culture, innovation, brand reputation, and many others. If you're constantly playing defense and having to justify your existence or methods by quantifying the financial value of your data products (or data product management team, or UX team, or any other indirect contributor/contribution), then either your work truly does lack value, or you haven't surfaced what the actual success metrics and outcomes are— in the eyes of the stakeholder. As such, the perceived value is “low” or opaque. They might be looking for a hard number to assign to it because they're not seeing any of the other forms of value that they care about that would indicate positive progress. It's easier to write [you] a large check  for a big, innovative, unproven initiative if your stakeholders know what you and your team can accomplish with a small check. (35:16)   Links Experiencing Data: Episode 80 with Doug Hubbard

In this episode we are looking at an area which impacts every business in the world. Unstructured data - that is, how we can start to squeeze insight from the piles of text, audio, video, and every other type of data that doesn't fit into a neat table.Carefully analysed, it can contain valuable insight, to be compared against other more traditional metrics such as sales figures, or economic results.Joining us to discuss is Gokul Sathiacama, VP of data storage for AI at Hewlett Packard Enterprise.This is Technology Now, a weekly show from Hewlett Packard Enterprise. Every week we look at a story that's been making headlines, take a look at the technology behind it, and explain why it matters to organizations and what we can learn from it. About this week's guest, Gokul Sathiacama: https://www.linkedin.com/in/gokuls/Sources cited in this week's episode:Statistics on global data generation: https://www.statista.com/statistics/871513/worldwide-data-created/Statistics on global IOT devices: https://paxtechnica.org/?page_id=738#:~:text=%E2%80%9COur%20IoT%20world%20is%20growing,billion%20by%202020.%E2%80%9D%20Intel.&text=Gartner.&text=Cisco.,-2011&text=%E2%80%9CGlobal%20M2M%20connections%20will%20increase,at%20the%20end%20of%202022.Global Web Index stats on smart devices: https://www.globalwebindex.net/

In this episode we are looking at an area which impacts every business in the world. Unstructured data - that is, how we can start to squeeze insight from the piles of text, audio, video, and every other type of data that doesn't fit into a neat table.Carefully analysed, it can contain valuable insight, to be compared against other more traditional metrics such as sales figures, or economic results.Joining us to discuss is Gokul Sathiacama, VP of data storage for AI at Hewlett Packard Enterprise.This is Technology Now, a weekly show from Hewlett Packard Enterprise. Every week we look at a story that's been making headlines, take a look at the technology behind it, and explain why it matters to organizations and what we can learn from it. About this week's guest, Gokul Sathiacama: https://www.linkedin.com/in/gokuls/Sources cited in this week's episode:Statistics on global data generation: https://www.statista.com/statistics/871513/worldwide-data-created/Statistics on global IOT devices: https://paxtechnica.org/?page_id=738#:~:text=%E2%80%9COur%20IoT%20world%20is%20growing,billion%20by%202020.%E2%80%9D%20Intel.&text=Gartner.&text=Cisco.,-2011&text=%E2%80%9CGlobal%20M2M%20connections%20will%20increase,at%20the%20end%20of%202022.Global Web Index stats on smart devices: https://www.globalwebindex.net/

In this episode we are looking at an area which impacts every business in the world. Unstructured data - that is, how we can start to squeeze insight from the piles of text, audio, video, and every other type of data that doesn't fit into a neat table.Carefully analysed, it can contain valuable insight, to be compared against other more traditional metrics such as sales figures, or economic results.Joining us to discuss is Gokul Sathiacama, VP of data storage for AI at Hewlett Packard Enterprise.This is Technology Now, a weekly show from Hewlett Packard Enterprise. Every week we look at a story that's been making headlines, take a look at the technology behind it, and explain why it matters to organizations and what we can learn from it. About this week's guest, Gokul Sathiacama: https://www.linkedin.com/in/gokuls/Sources cited in this week's episode:Statistics on global data generation: https://www.statista.com/statistics/871513/worldwide-data-created/Statistics on global IOT devices: https://paxtechnica.org/?page_id=738#:~:text=%E2%80%9COur%20IoT%20world%20is%20growing,billion%20by%202020.%E2%80%9D%20Intel.&text=Gartner.&text=Cisco.,-2011&text=%E2%80%9CGlobal%20M2M%20connections%20will%20increase,at%20the%20end%20of%202022.Global Web Index stats on smart devices: https://www.globalwebindex.net/

Data protection…simplified? Hosts Patrick Moorhead and Daniel Newman are joined by Cohesity's CRO, Kit Beall and Veritas' EVP, Worldwide Field Operations, Brian Hamel to discuss the monumental merger of their enterprise data protection businesses from a sales, customer focus, and integration perspective on this episode of Six Five On The Road.  Tune in to learn why this is big

Not everyone agrees on what an “AI agent” actually is, but they are all still the rage. At a broad level, these so-called “agents” promise to go several steps beyond a mere chatbot, making decisions and taking actions on people's behalf. Learn more about your ad choices. Visit podcastchoices.com/adchoices

How can you future-proof your collocation? Start by combining advanced AI hardware with global data center capabilities. On this episode of Six Five On The Road at SC24, hosts Keith Townsend and David Nicholson are joined by Lenovo's CMO Flynn Maloy and Digital Realty's Chief Executive of Alliances - Global Sales, Seamus Matthews for a conversation on how their collaboration addresses the increasing demand for #AIinfrastructure and sustainable data center solutions.  Watch the full video for more on

A discussion with Mabby Amouie, Assistant Vice President, Enterprise Data & Analytics, Norfolk Southern. He has advanced from his initial data scientist role to a senior leadership role in just over a decade. Mabby talks about how his industrial engineering background influences his approach to analytics. He then dives into how he always wants to know what's happening under the hood and has to fight his desire to make solutions over-optimized and too perfect. He also discusses the importance of building a professional network and establishing trust with business partners. He finishes with a few predictions related to autonomous vehicles, productivity, and data management. #analytics #datascience #ai #artificialintelligence #generativeAI #rail #transportation

In this episode, Carlos Gonzalez de Villaumbrosia interviews Andrew Davidson, Senior Vice President of Product at MongoDB.MongoDB is the most used modern database, powering some of the largest companies across industries like financial services, healthcare, and manufacturing. Originally launched as a free open-source project in 2009, MongoDB went public in 2017 and has grown into a $20Billion dollar enterprise data platform. In this episode, we'll discuss how to incentivize team members to grow within the same company for a long period of time, the key product milestones in MongoDB's transformation, nurturing a developer ecosystem, using AI to accelerate code deployment and scaling in the cloud, building 3rd-party integrations with over 100 partners, and allowing companies to integrate with MongoDB as they create their own data stack.Key Takeaways

This week, Chitrang Dave, Global Head of Enterprise Data & Analytics at Edwards Lifesciences, joins us to discuss the transformative power of real-time data, AI, and collaboration in medical device manufacturing and support.  He and host, Ross Katz, dive into how real-time data from IoT devices is reshaping quality assurance in medtech and what the future holds for medtech as big tech players like Apple and Meta enter the healthcare arena.  Together, they discuss everything from AI-powered patient identification to the integration of consumer wearables with FDA-approved medical devices. Tune in to hear how collaboration, innovation, and cutting-edge technology are improving patient outcomes and revolutionizing healthcare. Data in Biotech is a fortnightly podcast exploring how companies leverage data innovation in the life sciences. Chapter Markers [01:36] Chitrang shares the experience that led him to work at leading data and analytics organizations and what work there is to be done [04:09] Chitrang highlights the role of IoT devices in medical device manufacturing, where real-time data can drive automation and improve quality assurance [06:25] What is driving innovation right now in research and development, and how companies like Apple are disrupting the medical device space  [09:23] Chitrang talks about how connectivity in devices and the expectation of the user to be able to use an intuitive interface are evolving into more real-time medical device technology  [11:47] The importance of keeping patient data private between the patient and the practitioner while using anonymized data to create solutions and identify patterns in health  [13:25] Using data to create a complete picture of the patient in order to make their life easier  [14:20] Chitrang discusses the challenge of manufacturing medical devices when there are issues with raw materials  [16:30] Chitrang discusses the potential for automation for real-time data in manufacturing [19:17] Ross and Chitrang discuss the value of having comprehensive data to personalize treatments and ensure timely responses, especially for scenarios where early detection of Alzheimer's can save trillions of dollars [21:27] Chitrang mentions significant collaborations, such as the Cancer AI Alliance, where tech giants like AWS, Microsoft, NVIDIA, and Deloitte are working together to address critical problems in healthcare [27:10] How real-time data from medical devices could improve patient outcomes, stakeholder coordination and future trends  [28:29] Closing thoughts and where to find Chitrang Dave online  Download CorrDyn's latest white paper on “Using Machine Learning to Implement Mid-Manufacture Quality Control in the Biotech Sector.” Find the white paper online at: https://connect.corrdyn.com/biotech-ml

Ensuring your organization can quickly adapt to changing pressures is critical in today's fast-moving market environments. However, many organizations are still structured with traditional hierarchies, which enable stable operations but can make change difficult.This week, Dave, Esmee, and Rob talk to Tony McManus, Global Head of Enterprise Data and Index at Bloomberg, about tracking and responding to mega-trends, Bloomberg's collaborative structure, supporting customers on their cloud journeys and how this helped prioritize Bloomberg's Cloud strategy. TLDR 01:15 Confused about lost data and knowledge 05:05 Cloud conversation with Tony McManus, Bloomberg 38:10 Michael Porter's five forces model 46:40 Going to Taipei! GuestTony McManus: https://www.linkedin.com/in/mcmanustony/HostsDave Chapman: https://www.linkedin.com/in/chapmandr/Esmee van de Giessen: https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan: https://www.linkedin.com/in/rob-kernahan/ProductionMarcel van der Burg: https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman: https://www.linkedin.com/in/chapmandr/SoundBen Corbett: https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:  https://www.linkedin.com/in/louis-corbett-087250264/'Cloud Realities' is an original podcast from Capgemini

Purvi Shah is Vice President of Product Development for Enterprise Data Platforms at American Express. Prior to that she led a team of consultants at Deloitte in the public sector working with many state governments to build solutions for citizens to avail services for food stamps, child care and other interventions. Purvi was recently recognized on the 2024 Global Data Power Women List by CDO Magazine.  In this episode Purvi talks about: (1:05) Product development in the public sector (2:23) Looking at the end-to-end journey beyond the user's problem (4:36) Three important things about empathy in understanding users (6:21) Data challenges of a 170 year old company like American Express (7:43) Three success criteria for Customer-360 programs that strive to build a singular view of a customer (11:27) Applying Newton's first law of motion to data platforms (13:30) Applying Newton's second law of motion to data platforms (14:23) Example of customer delight that comes from having a good data ecosystem (16:43) Key lessons learnt from decommissioning legacy data platforms (22:05) Applying Newton's third law of motion to data platforms (25:26) Metrics to track for enterprise data platforms Connect with Purvi Shah: https://www.linkedin.com/in/purvi-shah-8298451/ Connect with Rahul Abhyankar: https://www.linkedin.com/in/rahulabhyankar Product Leader's Journey: https://www.productleadersjourney.com

Take a Network Break! This week we discuss Microsoft’s proposed deal to buy power from the Three Mile Island nuclear plant, new APs and switches from Juniper Networks, and T-Mobile landing a customer driven by the network slicing capabilities of 5G. China disbands a botnet, Nokia takes on data center switch giants with a new... Read more »

Take a Network Break! This week we discuss Microsoft’s proposed deal to buy power from the Three Mile Island nuclear plant, new APs and switches from Juniper Networks, and T-Mobile landing a customer driven by the network slicing capabilities of 5G. China disbands a botnet, Nokia takes on data center switch giants with a new... Read more »

Take a Network Break! This week we discuss Microsoft’s proposed deal to buy power from the Three Mile Island nuclear plant, new APs and switches from Juniper Networks, and T-Mobile landing a customer driven by the network slicing capabilities of 5G. China disbands a botnet, Nokia takes on data center switch giants with a new... Read more »

Rohit Choudhary's journey from a curious teenager in India to a seasoned entrepreneur and innovator demonstrates the transformative power of technology and entrepreneurial spirit. His latest venture, Acceldata, has attracted funding from top-tier investors like Insight Partners, March Capital, Industry Ventures, Lightspeed, and Sorenson Ventures.

This is DartPoints Podcast #100 for Friday, August 18th, 2024.  In this podcast, I'm accompanied by Brandon Nelson and Josh West from a company called Glean. Glean brings AI intelligence that is trained from your own Enterprise Data.  All this, AND MORE….in the next DartPoints Podcast.  William Sellers: Solutions Architect (DartPoints) Brandon Nelson: Enterprise Account Executive (Glean) Josh West: Solutions Engineer (Glean) The views, thoughts, and opinions expressed in this podcast are the speakers own---and do not necessarily represent the views, thoughts, and opinions of DartPoints or a guest's employer. #DartPoints #DaaS, #Storage, #DataCenter, #Cybersecurity, #TechNews, #DisasterRecovery, #DataProtection, #Enterprise, #InternetPrivacy, #LifecycleManagement, #VMware, #NOC, #NetworkOperationsCenter, #Veeam, #ArtificialIntelligence, #AI, #Glean

Dino Scheidt is an AI Engineer, former CTO and Founder who works with Fortune 50s, Start-Ups, and Governments on Data Intelligence and AI Architectures. In this conversation, we explore the evolving landscape of AI with a particular focus on generative AI and its applications. Dino criticizes the concept of 'AI strategies,' arguing that AI should be seen as a tool rather than a strategy. Despite his initial skepticism towards generative AI, Dino acknowledges its potential, especially in transforming traditional value chains through AI-enabled communications. We also delve into the challenges and opportunities posed by non-deterministic systems, the concept of Data P&L, and the complexity of integrating generative AI into existing business operations. Dino wraps up the discussion by emphasizing the importance of lateral thinking and digital representation of business processes to leverage future AI innovations effectively. EPISODE LINKS: Dino Scheidt LinkedIn: https://linkedin.com/in/dinoscheidt Dino Scheidt Website: https://din.ooo The Gartner Hype Cycle: https://en.wikipedia.org/wiki/Gartner_hype_cycle TIMESTAMPS: 00:00:12 Introduction and background 00:00:45 AI and Generative Models 00:04:36 Deterministic vs Non-Deterministic Systems 00:08:19 Unpredictability, Value Chain, and AI Integration 00:13:26 Hype Cycle: Adjective to Noun 00:19:13 Strategic Integration of Generative AI 00:28:36 Value of Generative AI: Consumer vs. Enterprise Perspectives 00:32:12 Event Storming and Tactical AI 00:39:51 Future of AI and Final Thoughts 00:42:36 Closing CONNECT: Website: https://hoo.be/elijahmurray YouTube: https://www.youtube.com/@elijahmurray Twitter: https://twitter.com/elijahmurray Instagram: https://www.instagram.com/elijahmurray LinkedIn: https://www.linkedin.com/in/elijahmurray/ Apple Podcasts: https://podcasts.apple.com/us/podcast/the-long-game-w-elijah-murray/ Spotify: https://podcasters.spotify.com/pod/show/elijahmurray RSS: https://anchor.fm/s/3e31c0c/podcast/rss

HealthLeaders Innovation Editor Eric Wicklund talks with Dane Hudelson, Senior Director of Enterprise Data & Analytics at Sanford Health and a member of the HealthLeaders Mastermind Program on AI in Revenue Cycle and Finance Operations, about how the health system has built its AI capabilities in-house and developed a strong strategy for future growth and innovation.

In Episode 40, of Season 4, of Driven by Data: The Podcast, Kyle Winterbottom is joined by Greg Freeman, CEO of Data Literacy Academy, Hannah Davies, Head of Data Culture and Excellence at Admiral Group Plc and Kate Jones, Head of Data Product and Strategy at Coventry Building Society as part of a live panel event, where they discuss how to design and deliver an enterprise data literacy programme, from planning to impact, which includes; Understanding the business case for data literacy programmes Who should drive it and whyThe key steps in planning effective enterprise data literacyApproaches to obtaining buy-in and sponsorship How to build a data academy Communicating the value to the wider business Branding people with persona names Ways to measure success Why success metrics will change with maturity Proving the ROI on data literacy initiativesIf there is resistance to the word 'literacy'If structured data literacy programmes can work in smaller organisationsThe scale of dedicated resources required to execute Typical objections to running data literacy programmesSelling the benefits of why a data literacy programme is required The common challenges faced when implementing a data literacy programmeOvercoming those challenges Assessing the current level of data literacy before startingTailoring data literacy activities for different stakeholders and departmentsThanks to our sponsor, Data Literacy Academy.Data Literacy Academy is leading the way in transforming enterprise workforces with data literacy across the organisation, through a combination of change management and education. In today's data-centric world, being data literate is no longer a luxury, it's a necessity.If you want successful data product adoption, and to keep driving innovation within your business, you need to start with data literacy first.At Data Literacy Academy, we don't just teach data skills. We empower individuals and teams to think critically, analyse effectively, and make decisions confidently based on data. We're bridging the gap between business and data teams, so they can all work towards aligned outcomes.From those taking their first steps in data literacy to seasoned experts looking to fine-tune their skills, our data experts provide tailored classes for every stage. But it's not just learning tracks that we offer. We embed a deep data culture shift through a transformative change management programme.We take a people-first approach, working closely with your executive team to win the hearts and minds. We know this will drive the company-wide impact that data teams want to achieve.Get in touch and find out how you can unlock the full potential of data in your organisation. Learn more at www.dl-academy.com.

In this episode of the Startup Hustle podcast, Matt Watson interviews Ozan Unlu, the founder and CEO of Edge Delta, an observability company. They discuss the challenges of managing large amounts of data in complex production environments and the need for developers to have access to production data without compromising security.  Ozan shares his journey from being a software developer at Microsoft to becoming a solution architect and eventually starting his own company. They also delve into the unique problem that Edge Delta solves, which is processing and analyzing data at the edge to determine its value and optimize storage and retention.  Takeaways- Observability is crucial for managing large amounts of data in complex production environments.- Developers need access to production data without compromising security.- Edge Delta solves the problem of processing and analyzing data at the edge to determine its value and optimize storage and retention.- The company provides flexible and scalable solutions for observability and data pipelines.- Edge Delta competes with log management tools like Splunk and DataDog, offering 80% of features that customers care about.- Edge Delta aims to be the best platform for customers in the long term, rather than focusing solely on revenue. Starting a deep tech and enterprise company requires a long-term vision and the ability to navigate both technical and sales challenges.- Raising early-stage funding can be lengthy, but having a strong team and unique insights can help attract investors.- The sales cycle for enterprise accounts is typically five to six months, involving multiple teams and evaluation periods.- Storing and analyzing large amounts of data in the observability space is a significant challenge, and implementing next-generation architectures can help bring down costs.- Entrepreneurs should seize the current market opportunities and the availability of talent and potential investors to pursue their dreams. Find Startup Hustle Everywhere:https://gigb.co/l/YEh5 This episode is sponsored by Full Scale:https://fullscale.io/ Visit the Edge Delta website:https://edgedelta.com/ Learn more about Ozan here:https://www.linkedin.com/in/ozanu/ Sign up for the Startup Hustle newsletter:https://newsletter.startuphustle.xyz/ Sound Bites"I'm excited because not very often do I get a podcast host who also has a tremendous amount of experience in the space.""I think my unique experience of being an ex-developer who then went solution architect and went salesperson, showing up once a week and really working with the customers on deep technical challenges, kind of in the front trenches, that...""Edge Delta is the concept of how do we start looking at data as it's being created, as close to the source of that data as possible, and how do we start to pre-process, pre-aggregate, and frankly have machine learning and some even basic AI running on the edge.""We have to have a very long-term vision on this, even on day one.""The team is probably the number one thing.""It feels like you're on the treadmill, just trying to get a few customers landed." Chapters00:00 The Challenges of Observability and Data Management06:17 Solving Unique Problems with Edge Delta12:55 Competing in the Landscape of Observability Vendors25:05 The Journey to Acquiring the First Paying Customer31:22 Innovation in the Observability SpaceSee omnystudio.com/listener for privacy information.

Heidi Lanford connects data to cocktails and campaigns while considering the nature of data disruption, getting from analytics to AI, and using data with confidence.Heidi studied mathematics and statistics and never looked back. Reflecting on analytics then and now, she confirms the appetite for data has never been higher. Yet adoption, momentum and focus remain evergreen barriers. Heidi issues a cocktail party challenge while discussing the core competencies of effective data leaders.Heidi believes data and CDOs are disruptive by nature. But this only matters if your business incentives are properly aligned. She revels in agile experimentation while counseling that speed is not enough. We discuss how good old-fashioned analytics put the right pressure on the foundational data needed for AI. Heidi then campaigns for endemic data literacy. Along the way she pans JIT holiday training and promotes confident decision making as the metric that matters. Never saying never, Heidi celebrates human experts and the spotlight AI is shining on data.Heidi Lanford is a Global Chief Data & Analytics Officer who has served as Chief Data Officer (CDO) at the Fitch Group and VP of Enterprise Data & Analytics at Red Hat (IBM). In 2023, Heidi co-founded two AI startups LiveFire AI and AIQScore. Heidi serves as a Board Member at the University of Virginia School of Data Science, is a Founding Board Member of the Data Leadership Collaborative, and an Advisor to Domino Data Labs and Linea. A transcript of this episode is here.

Send me a Text Message hereFULL SHOW NOTES https://podcast.nz365guy.com/565  Unlock the secrets of enterprise data management with insights from Microsoft's own Julie Koesmarno, a principal product manager in Dataverse. Julie's extraordinary journey from database enthusiast to MVP, and now a key player at Microsoft, will leave you inspired. From her passion for ice cream and travel to her innovative projects within Charles Lamanna's Business Applications Platform Group, Julie's story is a compelling blend of personal and professional triumphs.Explore the critical relationship between Copilot AI systems and enterprise data management. Julie sheds light on the concept of 'knowledge' as enterprise data, tackling the challenges and opportunities of integrating Microsoft Graph and Copilot. Learn why robust data housekeeping and security measures are non-negotiable as organizations adopt AI tools, and why grounding AI responses with accurate data is essential for maintaining integrity and governance.Experience a practical demonstration on data modeling and table creation using Copilot. Julie takes us through setting up an environment, designing data models with natural language, and creating a student accommodation review system. She also shares her journey as a female in tech, emphasizing the importance of a supportive team environment and active listening. Julie's reflections on overcoming challenges and fostering diversity offer invaluable lessons for anyone navigating the tech landscape. Tune in for an episode packed with expert advice and inspiring stories!90 Day Mentoring Challenge 10% off code use MBAP at checkout https://ako.nz365guy.comSupport the Show.If you want to get in touch with me, you can message me here on Linkedin.Thanks for listening

In this episode, Amir Bormand interviews Amie Bright, VP of Enterprise Data and Analytics at GitLab, about the importance of data governance, especially in the context of generative AI. Amie explains GitLab's role as an AI-powered DevSecOps platform and discusses strategies to ensure data quality and governance in modern data volumes. The conversation covers the traditional challenges data teams face and the transformative impact of generative AI on those practices. Amie also shares insights from her career and current work at GitLab, emphasizing the need for proper metadata management, certified data sources, and the shifting perception of data governance from a hindrance to an enabler of efficiency and innovation.  Highlights 01:29 Data Governance and Its Importance 04:53 Challenges and Evolution with Generative AI 07:52 Changing Perceptions and Measuring Accuracy 13:23 Future of Data Governance with Gen AI Guest: Amie Bright is an accomplished data and analytics leader with expertise in data management, architecture, integration, warehousing, science, and business intelligence. She excels at creating data strategies that align with long-term organizational goals. Amie is skilled in leading data teams to implement modern, cloud-based data solutions, including ingestion, pipeline orchestration, repositories, governance, security, advanced analytics, and business intelligence. She effectively motivates diverse teams and builds strong relationships across organizational boundaries to achieve analytics objectives. LinkedIn: https://www.linkedin.com/in/amie-bright-03329820/ ---- Thank you so much for checking out this episode of The Tech Trek. We would appreciate it if you would take a minute to rate and review us on your favorite podcast player. Want to learn more about us? Head over at https://www.elevano.com Have questions or want to cover specific topics with our future guests? Please message me at https://www.linkedin.com/in/amirbormand (Amir Bormand)

Alex Merced discusses the difference between Apache Iceberg Catalog and Enterprise Data Catalogs to help clarify the discussions around catalogs in today’s data trends. Follow Alex -> https://bio.alexmerced.com/data

Community is the foundation on which success is built. A strong support network opens opportunities and helps us overcome even the most challenging adversity.On this episode, I'm joined by Prem Natarajan, Chief Scientist, EVP, and Head of Enterprise Data and AI at Capital One, to explore his journey from Chennai to leading AI initiatives at a major financial services company.Key Takeaways:(00:11) The value of continual security testing.(00:18) The importance of prioritizing your security issues.(19:47) How early life influences and role models shape career ambitions.(21:30) The critical role of mentors and asymmetrical mentorship relationships.(29:00) Learning life lessons through sports and teamwork.(36:22) Early technical projects shaping an engineering career.(41:00) Balancing foundational understanding with pragmatic goals in engineering.(50:23) The importance of curiosity and admitting when you don't know.(56:25) Transitioning from academia to industry and the AI revolution.(01:07:11) Family influence and persistence as a mantra.(01:08:51) Nurturing relationships and leveraging education.(01:09:13) Advice on leveraging education and imagination for young professionals.Resources Mentioned:Prem Natarajan - https://www.linkedin.com/in/natarajan/Capital One - https://www.linkedin.com/company/capital-one/BBN Technologies - https://www.bbn.com/University of Southern California - https://www.usc.edu/Amazon Alexa - https://developer.amazon.com/en-US/alexaThanks for listening to the Indianness podcast. If you enjoyed this episode, hit the subscribe button and never miss another insightful conversation with leaders of Indian origin. And be sure to leave a review to help get the word out about the show. #Indian #IndiaBusiness #India #Indianness

Sol Rashidi is an esteemed executive, leader, and influencer within the AI, Data, and Technology space.  Having helped IBM launch Watson in 2011 as one of the earliest world applications of Artificial Intelligence, Sol has pioneered some of the early advancements of space.  Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/ Huge thank you to  @WeightsBiases  for sponsoring this episode. WandB Free Courses - http://wandb.me/courses_mlops MLOps podcast #227 with Sol Rashidi, CEO & Co-Founder of ExecutiveAI, Leading Enterprise Data Teams. // Abstract In the dynamic landscape of MLOps and data leadership, Sol shares invaluable insights on building successful teams and driving impactful projects. In this podcast episode, Sol delves into the importance of prioritizing relationships, introduces a pragmatic "Wrong Use Cases Formula" to streamline project prioritization, and emphasizes the critical role of effective communication in data leadership. Her wealth of experience and practical advice provide a roadmap for navigating the complexities of MLOps and leading data-driven initiatives to success. // Bio With eight (8) patents granted, 21 filed, and received awards that include: "Top 100 AI People" 2023 "The Top 75 Innovators of 2023" "Top 65 Most Influential Women in 2023" "Forbes AI Maverick of the 21st Century" 2022 “Top 10 Global Women in AI & Data”, 2023 "Top AI 100 Award", 2023 “50 Most Powerful Women in Tech”, 2022 “Global 100 Power List” - 2021, 2022, 2023 “Top 20 CDOs Globally” - 2022 "Chief Analytics Officer of the Year" - 2022 "Isomer Innovators of the Year" - 2021, 2022, 2023 "Top 100 Innovators in Data & Analytics” - 2020, 2021, 2022, 2023 "Top 100 Women in Business" - 2022 Sol is an energetic business executive and a goal-oriented technologist, skilled at coupling her technical acumen with story-telling abilities to articulate business value with both startups and Fortune 100's who are leaning into data, AI, and technology as a competitive advantage while wanting to preserve the legacy in which they were founded upon. Sol has served as a C-Suite member across several Fortune 100 & Fortune 500 companies including: Chief Analytics Officer - Estee Lauder Chief Data & Analytics Officer - Merck Pharmaceuticals EVP, Chief Data Officer - Sony Music Chief Data & AI Officer - Royal Caribbean Cruise Lines Sr. Partner leading the Digital & Innovation Practice- Ernsty & Young Partner leading Watson Go-To-Market & Commercialization - IBM Sol now serves as the CEO of ExecutiveAI LLC. A company dedicated to democratizing Artificial Intelligence for Humanity and is considered an outstanding and influential business leader who is influencing the space traveling the world as a keynote speaker, and serving as the bridge between established Gen1.0 markets and those evolving into 4.0. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Sol's Book will be out on April 30, 2024 Your AI Survival Guide: Scraped Knees, Bruised Elbows, and Lessons Learned from Real-World AI Deployments: https://www.amazon.com/Your-Survival-Guide-Real-World-Deployments/dp/1394272634?ref_=ast_author_mpb --------------- ✌️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 Sol on LinkedIn: https://www.linkedin.com/in/sol-rashidi-a672291/

Intel IT migrated enterprise data analytics to the cloud, enabling faster turnaround of business requests and increased operational eff...[…]

This is a re-post from November 2023. In this episode, Thomas Betts talks with Pamela Fox, a cloud advocate in Python at Microsoft. They discuss several ChatGPT sample apps that Pamela helps maintain. These include a very popular integration of ChatGPT with Azure OpenAI and Cognitive Search for querying enterprise data with a chat interface. Pamela also covers some best practices for getting started with ChatGPT apps. Read a transcript of this interview: https://www.infoq.com/podcasts/chatgpt-enterprise-data-search/ Subscribe to the Software Architects' Newsletter for your monthly guide to the essential news and experience from industry peers on emerging patterns and technologies: https://www.infoq.com/software-architects-newsletter Upcoming Events: QCon London (April 8-10, 2024) Discover new ideas and insights from senior practitioners driving change and innovation in software development. https://qconlondon.com/ InfoQ Dev Summit Boston (June 24-25, 2024) Actionable insights on today's critical dev priorities. https://devsummit.infoq.com/ QCon San Francisco (November 18-22, 2024) Get practical inspiration and best practices on emerging software trends directly from senior software developers at early adopter companies. https://qconsf.com/ The InfoQ Podcasts: Weekly inspiration to drive innovation and build great teams from senior software leaders. Listen to all our podcasts and read interview transcripts: - The InfoQ Podcast https://www.infoq.com/podcasts/ - Engineering Culture Podcast by InfoQ https://www.infoq.com/podcasts/#engineering_culture - Generally AI Podcast www.infoq.com/generally-ai-podcast/ Follow InfoQ: - Mastodon: https://techhub.social/@infoq - Twitter: twitter.com/InfoQ - LinkedIn: www.linkedin.com/company/infoq - Facebook: bit.ly/2jmlyG8 - Instagram: @infoqdotcom - Youtube: www.youtube.com/infoq Write for InfoQ: Learn and share the changes and innovations in professional software development. - Join a community of experts. - Increase your visibility. - Grow your career. https://www.infoq.com/write-for-infoq

In today's rapidly evolving digital landscape, where does the balance lie between harnessing the power of generative AI and ensuring the security and privacy of enterprise data? This pivotal question sets the stage for our insightful discussion with Rehan Jalil, CEO and President at Securiti.ai, a trailblazer in the realm of data security and privacy in the generative AI era. Is your enterprise ready to navigate the complexities of generative AI without compromising data security and privacy? Join me as I delve into the heart of this critical issue with Rehan Jalil. With a distinguished career at the helm of Securiti, Jalil brings a wealth of knowledge on the innovative strategies and technologies needed to safeguard sensitive information in a world increasingly driven by AI. From the pioneering Data Command Center that Securiti has developed, enabling the safe use of data across hybrid multicloud environments, to the nuanced challenges of data governance, our discussion spans the spectrum of securing enterprise data against the backdrop of generative AI advancements. We explore the myriad risks that accompany the generative AI era, such as privacy concerns, the generation of biased or inappropriate content, and the potential misuse of AI-generated materials. Jalil emphasizes the importance of governing sensitive data with robust policies, encryption, and a keen focus on data retention and deletion practices. Mitigating these risks requires cutting-edge techniques like differential privacy, synthetic data generation, and federated learning, all aimed at protecting data while fostering the responsible adoption of generative AI technologies. Jalil advocates for a collaborative implementation approach, highlighting the critical role of cross-functional teams in developing comprehensive data security policies and fostering a culture of continuous education on AI ethics and data privacy. As we navigate the conversation, Jalil underscores the need for a balanced approach to generative AI adoption, one that ensures benefits are harnessed without sacrificing control over data and privacy. He sheds light on the evolving landscape of risks and regulations, stressing the importance of adapting policies accordingly and promoting a culture of data responsibility and ethical AI practices. In a world where generative AI presents both immense opportunities and significant challenges, organizations must stay vigilant, embracing innovative solutions and collaborative strategies to ensure the secure and responsible use of AI technologies. Through the lens of Securiti's achievements and the insightful perspectives of Rehan Jalil, this episode offers a deep dive into securing enterprise data in the generative AI era. As we ponder the future of AI in enterprise security and privacy, we invite you to reflect on how your organization is preparing for these challenges. How are you balancing the potential of generative AI with the imperative of data security? Share your thoughts and join the conversation on responsible AI adoption.  

Automating the collection of dispersed, divergent and disjointed pools of enterprise data on to a single repository to drive analytics and build applications remains complex. In this edition of Bloomberg Intelligence's Tech Disruptors podcast, Fivetran cofounder and COO Taylor Brown joins Sunil Rajgopal, BI's senior software analyst, to discuss the evolution of data integration and management platforms in light of the explosion in enterprise data and shift to the Cloud. They also talk about product differentiation against rival platforms such as Informatica, potential disruption from Zero ETL initiatives and AI and overall market prospects.

In this episode, Nathan sits down with Juan Sequeda, Principal Scientist and Head of AI Lab at data.world. They discuss how knowledge graphs can be your organization's "brain" for AI, integrating structured and unstructured data, benchmarking enterprise AI systems, and more. If you need an ecommerce platform, check out our sponsor Shopify: https://shopify.com/cognitive for a $1/month trial period. We're hiring across the board at Turpentine and for Erik's personal team on other projects he's incubating. He's hiring a Chief of Staff, EA, Head of Special Projects, Investment Associate, and more. For a list of JDs, check out: eriktorenberg.com. --- LINKS: Data.world: https://data.world/ SPONSORS: Shopify is the global commerce platform that helps you sell at every stage of your business. Shopify powers 10% of ALL eCommerce in the US. And Shopify's the global force behind Allbirds, Rothy's, and Brooklinen, and 1,000,000s of other entrepreneurs across 175 countries.From their all-in-one e-commerce platform, to their in-person POS system – wherever and whatever you're selling, Shopify's got you covered. With free Shopify Magic, sell more with less effort by whipping up captivating content that converts – from blog posts to product descriptions using AI. Sign up for $1/month trial period: https://shopify.com/cognitive Omneky is an omnichannel creative generation platform that lets you launch hundreds of thousands of ad iterations that actually work customized across all platforms, with a click of a button. Omneky combines generative AI and real-time advertising data. Mention "Cog Rev" for 10% off www.omneky.com NetSuite has 25 years of providing financial software for all your business needs. More than 36,000 businesses have already upgraded to NetSuite by Oracle, gaining visibility and control over their financials, inventory, HR, eCommerce, and more. If you're looking for an ERP platform ✅ head to NetSuite: http://netsuite.com/cognitive and download your own customized KPI checklist. X/SOCIALS: @labenz (Nathan) @juansequeda (Juan) @datadotworld (data.world) @CogRev_Podcast TIMESTAMPS: (00:00:00) - Introduction to Juan Sequeda and data.world (00:01:11) - Discussion on data and generative ai (00:06:15) - Data.world's origins as an open data catalog platform ("Github for data") (00:09:35) - Using knowledge graphs and semantics to integrate and query data (00:12:52) - Main use cases for data catalogs: search/discovery, governance, data operations (00:15:00) - The process of building knowledge graphs automatically from data (00:24:29) - AI for unlocking and capturing tribal business knowledge (00:34:24) - Understanding the data landscape in enterprises (00:38:32) - The emergence of knowledge engineers and data product managers (00:40:44) - The consumer experience in data.world (00:45:36) - The importance of context in data analysis (00:46:58) - The role of AI in improving data analysis (00:48:08) - The importance of accuracy and explainability in data analysis (00:50:08) - Question cataloging in data analysis (00:51:44) - The future potential of "chat with your data" interfaces (01:18:02) - Finetuning with data vs metadata (01:29:24) - Future of enterprise data teams

In this episode of the Futurum Tech Webcast, host Steven Dickens speaks with IBM's Ryan Yackel, GTM PM and Growth Leader, IBM Databand, about the evolving landscape of data management and AI. They discuss the recent acquisition of Databand by IBM, highlighting Databand's role in data observability within the modern data stack. Yackel explains how data observability is becoming increasingly important due to the challenges faced by data engineering teams and the proliferation of diverse tool stacks. He also delves into how data observability complements data governance, emphasizing its role in improving detection, resolution times, and data SLAs. Their discussion covers: IBM's acquisition of Databand and its integration into IBM's data fabric team That data observability is identified as a critical trend due to the increasing demands on data engineering teams and the complexity of tool stacks How data observability enhances data governance, reliability, and quality within organizational data strategies  The intersection of data management practices with AI deployment, emphasizing the importance of quality and governance in AI strategies To learn more, and to download The Futurum Group's white paper done in partnership with IBM, visit the company's website.

Juan Sequeda (Principal Scientist & Head of AI Lab) and Dean Allemang (Principal Solutions Architect) are knowledge graph experts at data.world, a startup that offers a data catalog powered by a knowledge graph to help organizations better understand and gain value from their data.Subscribe to the Gradient Flow Newsletter:  https://gradientflow.substack.com/Subscribe: Apple • Spotify • Overcast • Google • AntennaPod • Podcast Addict • Amazon •  RSS.Detailed show notes can be found on The Data Exchange web site.

In this episode, Thomas Betts talks with Pamela Fox, a cloud advocate in Python at Microsoft. They discuss several ChatGPT sample apps that Pamela helps maintain. These include a very popular integration of ChatGPT with Azure OpenAI and Cognitive Search for querying enterprise data with a chat interface. Pamela also covers some best practices for getting started with ChatGPT apps. Read a transcript of this interview: https://bit.ly/47wmE9r Subscribe to the Software Architects' Newsletter [monthly]: www.infoq.com/software-architect…mpaign=architectnl Upcoming Events: QCon London https://qconlondon.com/ April 8-10, 2024 Follow InfoQ: - Mastodon: https://techhub.social/@infoq - Twitter: twitter.com/InfoQ - LinkedIn: www.linkedin.com/company/infoq - Facebook: bit.ly/2jmlyG8 - Instagram: @infoqdotcom - Youtube: www.youtube.com/infoq Write for InfoQ - Join a community of experts. - Increase your visibility. - Grow your career. www.infoq.com/write-for-infoq/?u…aign=writeforinfoq