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David Sweenor
Founder at TinyTechGuides
South Burlington, United States
David Sweenor is an analytics thought leader, international speaker, author, founder of TinyTechGuides, and has co-developed several patents. David has over 20 years of hands-on business analytics experience spanning product marketing, strategy, product development, and data warehousing. He specializes in artificial intelligence, machine learning, data science, business intelligence, the internet of things (IoT), and manufacturing analytics.
David Sweenor
Points
Academic
15
Author
561
Influencer
54
Speaker
15
Entrepreneur
110
Total
755
Points based upon Thinkers360 patent-pending algorithm.
The Gorilla in the Quadrant: Why Your Data and AI Governance Strategy Needs More Than Gartner’s…
Import from medium.com
February 07, 2025
It’s no surprise that AI and ML are transforming how we collect and use data. But what may be surprising is the lack of attention given to…Continue reading on Medium »
10x Your Productivity: The AI Tools Every B2B Product Marketer Needs
Import from medium.com
February 05, 2025
Tired of waiting for your company to finally choose a generative AI tool? Let’s be honest, sometimes glaciers are melting faster than the…Continue reading on Medium »
Episode 3 of Data Faces: Insights on AI in 2025 with Kjell Carlsson
Import from medium.com
January 14, 2025
Kjell Carlsson on why most Gen AI projects fall short, the evolution of AI engineering, practical governance strategies, agentic AI myths…Continue reading on Medium »
33 Must-Read Articles for Business Leaders in 2024: AI, Ethics, and Innovation
Import from medium.com
December 30, 2024
Last year, I vowed to write a blog a week on Medium. I didn’t quite accomplish that, but I did manage to write 33 different articles on AI…Continue reading on Medium »
Who Watches the Watchman? Managing Cats, Eggplants, and AI Risks
Import from medium.com
December 14, 2024
A couple of months back, my good friend Nick tried using generative AI to brainstorm names for his family’s new kittens. Rather than…Continue reading on Towards AI »
Mind Your AI Manners: The Bottom-Line Impact of Cultural Intelligence
Import from medium.com
December 09, 2024
Remember when HAL 9000 ominously said, “I’m sorry Dave, I’m afraid I can’t do that”? While our current incarnation of AI won’t lock us out…Continue reading on Medium »
Spotting AI junk words: Why AI still can’t write like humans
Import from medium.com
November 18, 2024
In an age where AI seems to be doing it all, from writing never-ending sales cadence emails, generating mediocre marketing blogs, or…Continue reading on Medium »
Generative AI: Turning Hype into Real Business Value — Insights from My Conversation with Yves…
Import from medium.com
November 11, 2024
Generative AI is often surrounded by excitement and speculation, but how can businesses turn this technology into genuine value? In the…Continue reading on Medium »
AI Governance: Your Business’s Competitive Edge or Its Biggest Risk?
Import from medium.com
November 04, 2024
As artificial intelligence (AI) becomes ubiquitous, it’s reshaping decision-making in ways that go far beyond the scope of traditional…Continue reading on Towards AI »
AI Governance Best Practices: A Framework for Data Leaders
Import from medium.com
October 16, 2024
The debate over the role of AI governance in business success is not just about compliance or ethical concerns — it’s a question of…Continue reading on Towards AI »
How AI-Ready Data Drives AI Success
LinkedIn
October 16, 2024
This blog examines Gartner’s report “Quick Answer: What Makes Data AI-Ready?” and argues that ensuring data readiness upfront is the single most important factor for AI success. By aligning data with use cases, qualifying it to meet AI requirements, and implementing governance that accounts for AI’s unique challenges, organizations can not only avoid costly failures but also accelerate their competitive advantage. Business leaders must act now or risk being left behind.
Why LLM Patterns Are the Key to Enterprise Success – And Why Ignoring Them is a Mistake
TinyTechGuides
October 16, 2024
As the generative AI aura begins to fade, organizations realize that the technology is not the panacea they once thought. To move beyond tinker-toy prototypes to enterprise-grade AI systems is challenging, to say the least. McKinsey estimates that only 11% of organizations have adopted generative AI at scale.[1] Organizations are hoping to automate tasks, optimize processes, and improve overall productivity are excited about the potential. The excitement is certainly justified—AI can generate content, retrieve critical information, and summarize large corpora of data at a scale and speed unmatched by human labor. However, there’s a critical oversight many enterprise leaders are making
Why LLM Patterns Are the Key to Enterprise Success — And Why Ignoring Them is a Mistake
Import from medium.com
October 12, 2024
As the generative AI aura begins to fade, organizations realize that the technology is not the panacea they once thought. To move beyond…Continue reading on Towards AI »
How to Build a Compelling Business Case for Generative AI
LinkedIn
October 09, 2024
The race to implement generative AI is no longer an option for enterprises—it’s an imperative. Companies delaying AI adoption risk losing ground. As businesses rush to integrate generative AI into their operations, the path to successful implementation is far from straightforward. Moving beyond initial experiments and proof of concepts (POCs) demands thorough planning and coordination between business and IT leaders. A solid business case is not a mere formality; it is essential to steer clear of poorly executed initiatives and ensure that AI investments deliver business value.
Alkermes’ Commercial Data Governance Journey: Enhancing Data Use to Support the Launch of a New…
Import from medium.com
August 13, 2024
Note: This blog was created from a presentation delivered at Snowflake Summit 2024 by Bo Yang and me. It represents Alkermes’ experience…Continue reading on Medium »
Are You Data-Driven, Insight-Driven, or AI-Driven?
Import from medium.com
July 16, 2024
As the world turns, data is created at a pace that is incomprehensible to most. In fact, in the 2030s, it’s estimated that we will be…Continue reading on Medium »
The Generative AI Practitioner’s Guide: How to Apply LLM Patterns to Build Real World Enterprise Applications
TinyTechMedia LLC
July 20, 2024
Generative AI is revolutionizing the way organizations leverage technology to gain a competitive edge. However, as more companies experiment with and adopt AI systems, it becomes challenging for data and analytics professionals, AI practitioners, executives, technologists, and business leaders to look beyond the buzz and focus on the essential questions: Where should we begin? How do we initiate the process? What potential pitfalls should we be aware of?
This TinyTechGuide offers valuable insights and practical recommendations on constructing a business case, calculating ROI, exploring real-life applications, and considering ethical implications. Crucially, it introduces five LLM patterns—author, retriever, extractor, agent, and experimental—to effectively implement GenAI systems within an organization. The Generative AI Practitioner’s Guide: How to Apply LLM Patterns for Enterprise Applications bridges critical knowledge gaps for business leaders and practitioners, equipping them with a comprehensive toolkit to define a business case and successfully deploy GenAI.
In today’s rapidly evolving world, staying ahead of the competition requires a deep understanding of these five implementation patterns and the potential benefits and risks associated with GenAI. Designed for business leaders, tech experts, and IT teams, this book provides real-life examples and actionable insights into GenAI’s transformative impact on various industries.
Empower your organization with a competitive edge in today’s marketplace using The Generative AI Practitioner’s Guide: How to Apply LLM Patterns for Enterprise Applications. Remember, it’s not the tech that’s tiny, just the book!
Generative AI Business Applications: An Executive Guide with Real-Live Examples and Case Studies
TinyTechMedia LLC
February 01, 2024
Within the past year, generative AI has broken barriers and transformed how we think about what computers are truly capable of. But, with the marketing hype and generative AI washing of content, it’s increasingly difficult for business leaders and practitioners to go beyond the art of the possible and answer that critical question–how is generative AI actually being used in organizations?
With over 70 real-world case studies and applications across 12 different industries and 11 departments, Generative AI Business Applications: An Executive Guide with Real-Life Examples and Case Studies fills a critical knowledge gap for business leaders and practitioners by providing examples of generative AI in action. Diving into the case studies, this TinyTechGuide discusses AI risks, implementation considerations, generative AI operations, AI ethics, and trustworthy AI.
The world is transforming before our very eyes. Don’t get left behind—while understanding the powers and perils of generative AI. Full of use cases and real-world applications, this book is designed for business leaders, tech professionals, and IT teams. We provide practical, jargon-free explanations of generative AI’s transformative power.Gain a competitive edge in today’s marketplace with Generative AI Business Applications: An Executive Guide with Real-Life Examples and Case Studies. Remember, it’s not the tech that’s tiny, just the book!
The CIO’s Guide to Adopting Generative AI: Five Keys to Success
TinyTechMedia
October 24, 2023
In a world full of generative AI hoopla, it's easy to get lost in the maze of options and marketing hype. Don't get distracted by the vendor hype; instead, focus on building resilient, high-value platforms that will set you apart from the competition. The CIO’s Guide to Adopting Generative AI: Five Keys to Successfills a critical knowledge gap for CIOs and business leaders by succinctly offering five success factors that need to be met before an organization can successfully incorporate generative AI.
To unlock the transformative business value of generative AI, business leaders must: 1) identify enterprise use cases, 2) apply context to large language models (LLMs) using their organization's data, 3) take special precautions to ensure data security and privacy, 4) implement an artificial intelligence (AI) governance framework, and 5) build manageable AI applications for business users. This report provides the keys to unlocking the true potential of generative AI.
Full of use cases and real-world applications, this report is designed for business leaders, tech professionals, and IT teams. We provide practical, jargon-free explanations of generative AI's transformative power.
Gain a competitive edge in today's marketplace with The CIO’s Guide to Adopting Generative AI: Five Keys to Success. Remember, it's not the tech that's tiny, it's the book!
Artificial Intelligence: An Executive Guide to Make AI Work for Your Business
TinyTechMedia LLC
April 02, 2022
In the business world, the very term artificial intelligence (AI) is shrouded in mystery. For some, it’s the brains behind a robotic apocalypse. For others, it provides hope for a better society with self-driving cars, food security, and medical breakthroughs. But what about for businesses? For most executives , the term “AI” is vague, confusing, and although intriguing, it seems unapproachable.
Artificial Intelligence: An Executive Guide to Make AI Work for Your Business is designed for non-experts—it’s for business teams, business leaders, and executives who never seem to have enough time in the day to learn about the latest technology trends. TinyTechGuides are meant to be read in under two hours and focus on the application of technologies in business, government, and educational settings.
This book covers the fundamentals of AI: data, analytic, and automation technologies—from modern data management techniques to chatbots, machine learning, natural language processing (NLP), robotic process automation (RPA), and computer vision. It discusses the business benefits of AI, the importance of AI ethics, MLOps, and provides real steps on how to start your AI journey.
With real-world examples of businesses applying AI, you’ll learn how to use AI within Accounting & Finance, Marketing & Sales, Research & Development, Supply Chain, IT, Human Resources, and Service and Support. There are practical industry examples across Banking & Finance, Energy & Utilities, Insurance, Government, Healthcare, Life Sciences, Manufacturing, Retail, Telecom, and Transportation & Logistics.
If you want to know how AI can be applied to improve your business, this TinyTechGuide is for you! Remember, It’s not the tech that’s tiny, just the book!
It's All Analytics – Part II: Designing an Integrated AI, Analytics, and Data Science Architecture for Your Organization
Routledge, Taylor and Francis Group
September 29, 2021
Up to 70% and even more of corporate Analytics Efforts fail!!! Even after these corporations have made very large investments, in time, talent, and money, in developing what they thought were good data and analytics programs. Why? Because the executives and decision makers and the entire analytics team have not considered the most important aspect of making these analytics efforts successful. In this Book II of "It’s All Analytics!" series, we describe two primary things: 1) What this "most important aspect" consists of, and 2) How to get this "most important aspect" at the center of the analytics effort and thus make your analytics program successful.
Customers such as Cisco, DocuSign, Nasdaq, Pfizer, and Samsung trust Alation’s platform for self-service analytics, cloud transformation, data governance, and AI-ready data, fostering data-driven innovation at scale. Headquartered in Redwood City, California, Alation has been recognized five times by Inc. Magazine as one of the Best Workplaces.
TinyTechMedia, the makers of TinyTechGuides. TinyTechGuides are designed for non-experts — they’re designed for business teams, business leaders, and executives who never seem to have enough time in the day to learn about the latest technology trends. They are designed to be read in an hour or two and focus on the application of technologies in a business, government, or educational institution.
THE USE OF POLYNOMIAL NEURAL NETWORKS IN PREDICTING SURVIVAL OF SWINE IN HEMORRHAGIC SHOCK
Telemedicine Journal
July 01, 2004
The first 20 to 30 minutes after an injury is the time window faced by the Army medic within which triage and rescue decisions must be made. In the battlefield setting, roughly 20% of mortalities occur before injured soldiers can he transported to the Battle Aid Station (BAS). After arriving at the BAS, mortality rates fall to about 3%. This research addresses the utility of polynomial neural network (PNN) models in predicting mortality during hemorrhagic shock (HS) for use in trauma triage. Data from over 100 swine were acquired from two HS experimental protocols. Swine in the first group had HS induced through a Grade V liver injury, whereas those in the second group received an aortotomy. Four time-stamped physiological variables were measured: systolic and diastolic blood pressure, mean arterial pressure, and heart rate. Sampled every 10 seconds, these data will be used to predict mortality one hour after injury. The hypothesis is that the PNN models will be able to learn effectively the dynamic characteristics of HS data and will be an effective aid in mortality prediction for trauma triage.
The use of polynomial neural networks for mortality prediction in uncontrolled venous and arterial hemorrhage
Journal of Trauma
January 01, 2002
The ability to rapidly and accurately triage, evacuate, and utilize appropriate interventions can be problematic in the early decision-making process of trauma care. With current methods of prehospital data collection and analysis, decisions are often based upon single data points. This information may be insufficient for reliable decision-making. To date, no studies have attempted to utilize data at multiple time points for purposes of enhancing prediction, nor have studies attempted to synthesize prediction models with data reflecting both large-vessel venous and arterial injuries. Therefore, we performed a retrospective study to examine the potential utility of dynamic neural networks in predicting mortality using highly discretized uncontrolled hemorrhagic shock data.
On defining the optical gap of an amorphous semiconductor: an empirical calibration for the case of hydrogenated amorphous silicon
Solid State Communications
April 08, 1999
It is pointed out that there are a number of different means whereby the optical gap of an amorphous semiconductor may be defined. We analyze some hydrogenated amorphous silicon data with respect to a number of these empirical measures for the optical gap. By plotting these gap measures as a function of the breadth of the optical absorption tail, we provide a means of relating these disparate measures of the optical gap.
Escaping operational black holes with unified ‘full-fidelity’ observability
IDG Connect
June 26, 2022
As tech leaders now look to gain deep and granular tranches of management control across their IT estates, there is a reasonable (if not compelling) argument for questioning the form, focus and fidelity of our observability viewpoint – the alternative may be something like a journey down an operational black hole, which is clearly a fairly suffocating experience for everyone.
A new recipe for enterprise data, 'too many cooks' is over
IDC Connect
January 20, 2022
The adage 'too many cooks' might still apply in the soup kitchen, but in cloud-centric data analytics, there is an argument for more ingredients (data sources), more cooks (data scientists) and more servings all round.
Getting Started with Data Science as an SMB
EM360
July 09, 2021
science is out of their reach because they can’t afford to hire an expensive data analyst. In fact, Michael Guta reports that 67% of small businesses spend more than $10,000 per year on analytics. However, analysing data doesn’t have to come with a high cost and you don’t have to be an online titan like Amazon to be able to compete in the data-driven business movement. Modern technology means that the ability to take data and turn it into a problem-solving insight is no longer exclusively within the realms of those companies with big budgets or individuals with years of experience or a specific university degree. These myths against the use of data in SMBs need to be dispelled. It's time for SMBs to leverage data in order to survive and thrive!
Method and a system for on-boarding, administration and communication between cloud providers and tenants in a share-all multi-tenancy environment
USPTO
September 12, 2013
A method of ascertaining requirements for onboarding new users to a multi-tenant computing environment is provided herein. The method starts off with the stage of recognizing the organizational roles of the new users. The method continues to the stage of identifying parameters pertaining to the service. The method then goes on to mapping the organization roles of the new users and the service parameters to a set of rules. The method further includes determining actions needed to be taken on the computer environment based on the set of rules. Finally, the method goes on to the stage of onboarding the new users to the computer environment.
Learning based logic diagnosis
USPTO
July 07, 2009
A system and method for diagnosing a failure in an electronic device. A disclosed system comprises: a defect table that associates previously studied features with known failures; and a fault isolation system that compares an inputted set of suspected faulty device features with the previously studied features listed in the defect table in order to identify causes of the failure.
What continued growth for Digital Transformation means for CIOs
Intelligent CIO
February 18, 2022
Digital Transformation is the incorporation of computer-based technologies into an organisation’s solutions, processes, and strategies. Industry pundits look at how enterprises in the Middle East and Africa (MEA) are winning with Digital Transformation, business benefits and digital technologies as the growth trajectory continues.
Analytics Democratization vs. Governance: Are they at odds?
Gartner.com
May 11, 2022
As companies embark on their journey to democratize analytics across their organization, questions of governance often come up. Is the very concept of analytics governance and democratization at odds? Join this session to hear how UBS balances the seemingly conflicting approaches.
Training Day: How to Optimize Predictive Algorithms (ML)
DMRadio
December 02, 2021
The power of prediction is well known in the analytics industry. The big question for today: How do you optimize, both from a design and production perspective? Join this conversation to hear Host @Eric_Kavanagh interview Kathleen L. D. Maley of Experian, David Sweenor of Alteryx and Tim Wyatt from Lookout.
Mcdonald's Journey to Analytics Enablement
DATACated
March 29, 2022
See how McDonald's is using analytics automation to democratize data
Every organization needs insights, but the path to analytics maturity can be daunting if leaders don’t know where to start. Listen to this on-demand webinar with McDonald’s’ director of global data and analytics enablement, Jeff Nieman, to hear how he’s helping McDonald’s on its path to enterprise-wide analytics enablement.
Up-Skilling for Analytics – Keys to Success
Inside Analysis
January 19, 2022
The key to success in analytics tends to be a moving target. First, we focused on the data, then the queries. With data science, we pivoted to more complex algorithms to find those meaningful insights. But regardless of which approach you take, there is one common thread that leads to breakthrough outcomes for your business: Automation!
Check out this episode of #InsideAnalysis to hear Host @eric_kavanagh interview several industry experts: Melissa Burroughs and David Sweenor of Alteryx, and Nick Jewell of Datacurious.ai. They’ll discuss the importance of knowing which automations can generate the optimal value, as well as the critical need for up-skilling your team.
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