BEGIN:VCALENDAR
VERSION:2.0
X-WR-CALNAME:datatech2026
X-WR-CALDESC:Event Calendar
METHOD:PUBLISH
CALSCALE:GREGORIAN
PRODID:-//Sched.com Data Tech 2026//EN
X-WR-TIMEZONE:UTC
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T130000Z
DTEND:20260515T140000Z
SUMMARY:Registration\, networking & coffee
DESCRIPTION:\n
CATEGORIES:
LOCATION:Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:a0feba4c278b2caa88c9ac85a938777d
URL:http://datatech2026.sched.com/event/a0feba4c278b2caa88c9ac85a938777d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T140000Z
DTEND:20260515T141500Z
SUMMARY:Kickoff
DESCRIPTION:\n
CATEGORIES:
LOCATION:(b) Cafeteria\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:321812e038454776ac8631a6de18df92
URL:http://datatech2026.sched.com/event/321812e038454776ac8631a6de18df92
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T143000Z
DTEND:20260515T150000Z
SUMMARY:LEHRN Panel Part I: The MSP AI and Data Scientist Job Market in 2026: Truths\, Trends and (actionable) Takeaways
DESCRIPTION:This presentation delivers a clear-eyed analysis of the Minneapolis–Saint Paul AI and data science job market in 2026\, cutting through hype to reveal what’s actually driving hiring decisions. Led by seasoned HR leaders and domain experts\, it examines evolving employer expectations\, the impact of automation on role design\, and the growing importance of hybrid skill sets that blend technical depth with business fluency. Attendees will gain insight into compensation trends\, in-demand capabilities\, and common gaps in candidate readiness. The session concludes with practical\, actionable takeaways for both job seekers and organizations—covering how to position talent\, build competitive teams\, and adapt to a rapidly shifting landscape.
CATEGORIES:0 - PANEL / SHOWCASE
LOCATION:(b) Cafeteria\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:dc6735e4e0325675ad0425b943c2dc2b
URL:http://datatech2026.sched.com/event/dc6735e4e0325675ad0425b943c2dc2b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T143000Z
DTEND:20260515T171500Z
SUMMARY:Startup Showcase & VC Panel
DESCRIPTION:The Startup Showcase features pitches from startups that are doing something unique or innovative in analytics\, AI\, machine learning\, or other emerging data technology. Each startup gets six minutes to pitch followed by a Q&A.\n\nIn addition to the pitches\, this session also features: \n\n"Data Tech '24 Alum: From MN Cup Winner to Market Traction -- in a 100-year-old Industry”&nbsp\;with&nbsp\;Michael Peterson\, Founder/CEO\, Raise a Hood\nVC Panel: "How AI Has -- and Hasn't -- Changed Venture Investing" with&nbsp\;Grant Gibson\, Principal\, Great North Ventures and Garrett Lauderdale\, Vice President\, Idea Fund of La Crosse."Accelerating High-Impact Data Technologies with Non-Dilutive Federal Funding" with Pat Dillon\, Founder & CEO\, MNSBIR Inc.
CATEGORIES:0 - PANEL / SHOWCASE
LOCATION:(f) Bde Maka Ska\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:b2079001dc3202efe8727c7b5063799b
URL:http://datatech2026.sched.com/event/b2079001dc3202efe8727c7b5063799b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T143000Z
DTEND:20260515T150000Z
SUMMARY:Building an Enterprise Foundation to Support Your AI Journey
DESCRIPTION:As organizations accelerate their adoption of artificial intelligence\, many discover that success depends less on the models and tools\, and more on the strength of the enterprise foundation beneath them. This presentation explores how to build a scalable\, secure\, and trusted foundation that enables AI to deliver sustained business value. &nbsp\;We will touch on building blocks required to support an AI journey at enterprise scale\, including data architecture\, governance\, security\, operating models\, and workforce development. While focused on a business driven outcome mindset\, attendees will gain insight into why foundational capabilities must come before advanced use cases\, common pitfalls organizations encounter\, and concrete steps leaders can take to move from experimentation to enterprise impact. The session is designed for executives and practitioners seeking to move beyond pilots and build an AI foundation that is resilient\, adaptable\, and ready for what comes next.
CATEGORIES:2 - MORE BUSINESS
LOCATION:(c) Alaska\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:82e37c9d9289248c7b19b21a7025d8a0
URL:http://datatech2026.sched.com/event/82e37c9d9289248c7b19b21a7025d8a0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T143000Z
DTEND:20260515T150000Z
SUMMARY:AI\, Automation\, and Quantum: Three Computing Models Walk into an Enterprise
DESCRIPTION:AI agents increasingly serve as the cognitive interface of modern enterprises\, while automation platforms provide the deterministic\, operational backbone. These two models often operate in tension: probabilistic AI systems excel at reasoning and adaptation\, whereas automation drives predictability\, repeatability\, and control. This session will explore how enterprises can reconcile non-deterministic AI with deterministic workflows at scale.\n\nWe will then introduce quantum computing as a fundamentally different computational paradigm\, one that leverages superposition\, entanglement\, and quantum optimization to address classes of problems that are computationally intractable with classical approaches. Finally\, we will synthesize these models into a cohesive architectural view\, illustrating how AI\, automation\, and quantum computing can be positioned together to maximize enterprise value\, reliability\, and future readiness.
CATEGORIES:3 - BUSINESS & TECHNICAL
LOCATION:(e) Harriet\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:14c3bee6d7013fdf72f3204a53799bab
URL:http://datatech2026.sched.com/event/14c3bee6d7013fdf72f3204a53799bab
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T143000Z
DTEND:20260515T150000Z
SUMMARY:Accessibility in Data Visualizations: Tips and Tricks
DESCRIPTION:Interactive data visualizations and reports provide so much more insight into data for a user than a static image or tables with rows upon rows of data points. But with the changes in how we present our information\, we also need to understand how all users will be able to engage with and leverage these digital assets. This is where digital accessibility comes in – ensuring that the information we present is available to everyone. Digital accessibility takes proper planning and design but with it\, you can make your work accessible to a wider audience.\n\nDigital accessibility has four key principles as defined by WCAG 2.1:\n\nPerceivable - users must be able to perceive the information being presented\nOperable - users must be able to operate the interface\nUnderstandable - users must be able to understand the information as well as the operation of the user interface\nRobust - users must be able to access the content as technologies advance\n\nWe will provide examples of how our work in Tableau at the Minnesota Pollution Control Agency embodies these four principles and tips and tricks to include digital accessibility in all you do.
CATEGORIES:4 - MORE TECHNICAL
LOCATION:(a) Theater\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:c9942715ff12dd038cb0e6ae7d4fd948
URL:http://datatech2026.sched.com/event/c9942715ff12dd038cb0e6ae7d4fd948
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T143000Z
DTEND:20260515T150000Z
SUMMARY:evaluAIte: Where Feedback Meets Intelligence
DESCRIPTION:evaluAIte\, winner of the United States Space Force AI Challenge in the Staff Category\, is an AI-enabled evaluation analysis tool built to convert large volumes of qualitative and quantitative feedback into explainable\, decision-ready insight. Organizations often collect enormous amounts of feedback\, yet the process of reviewing\, interpreting\, and acting on that data remains manual\, time-intensive\, and difficult to scale. evaluAIte was developed to bridge that gap by integrating structured survey data\, sentiment-aware text analysis\, and AI-assisted thematic classification into a unified analytical workflow.\n\nThe platform is designed to identify recurring themes\, highlight root causes\, and surface actionable recommendations while maintaining transparency in how outputs are generated. A key feature of evaluAIte is its emphasis on explainability\, pairing AI-derived labels and findings with traceable rationale so users can validate conclusions against the original source data. This balance between automation and human oversight makes the tool especially valuable in environments where trust\, auditability\, and speed are all mission-critical.
CATEGORIES:4 - MORE TECHNICAL
LOCATION:(g) Minnetonka\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:b59f9ddf58c560e59a999089d5892c7f
URL:http://datatech2026.sched.com/event/b59f9ddf58c560e59a999089d5892c7f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T143000Z
DTEND:20260515T150000Z
SUMMARY:Inverse Molecular Design for Selective Adsorption
DESCRIPTION:Selective adsorption on functionalized surfaces underpins many technologies in separation\, sensing\, and filtration\, yet rational design remains challenging because adsorption depends on many coupled factors\, including surface chemistry\, molecular structure\, and environment. Here\, we present an inverse molecular design framework that integrates generative AI models with physics‑based adsorption free‑energy calculations to accelerate discovery of surface chemistries optimized for target capture. \n\nIn this approach\, generative AI models explore large spaces of candidate functional groups and molecular motifs conditioned on desired adsorption behavior\, while molecular simulations provide quantitative feedback through computed potentials of mean force. Adsorption is modeled using umbrella sampling to explicitly capture the free‑energy landscape between bound and unbound states\, enabling robust estimation of relative binding affinities and selectivities. These physically grounded signals are used to guide and refine the generative model\, closing the loop between hypothesis generation and validation. \n\nBy combining data‑driven exploration with first‑principles adsorption modeling\, this workflow shifts molecular discovery from a forward\, trial‑and‑error paradigm to a goal‑directed inverse design process. The result is a scalable\, interpretable methodology for proposing experimentally actionable surface chemistries with improved confidence\, reduced iteration time\, and direct alignment to performance objectives.
CATEGORIES:5 - TECHNICAL
LOCATION:(h) Proverb / Edison\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:892645fca879d57638ec72761de35d5a
URL:http://datatech2026.sched.com/event/892645fca879d57638ec72761de35d5a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T143000Z
DTEND:20260515T150000Z
SUMMARY:Longitudinal Framework for Measuring Long-Term Campaign Impact
DESCRIPTION:Most campaign measurement frameworks rely on redemption-based metrics\, which fail to distinguish true incremental impact from temporal demand shifting. We introduce the Longitudinal Measurement Framework (LMF)\, a causal inference–driven approach to quantify the long-term effects of campaigns on guest behavior.\n\nLMF estimates changes in Guest Lifetime Value and churn using a difference-in-differences design over pre- and post-campaign periods. The framework produces treatment effects that account for partial incrementality\, separating short-term transactional lift from sustained behavioral change. By incorporating counterfactual baselines and longitudinal tracking\, LMF mitigates biases inherent in redemption-based approaches\, including selection effects and pull-forward behavior.\n\nWe present the methodological foundations of LMF along with key considerations for large-scale implementation in real-world settings\, emphasizing the need to move beyond point-in-time metrics toward longitudinal\, causally grounded evaluation of marketing effectiveness.
CATEGORIES:5 - TECHNICAL
LOCATION:(d) Nokomis\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:e14504f98bb5eef5fb11a2aa73b39910
URL:http://datatech2026.sched.com/event/e14504f98bb5eef5fb11a2aa73b39910
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T151500Z
DTEND:20260515T154500Z
SUMMARY:LEHRN Panel Part II: Now that we know the landscape of the AI and Data Scientist Jobscape\, how do we navigate it successfully?
DESCRIPTION:Building on a clear understanding of the AI and data science job landscape\, this presentation focuses on how to navigate it effectively in 2026. Led by experienced HR leaders and industry practitioners\, it translates market realities into practical strategies for both candidates and employers. The session explores how professionals can differentiate themselves through targeted skill development\, portfolio building\, and strategic networking\, while organizations learn how to attract\, assess\, and retain high-impact talent in a competitive market. Attendees will leave with actionable guidance on aligning skills with demand\, positioning for emerging roles\, and making informed career or hiring decisions in an environment defined by rapid change and increasing specialization.
CATEGORIES:0 - PANEL / SHOWCASE
LOCATION:(b) Cafeteria\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:f536d1954d80440a3c6b59a0c2027dac
URL:http://datatech2026.sched.com/event/f536d1954d80440a3c6b59a0c2027dac
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T151500Z
DTEND:20260515T154500Z
SUMMARY:Lessons Learned Filling a Supercomputer with Crappy (Manure) Data
DESCRIPTION:ManureDB is a manure and organic amendment database that contains over 550\,000 samples developed by the University of Minnesota. Managing the agronomic\, economic\, environmental\, and logistical challenges and opportunities of manure warranted updates to previously published manure characteristics from the early 2000s. Data use agreements\, standardized data templates\, and data validation measures were developed prior to data upload into the database to maintain data privacy\, data integrity\, and FAIR (Findability\, Accessibility\, Interoperability\, and Reusability) principles. Data visualizations and summaries are available on the ManureDB website for beef\, dairy\, swine\, poultry\, horse\, and sheep\, and a data explorer tab enables CSV downloads of datasets in selected units. The data is being used for farmer benchmarking\, agricultural and environmental modeling\, and updating ASABE’s Manure Characteristics standard D384.3. A new Minnesota well water database is utilizing this framework to accelerate their project progress.
CATEGORIES:3 - BUSINESS & TECHNICAL
LOCATION:(e) Harriet\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:f0269c30f8c170cc2727df8eaa5be007
URL:http://datatech2026.sched.com/event/f0269c30f8c170cc2727df8eaa5be007
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T151500Z
DTEND:20260515T154500Z
SUMMARY:Modern Data Stack Meets AI: Real-World Integration for Analytics Innovation
DESCRIPTION:Discover how modern data stack tools and platforms can be seamlessly integrated to power analytics applications\, enhanced by large language models for AI-driven insights. This session will showcase a real-world example of leveraging tools like Snowflake\, dbt\, Fivetran\, and IBM Cognos Analytics\, combined with AI for advanced analysis and decision-making. Gain practical insights into building scalable\, intelligent analytics solutions to tackle complex data challenges in today’s tech landscape.
CATEGORIES:3 - BUSINESS & TECHNICAL
LOCATION:(c) Alaska\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:29ea950570415882d3b605977afc7ad5
URL:http://datatech2026.sched.com/event/29ea950570415882d3b605977afc7ad5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T151500Z
DTEND:20260515T154500Z
SUMMARY:Spreadsheet Modeling Design - Case study: Building Robust Retirement Planning Models
DESCRIPTION:Despite the widespread adoption of Enterprise Resource Planning (ERP) tools\, we often hear “The world runs on Excel.” Today’s business climate requires Enterprise level tools\, even for tools which are in the prototype stage or in production but awaiting integration into more formal and mature systems.&nbsp\;Implementing and following standards for spreadsheet model design provides reliable and stable tools to manage and run “the business” in parallel with strong prototypes for enterprise level application development.\n\nAttendees will leave with a greater understanding optimal spreadsheet model design through a case study examining the design of robust retirement planning models which will remove the mystery of online and black-box models.
CATEGORIES:3 - BUSINESS & TECHNICAL
LOCATION:(g) Minnetonka\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:60bf8687c37db7be4084c1526ee3ed18
URL:http://datatech2026.sched.com/event/60bf8687c37db7be4084c1526ee3ed18
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T151500Z
DTEND:20260515T154500Z
SUMMARY:Agent Improv
DESCRIPTION:You name it\, we'll build it. If a live demo feels too polished without enough opportunities for things to go wrong\, let's throw out the script and do it live. We'll use build an AI agent from scratch. Then\, we'll deploy it to to the world! If you are a developer or if you just want to join the chaos\, this will be an exciting ride worth joining.
CATEGORIES:4 - MORE TECHNICAL
LOCATION:(a) Theater\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:2254f54655f3aacc899a081fcf679c08
URL:http://datatech2026.sched.com/event/2254f54655f3aacc899a081fcf679c08
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T151500Z
DTEND:20260515T154500Z
SUMMARY:Structure Before Speed: Spec-First Engineering for High-Stakes AI
DESCRIPTION:AI coding tools are transforming how data and analytics teams build software\, but speed without structure has a hidden cost. Unstructured AI-assisted development produces pipelines that fail unexpectedly\, agentic systems that make decisions no one can trace\, and codebases where intent is lost the moment the chat window closes. This session makes the case that spec-first engineering\, defining system intent\, constraints\, and behavior before the AI writes a line\, is the discipline that separates reliable AI-assisted development from fragile\, untraceable output.\n\nAttendees will see a live demonstration using GitHub's open-source SpecKit framework applied to a healthcare medication alert system\, showing how structured workflows transform AI into a disciplined engineering partner. Key takeaways include how to encode requirements as AI context\, why your spec is your audit trail\, and how to know when structure is non-negotiable. Whether you are building data pipelines\, ML systems\, or agentic AI workflows\, you will leave with a concrete and repeatable approach you can apply immediately.
CATEGORIES:4 - MORE TECHNICAL
LOCATION:(h) Proverb / Edison\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:90a57f70894efd4a7be7bef67f9e0a50
URL:http://datatech2026.sched.com/event/90a57f70894efd4a7be7bef67f9e0a50
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T151500Z
DTEND:20260515T154500Z
SUMMARY:vLLM - LLM serving with PagedAttention
DESCRIPTION:Serving large language models efficiently in production is a hard problem. Traditional inference engines waste up to 80% of GPU memory through KV cache fragmentation and over-allocation\, leading to poor throughput and high costs. This talk dives into PagedAttention\, a key innovation that borrows virtual memory paging from operating systems as a solution to this and vLLM\, the open-source engine built on top of it.\n\nThis talk will cover the theory\, walk through using vLLM in practice\, and look at benchmark results showing up to 24× throughput improvements. We'll close with a look at how vLLM has been rapidly adopted across the industry and why PagedAttention has become a foundational primitive in LLM serving.
CATEGORIES:5 - TECHNICAL
LOCATION:(d) Nokomis\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:7bfaae3fa8f96b2fcddad45451db06e7
URL:http://datatech2026.sched.com/event/7bfaae3fa8f96b2fcddad45451db06e7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T160000Z
DTEND:20260515T163000Z
SUMMARY:Data for Agents: Building the Trusted Data Spine for AI
DESCRIPTION:AI agents are only as effective as the data they can securely access and accurately interpret. As organizations implement agent-based workflows\, challenges like fragmented\, inconsistent\, ungoverned\, and contextless data often hinder progress\, reduce trust\, and increase compliance risks. This session highlights the importance of the “Context Data Product”\, a foundational element in building a Trusted Data Spine for AI. This robust\, enterprise-grade data foundation empowers agents\, analytics\, and automation by ensuring data is contextual\, reliable\, and actionable.\n\nWith so many ambiguous definitions of data products ranging from dashboards and pipelines to datasets and APIs\, organizations risk misallocating resources and missing out on adoption. In this session\, we will demystify what a real data product is\, break down its essential components\, and highlight how both a Context Data Product and a data product mindset can drive tangible business results that extend beyond technical implementation. Whether you’re a data leader or practitioner\, you’ll gain clear\, actionable strategies for building effective data operating models and leave with a practical framework ready to adapt to your own environment.
CATEGORIES:2 - MORE BUSINESS
LOCATION:(b) Cafeteria\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:55b8325d94add0c3652c868305477036
URL:http://datatech2026.sched.com/event/55b8325d94add0c3652c868305477036
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T160000Z
DTEND:20260515T163000Z
SUMMARY:Developing a new Grateful Patient score for the Vet Med Center
DESCRIPTION:The Veterinary Medical Center (VMC) identified a business need for a re-vamped Grateful Patient-like score to identify clients who are likely to make Major Gifts ($50K+) to the VMC. Their current score rated too many clients in the top bracket. Using factors that we thought were indicators of propensity to give\, we developed a simple score. It seemed reasonably accurate\, but the scoring system was too convoluted and resulted in too few top scorers. After Gemini became approved for use at the University\, we used Gemini to look at the data. The scoring system that Gemini helped us to create performs slightly better than our re-vamped version but has the benefit that it is much simpler to explain. Our score prioritizes length and depth of the clinic-client relationship. We are identifying roughly half of our Major Donors (8 out of 16) within the top 10-15% of our total population which satisfies the need of the VMC Development team to have a reasonably prioritized list for donor qualification.
CATEGORIES:2 - MORE BUSINESS
LOCATION:(e) Harriet\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:d48d523e43fa6cc5febdf8fd6d2cc11a
URL:http://datatech2026.sched.com/event/d48d523e43fa6cc5febdf8fd6d2cc11a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T160000Z
DTEND:20260515T163000Z
SUMMARY:AI-Assisted Engineering in 2026: Workflows\, Taste\, and What Comes Next
DESCRIPTION:This presentation will cover the bleeding edge of AI tooling for engineers and builders\, from vibe coding tools like Lovable to agentic coding tools like Claude Code. As a principal software engineer specialising in AI\, I will break down the workflows that have been most productive for my team and provide recommendations for when & where you should use each one\, with a particular focus on test-driven development and the power of AI self-testing loops. These lessons apply whether you're a seasoned engineer or a non-technical builder willing to learn and ask the right questions.\n\nI will also zoom out on what engineers are becoming as AI removes the grunt work of implementation. When everyone can build anything\, the differentiator shifts from capability to taste\, judgement\, and critical thinking.
CATEGORIES:3 - BUSINESS & TECHNICAL
LOCATION:(a) Theater\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:8d7edb2aaaac4325992a98a3bfd26384
URL:http://datatech2026.sched.com/event/8d7edb2aaaac4325992a98a3bfd26384
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T160000Z
DTEND:20260515T163000Z
SUMMARY:Secure Data Collaboration: Data Cleanroom in Practice
DESCRIPTION:As organizations increasingly collaborate on data-driven insights\, privacy\, security\, and regulatory constraints make traditional data sharing risky and often impractical. This session introduces the Data Cleanroom concept\; a secure\, privacy preserving environment that allows multiple parties to analyze and collaborate on sensitive data without directly sharing or exposing raw datasets.
CATEGORIES:3 - BUSINESS & TECHNICAL
LOCATION:(d) Nokomis\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:bc5ecc148122ba64a2bb3912fd0d0f3e
URL:http://datatech2026.sched.com/event/bc5ecc148122ba64a2bb3912fd0d0f3e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T160000Z
DTEND:20260515T163000Z
SUMMARY:Beyond Rows and Columns — Building a Multi-Modal Data Lakehouse with Bedrock Data Automation and Apache Iceberg
DESCRIPTION:This session presents a reference architecture for a multi-modal data lake house on AWS that closes this gap. We combine Amazon Bedrock Data Automation (BDA) for intelligent content extraction with Apache Iceberg for scalable\, ACID-compliant data management — turning documents\, images\, and media into queryable\, governed datasets alongside traditional structured data.\n\nWe walk through how to design a multi-modal ingestion pipeline\, extract structured signals from unstructured content using AI\, store everything in Iceberg tables with schema evolution and time travel\, and query across modalities using a single engine. Real-world patterns from customer support analytics\, retail intelligence\, and compliance document processing illustrate the architecture in action. Attendees leave with a clear blueprint for moving beyond traditional data lakes toward AI-ready\, multi-modal data platforms.
CATEGORIES:4 - MORE TECHNICAL
LOCATION:(g) Minnetonka\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:b9a716c860849b38bf5f13ef58f676c3
URL:http://datatech2026.sched.com/event/b9a716c860849b38bf5f13ef58f676c3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T160000Z
DTEND:20260515T163000Z
SUMMARY:Enhancing Credit Risk Monitoring: Integrating Diverse Data Pipelines and Advanced Modeling
DESCRIPTION:In the current dynamic financial landscape\, effective credit risk monitoring necessitates a harmony between advanced data science techniques and human expertise. This presentation explores the development of a comprehensive credit risk monitoring system that integrates complex data pipelines alongside domain experts from finance and investment management. We will address the challenges of harmonizing multiple data sources through both internal system changes and external services. We will discuss various modeling approaches that were considered including the Hidden Markov Models and XGBoost.\n\nAs a centralized data science team\, domain experts from the business team are crucial to our development process. This collaboration ensures that financial nuances and industry-specific features are effectively captured and explained\, enhancing predictive capabilities and transparency for end-users. We will discuss considerations for model delivery as well as a measurement framework to evaluate model benefits across a multi-stage production plan to establish trust while advocating for data science benefits. Attendees will gain insights into how this integrated framework not only improves risk prediction but also fosters a dynamic collaborative environment for continuous innovation and learning.
CATEGORIES:4 - MORE TECHNICAL
LOCATION:(h) Proverb / Edison\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:16b3e79e17aa5209ca8805cb0e2b975b
URL:http://datatech2026.sched.com/event/16b3e79e17aa5209ca8805cb0e2b975b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T160000Z
DTEND:20260515T163000Z
SUMMARY:Zen & the Art of Mario Kart Maintenance
DESCRIPTION:Advancements in LLM coding models have made it easier than ever to work in a domain you know nothing about\, but there are still plenty of challenges you'll run into. Follow along with me as I convert Mario Kart 64 into an immersive PC VR motion simulation experience using AI-assistants to code in a language I don't know and in a domain I've never worked in. N64 game code is not well-represented in LLM training data\, so I was often steering AI assistants through low-information territory\, trying to recognize when a model was pattern-matching instead of reasoning\, and structure my collaboration to get useful output I couldn't always fully evaluate. The lessons I took from that journey now shape how I work with AI on unfamiliar codebases in my day job\, and they're more practical than one might expect from a weekend project involving virtual go-karts.
CATEGORIES:4 - MORE TECHNICAL
LOCATION:(c) Alaska\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:857416950cd91b3dd6ac2698cfc0e663
URL:http://datatech2026.sched.com/event/857416950cd91b3dd6ac2698cfc0e663
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T164500Z
DTEND:20260515T171500Z
SUMMARY:A Game Theorist's Guide to AI Governance
DESCRIPTION:As corporations shift from standalone AI models to multi-agent systems—where a "supervisor" agent hands off tasks to specialized teammates—AI governance is evolving from worrying about individual model flaws to tackling the trickier risks of coordination. Think of it like a corporate hierarchy: delegation creates blind spots (agents handle private data\, hidden reasoning steps\, and tool results) and can spark misaligned motives\, leading to "agency loss"—the frustrating gap between what you intend and what the system actually delivers. Drawing from recent research\, I'll frame these dynamics as classic principal-agent problems from economics and game theory\, where tools like addressing moral hazard\, countering adverse selection\, and crafting smart mechanisms offer sharper insights than rigid checklists ever could.\n\nIn this talk\, we'll unpack how everyday MAS governance tactics—everything from evaluation suites and risk grading to release checkpoints\, live monitoring\, and escalation protocols—map onto game-theoretic levers that reshape the incentives and equilibria. You'll walk away with a hands-on framework for treating multi-agent setups as strategic games: designing payoffs\, verifications\, and oversight to curb deception\, boost dependability\, and turn agentic AI into something truly decision-ready.
CATEGORIES:3 - BUSINESS & TECHNICAL
LOCATION:(b) Cafeteria\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:1c670c5a5f1cd56e552cc82c510ac9c1
URL:http://datatech2026.sched.com/event/1c670c5a5f1cd56e552cc82c510ac9c1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T164500Z
DTEND:20260515T171500Z
SUMMARY:From Data Strategy to Deal Value: How AI-Ready Data Environments Drive Exit Multiples
DESCRIPTION:Why this topic?\nConnects data science work directly to tangible business outcomes (M&A valuations)Addresses the "so what?" question many practitioners face when justifying data investmentsLeverages decades of speaker's M&A experience that conference attendees may not have heard much on\nAudience takeaways?\nHow acquirers evaluate data maturity during due diligenceThe valuation premium differential between AI-ready vs. fragmented data environmentsCommon data architecture red flags that kill deals or reduce valuations
CATEGORIES:3 - BUSINESS & TECHNICAL
LOCATION:(c) Alaska\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:0f8196b1a161fb161c058f48b74182ff
URL:http://datatech2026.sched.com/event/0f8196b1a161fb161c058f48b74182ff
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T164500Z
DTEND:20260515T171500Z
SUMMARY:SAR Q-Learning for Multi-Regional Inventory Optimization
DESCRIPTION:Effective inventory management across multiple regions is often hindered by stochastic demand\, inter-regional dependencies\, and lead-time uncertainties. In this study\, we introduce a spatially-aware reinforcement learning framework (SAR-Q-Learning) that integrates region-specific demand forecasting with multi-agent Q-learning to optimize inventory decisions across a network of warehouses. Using the UCI Online Retail dataset\, we estimate autoregressive demand dynamics for each region and incorporate spatial correlations to guide policy learning. Empirical results show that the SARQ-Learning agent successfully balances service levels\, holding costs\, and stockouts\, achieving higher mean rewards and more stable final inventories compared to naive or independent policies. Visual analyses of weekly sales\, AR(1) parameters\, training convergence\, and forecasted inventory trajectories demonstrate the model’s capability to capture complex temporal and spatial patterns. These findings underscore the potential of spatially-informed reinforcement learning for robust\, data-driven inventory control in multi-regional supply networks
CATEGORIES:3 - BUSINESS & TECHNICAL
LOCATION:(g) Minnetonka\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:b28f606abec8f2e91988e9ec1d20df1e
URL:http://datatech2026.sched.com/event/b28f606abec8f2e91988e9ec1d20df1e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T164500Z
DTEND:20260515T171500Z
SUMMARY:Score Big with the Right Data Strategy
DESCRIPTION:Data Privacy\, Consent & Your MarTech EcoSystem\n\nCompanies are collecting unprecedented amounts of sensitive data\, and yet privacy regulations\, consumer expectations\, and supply chain accountability are shifting faster than most MarTech stacks can adapt.\n\nIn this rapidly changing and AI-fueled landscape\, consent and trust are increasingly important\, and when used properly\, can become your competitive advantage.\n\nIn this presentation\, Scotty will outline the biggest data and consent challenges companies face\, and share practical resources to fix them.\n\nYou’ll learn how to make permission travel with your data\, protect customers while still ensuring ROI\, all while preparing your MarTech stack for responsible AI and future changes.
CATEGORIES:3 - BUSINESS & TECHNICAL
LOCATION:(d) Nokomis\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:ad9ccc9ce19a96fdf0aeb1ab532801ce
URL:http://datatech2026.sched.com/event/ad9ccc9ce19a96fdf0aeb1ab532801ce
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T164500Z
DTEND:20260515T171500Z
SUMMARY:Are TSFMs a Magic Wand? A Deeper Dive into How to Choose the Right Model
DESCRIPTION:This talk builds on prior work presented at FASTCon 2026.\n\nTime Series Foundation Models (TSFMs) are gaining traction in forecasting\, but their real-world behavior is more nuanced than the hype suggests. In our experiments\, we observe that performance varies across data regimes\, forecast horizons\, and retraining setups—with only small differences among top models.\n\nThis talk focuses on the why behind these results.\n\nWe connect architectural choices in models like Chronos/Chronos2\, TimeGPT\, and TiReX to their observed behavior—covering data efficiency\, generalization\, regime sensitivity\, and stability.\n\nRather than asking “which model is best\,” we provide a practical framework for understanding when and why each model works\, helping practitioners make more informed\, context-driven decisions.
CATEGORIES:4 - MORE TECHNICAL
LOCATION:(h) Proverb / Edison\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:43808418d6c06ecb9eaf4f62cff2de0a
URL:http://datatech2026.sched.com/event/43808418d6c06ecb9eaf4f62cff2de0a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T164500Z
DTEND:20260515T171500Z
SUMMARY:Right-Sizing AI: Architectural Choices Beyond Model Selection
DESCRIPTION:Should you use an off-the-shelf AI service? Compose narrow tools into a pipeline? Strip an open-source model for parts? Build a chat--bot? &nbsp\; For one SDG client\, each of these has been the correct answer to a different problem. Through four short case studies spanning video\, analytics\, accounting\, and ETL\, this talk offers a practical framework for matching AI tools to problems. The core claim: in production AI\, the architecture decision usually matters more than the model decision.
CATEGORIES:4 - MORE TECHNICAL
LOCATION:(e) Harriet\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:0dc50d9d6200c814db4e8396140f942d
URL:http://datatech2026.sched.com/event/0dc50d9d6200c814db4e8396140f942d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T164500Z
DTEND:20260515T171500Z
SUMMARY:Can Enterprise AI Agents Actually Collaborate? Lessons from SimRetail - A Multi-Agent AI Simulator for Enterprise-Scale Retail Assortment Planning
DESCRIPTION:Can multiple AI agents\, built across different frameworks\, owned by different teams\, and trained on different data sources\, come together to solve an enterprise-scale problem? That's the central question SimRetail\, developed at Target Corporation and accepted at AAMAS 2026 (the International Conference on Autonomous Agents and Multiagent Systems)\, is built to explore.\n\nRetail assortment planning is a complex\, high-stakes decision that has traditionally required human merchandisers to manually synthesize signals across market trends\, sales performance\, vendor constraints\, and consumer behavior. SimRetail reimagines this as a multi-agent collaboration challenge\, where specialist agents spanning trend research\, merchandising analytics\, and vendor intelligence must coordinate reasoning across multiple decision cycles to produce a coherent\, consumer-validated assortment. A persona-based scoring agent\, powered by NVIDIA's Nemotron-Personas-USA dataset of over 181\,000 synthetic consumer profiles\, then evaluates the output against eight buyer archetypes\, stress-testing whether the agents' collective judgment actually translates to real consumer resonance. \n\nAttendees will walk away with practical insights into multi-agent system design\, agent-to-agent communication protocols such as A2A\, MCP\, challenges and evaluation of various agentic AI frameworks\, observability solutions\, synthetic persona evaluation\, and building explainable agentic pipelines.
CATEGORIES:5 - TECHNICAL
LOCATION:(a) Theater\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:c2b2f784ed43398f0eaa6017d79f86e3
URL:http://datatech2026.sched.com/event/c2b2f784ed43398f0eaa6017d79f86e3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T171500Z
DTEND:20260515T181500Z
SUMMARY:Lunch
DESCRIPTION:\n
CATEGORIES:
LOCATION:(b) Cafeteria\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:70e3191ed9dd930877af459f31a1ea1a
URL:http://datatech2026.sched.com/event/70e3191ed9dd930877af459f31a1ea1a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T181500Z
DTEND:20260515T184500Z
SUMMARY:Beyond Technical Skills: Practical Habits for Effective Data Professionals
DESCRIPTION:Data and analytics professionals are often promoted and praised for their technical skills. Yet many stall not because they lack capability\, but because they struggle to communicate\, influence\, prioritize\, or collaborate effectively. As AI continues to automate coding and technical execution\, these human skills matter more than ever.\n\nIn this session\, we’ll explore the practical interpersonal and problem‑solving behaviors that help data professionals be more effective in their day‑to‑day work. Using a familiar but modernized framework inspired by The 7 Habits of Highly Effective People\, this talk translates timeless principles into concrete actions for analytics roles - running better meetings\, framing problems clearly\, partnering with stakeholders\, and working productively with AI tools. Attendees will leave with actionable habits they can apply immediately to increase their impact\, effectiveness\, and long‑term relevance in a rapidly changing field.
CATEGORIES:1 - BUSINESS
LOCATION:(a) Theater\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:2a4b7553ba46e006a4f4aa7980feeaa6
URL:http://datatech2026.sched.com/event/2a4b7553ba46e006a4f4aa7980feeaa6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T181500Z
DTEND:20260515T184500Z
SUMMARY:Crowd-Sourced Truth
DESCRIPTION:When official narratives don't match what communities are seeing on the ground\, grassroots organizations turn to crowd-sourced data to fill the gaps. But good intentions alone don't produce reliable data. This talk walks through the key questions any community group should ask before\, during\, and after launching a collection effort.
CATEGORIES:3 - BUSINESS & TECHNICAL
LOCATION:(c) Alaska\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:f1da540464a975eead75d41536fa9cd9
URL:http://datatech2026.sched.com/event/f1da540464a975eead75d41536fa9cd9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T181500Z
DTEND:20260515T184500Z
SUMMARY:From Batch to Streaming: Data Architecture for Context-Aware Personalization
DESCRIPTION:Real-time personalization requires more than fast model inference. It depends on data systems that can combine batch computation\, streaming signals\, feature management\, and ranking logic while operating under strict latency and reliability constraints.\n\nThis talk presents a production-inspired architecture for context-aware personalization\, showing how batch pipelines\, streaming data flows\, feature stores\, and downstream scoring or ranking layers work together to support dynamic user experiences at scale. Rather than focusing only on model development\, the session highlights the data architecture needed to make personalization practical\, resilient\, and responsive in real-world systems.\n\nThe talk also explores key engineering trade-offs\, including data freshness versus latency\, feature completeness versus response time\, and resilience strategies when parts of the pipeline are delayed or degraded. Attendees will gain a practical view of how modern data platforms can support intelligent decisioning and real-time personalization in production environments.
CATEGORIES:3 - BUSINESS & TECHNICAL
LOCATION:(g) Minnetonka\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:c539f5ea2962bfe9b98b604a3d7351a7
URL:http://datatech2026.sched.com/event/c539f5ea2962bfe9b98b604a3d7351a7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T181500Z
DTEND:20260515T184500Z
SUMMARY:Practical Workflow Capture: Reduce Rollout Risk in AI Adoption Initiatives
DESCRIPTION:Automation is only as effective as the work we automate. In AI initiatives\, it’s easy to skip or shortcut confirming that we’re automating the right work in the first place. This talk argues that workflow capture\, an updated and expanded take on “process mapping\,” is an overlooked make-or-break prerequisite for AI readiness. It creates shared understanding and reduces rework\, mis-scoped builds\, and adoption surprises. We’ll cover why formal\, heavyweight mapping is often unnecessary and counterproductive\, and how “lightweight but rigorous” capture can look in practice.\n\nYou’ll leave with a practical recipe for efficient\, modern workflow capture\, plus a checklist to stress-test your own approach\, based on ongoing field testing and refinement. The recipe supports the data and analytics needs of AI adoption initiatives and scales through a mix of expert facilitation and self-service input from the broader organization.\n\nYou’ll also leave with clarity on why analytics and data science professionals should advocate early for effective workflow capture\, so strong technical work translates into real-world impact.
CATEGORIES:3 - BUSINESS & TECHNICAL
LOCATION:(d) Nokomis\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:da170339b3cbf6e08e92e68702c4103e
URL:http://datatech2026.sched.com/event/da170339b3cbf6e08e92e68702c4103e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T181500Z
DTEND:20260515T184500Z
SUMMARY:A Beginner's Guide to Building a Recommendation System with Vector Search
DESCRIPTION:One of the easiest ways to have AI do some of the heavy lifting on your customer-facing website\, is with a vector search based recommendation system. In this session we will focus on the implementation for building the recommendation system's components. We will start from database modeling\, to building the data access layers\, and on up to showing the results on the front end. We will also cover data loading and generating vector embeddings. Join us to see how to leverage restful services\, embedding models\, and large-scale databases.
CATEGORIES:4 - MORE TECHNICAL
LOCATION:(b) Cafeteria\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:b4245a050d187664de8efea73baec2ee
URL:http://datatech2026.sched.com/event/b4245a050d187664de8efea73baec2ee
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T181500Z
DTEND:20260515T184500Z
SUMMARY:The Computable Phenotype Library (CPL): Managing computable phenotypes for better real-world evidence
DESCRIPTION:Teams tasked with real world evidence generation often spend significant effort defining patient populations\, clinical events\, and procedures - only to manage those definitions in spreadsheets\, ad hoc SQL\, and scattered documentation. These approaches can negatively impact the quality of evidence generated via introduction of errors\, no formal validation\, and lack of proper governance.\n\nIn this session\, I will introduce the Computable Phenotype Library (CPL): A lightweight framework for managing clinical definitions as reusable\, validated\, governed assets. Drawing from a practical case study in real-world evidence operations of a medical device company (Philips)\, I will cover the framework's core components: The master code set\, an internal user interface\, reusable phenotype artifacts\, automated validation\, centralized cloud storage\, and an external user interface. Attendees will leave with practical design principals for improving phenotype quality\, maintainability\, reproducibility\, and trust - along with lessons learned about why adoption is often the hardest part of building better data infrastructure.
CATEGORIES:4 - MORE TECHNICAL
LOCATION:(e) Harriet\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:9ae35f79bf43724cf774c301eac0145a
URL:http://datatech2026.sched.com/event/9ae35f79bf43724cf774c301eac0145a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T181500Z
DTEND:20260515T184500Z
SUMMARY:Flow Based and Score Based Generative Models
DESCRIPTION:Generative models generate objects (picture\, animation\, sound\, etc) by iteratively converting noise into data. This is done by the simulation of ordinary or stochastic differential equations (ODEs/SDEs). The two techniques allow us to construct\, train\, and simulate such ODEs/SDEs with deep neural networks.\n\nIn this talk I will introduce the two methods and will give simple examples.
CATEGORIES:5 - TECHNICAL
LOCATION:(f) Bde Maka Ska\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:3ccfd84d4f24fe3cf702f9856fdbc72b
URL:http://datatech2026.sched.com/event/3ccfd84d4f24fe3cf702f9856fdbc72b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T190000Z
DTEND:20260515T193000Z
SUMMARY:High-Signal Networking in a Noisy\, AI-Driven Market
DESCRIPTION:In today’s AI-driven and increasingly competitive job market\, networking has evolved from a “nice to have” into a critical career skill. As applying becomes easier and more automated\, standing out becomes harder. This can push more opportunities into the hidden market where relationships matter most. This session explores how to build a high-signal network that creates real opportunities\, whether you’re actively job searching or looking to grow in your current role.\n\nWe’ll cover practical strategies for expanding your network both online and in person\, with a focus on authentic\, non-transactional relationships. You’ll learn how to turn conversations into opportunities\, build a personal brand that attracts the right people\, and create a simple system for consistent follow-up. Drawing from real-world experience growing communities like the Twin Cities Data Science & Analytics (TCDSA) network and Innovation Studio\, this session delivers actionable frameworks to help you build a network that compounds over time and supports long-term career growth.
CATEGORIES:1 - BUSINESS
LOCATION:(f) Bde Maka Ska\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:9a3e70929fda7c238d8bcbf6ff2137ae
URL:http://datatech2026.sched.com/event/9a3e70929fda7c238d8bcbf6ff2137ae
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T190000Z
DTEND:20260515T193000Z
SUMMARY:AI in Drug Discovery
DESCRIPTION:We will trace the lifecycle of a pre-clinical R&D drug discovery and the areas where AI has accelerated development as well as areas where AI is causing problems. It's never been easier to generate hypotheses\, but validating them in lab is still a bottleneck. How does the industry handle a tsunami of AI generated structures?\n\nWe'll discuss things like pharmacophore development\, structure-activity-relationships\, assay design\, ADMET\, all the way up to IND filings.\n\nThis talk will be technical\, but assumes no prior knowledge of the drug discovery process or the models used.
CATEGORIES:3 - BUSINESS & TECHNICAL
LOCATION:(e) Harriet\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:4b7ed956004fe791e7bfc85c5f1f5479
URL:http://datatech2026.sched.com/event/4b7ed956004fe791e7bfc85c5f1f5479
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T190000Z
DTEND:20260515T193000Z
SUMMARY:From Black Boxes to Open Glass: Democratizing Marketing Mix Models with GenAI and Open-Source analytics
DESCRIPTION:The era of relying on vendor-based proprietary and opaque ‘Black Box’ Marketing Mix models is over. By leveraging open-source analytics frameworks and Generative AI as a semantic layer\, businesses can now utilize a scalable stack that can bridge the gap between complex Bayesian statistical output and actionable business strategy\, making marketing analytics and science more accessible and inexpensive for the broader audiences.\n\nAttendees will learn how to build transparent analytics pipeline that balances statistical rigor from Bayesian MMMs with business agility and how organizations can empower non-technical stakeholders to interact directly with statistical model output.
CATEGORIES:3 - BUSINESS & TECHNICAL
LOCATION:(d) Nokomis\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:b8dcb04b9f5f596c44e8b693ff686b5d
URL:http://datatech2026.sched.com/event/b8dcb04b9f5f596c44e8b693ff686b5d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T190000Z
DTEND:20260515T193000Z
SUMMARY:TRASH TALKING- Smart Autonomous Connected Refuse Collection Vehicle
DESCRIPTION:What if garbage trucks weren’t just garbage trucks\, but smart\, connected systems that optimized routes\, reduced emissions\, and improved recycling rates? That’s exactly what we’ll be exploring—how AI and IoT are transforming waste management to be more efficient\, sustainable\, and intelligent.\nAI in Refuse Management – Optimizing operations & improving safetyIoT-Driven Connectivity – Real-time monitoring of vehicle health & recycling contaminationAutonomous Trucks – The role of self-driving tech in waste collectionSustainability Impact – Using technology to reduce landfill waste & increase recyclingWhether you're passionate about tech\, sustainability\, or smart cities\, this is a conversation you won’t want to miss! \n\n
CATEGORIES:3 - BUSINESS & TECHNICAL
LOCATION:(g) Minnetonka\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:5168739e41201c0fd0d592cebc3d7b25
URL:http://datatech2026.sched.com/event/5168739e41201c0fd0d592cebc3d7b25
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T190000Z
DTEND:20260515T193000Z
SUMMARY:LLMs as Data Scientists: What Actually Works
DESCRIPTION:Large language models are increasingly being used to write SQL\, summarize datasets\, and automate parts of the data science workflow. In some cases\, they perform remarkably well. In others\, they fail in subtle but systematic ways\, including misinterpreting metrics\, hallucinating joins\, or reasoning inconsistently over schemas. This talk examines what actually happens when LLMs are applied to real structured data\, using concrete examples to separate surface fluency from reliable analytical behavior.\n\nWe will explore a practical approach to improving reliability by providing LLMs with interpretable\, structured components derived directly from the data itself. I will demo how exposing explicit statistical structure changes model behavior and reduces hallucination while improving reasoning over real datasets. The goal is to clarify where LLMs add value today\, where they remain brittle\, and which system design choices materially improve performance in analytics workflows.
CATEGORIES:4 - MORE TECHNICAL
LOCATION:(a) Theater\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:c7cdac8d917debaeeb1aa8d2f7c8738d
URL:http://datatech2026.sched.com/event/c7cdac8d917debaeeb1aa8d2f7c8738d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T190000Z
DTEND:20260515T193000Z
SUMMARY:Single\, Multi\, Orchestrator: Choosing the Right Agent Architecture
DESCRIPTION:Agent architecture decisions are often made before the tradeoffs are fully understood. Reaching for multi-agent can add unnecessary coordination overhead\, while stretching a single agent too far can create brittle systems that should have been split or orchestrated earlier.\n\nThis session provides a practical framework for choosing between single-purpose\, multi-purpose\, multi-agent\, and orchestrated designs. Informed by real production systems in freight operations and ISO 9001 compliance\, it gives attendees clearer signals for when complexity is justified\, when specialization adds value\, and how to design agent systems that hold up in production.
CATEGORIES:4 - MORE TECHNICAL
LOCATION:(c) Alaska\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:4b59daf70980cc9bd1bc894a6f83f50d
URL:http://datatech2026.sched.com/event/4b59daf70980cc9bd1bc894a6f83f50d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T190000Z
DTEND:20260515T193000Z
SUMMARY:Why Your Model Lies (And What to Do About It): Causal Reasoning in Product Analytics
DESCRIPTION:Your model is lying to you. Not intentionally—but somewhere in your company\, a model is telling you something matters when it doesn't. This talk exposes the gap between correlation and causation in product analytics\, and shows you how to think causally about product decisions. Using two real-world case studies\, we'll walk through why feature importance misleads you and how to design experiments that isolate true causal effects. Learn the framework that turns data scientists into product strategists.
CATEGORIES:4 - MORE TECHNICAL
LOCATION:(b) Cafeteria\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:1eb5899b019a1fa2969ce65272126d8d
URL:http://datatech2026.sched.com/event/1eb5899b019a1fa2969ce65272126d8d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T194500Z
DTEND:20260515T201500Z
SUMMARY:Why Manufacturing Data Fails to Deliver ROI and How to Fix It
DESCRIPTION:Manufacturing organizations collect massive amounts of process data\, yet many struggle to translate it into measurable financial impact. This talk explores how to bridge the gap between process engineering insights and business outcomes\, using examples to show how raw manufacturing data can be transformed into actionable insights tied to cost savings\, yield improvement\, and operational efficiency. We’ll cover practical workflows including data cleansing\, contextualization\, and aligning metrics with financial KPIs.\n\nAttendees will leave with a clear framework for connecting shop floor data to business value\, along with examples of where traditional data science efforts fall short—and how to correct them. This session is designed for data scientists\, engineers\, and business leaders who want to move beyond dashboards and deliver measurable ROI.
CATEGORIES:2 - MORE BUSINESS
LOCATION:(e) Harriet\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:85f5f78c37f801e1f1cfa6fb3502f2c5
URL:http://datatech2026.sched.com/event/85f5f78c37f801e1f1cfa6fb3502f2c5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T194500Z
DTEND:20260515T201500Z
SUMMARY:False Positives\, Real Consequences: Adventures in Government ML
DESCRIPTION:Local government data is a machine learning goldmine – and a minefield. A false positive isn’t a bad recommendation or misplaced ad: it’s someone’s food assistance\, healthcare\, or housing stability on the line. Our team in Hennepin County has spent over seven years piloting classification models for these kind of high-stakes risks. We’ll cover the technical stuff – model design\, evaluation\, data quality under equity constraints – and the harder stuff: building trust with leadership and communities at the receiving end. Familiarity with supervised learning helps. A tolerance for ambiguity helps more.
CATEGORIES:3 - BUSINESS & TECHNICAL
LOCATION:(b) Cafeteria\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:4e056d7897e31e3ea7ea4a9b316db7f4
URL:http://datatech2026.sched.com/event/4e056d7897e31e3ea7ea4a9b316db7f4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T194500Z
DTEND:20260515T201500Z
SUMMARY:From No Code to No Monitoring
DESCRIPTION:A year ago I set a specific goal: build a production system without writing a single line of application code. I reached it. And then it kept going. I also stopped reading the generated code\, stopped touching configuration files\, stopped issuing CLI commands\, and eventually the running system began monitoring itself and correcting drift before I was notified. This talk is the story of how each of those steps happened\, what structural discipline made them possible\, and where the cascade ends when you keep asking what comes next.\n\nThe discipline is called Generative Specification. Attendees will leave with a clear understanding of why AI sessions drift and what architecture prevents it\, what a complete specification looks like for a real data or software system\, and a concrete starting point for their own work. We will also look at what happens when this approach transfers beyond one practitioner: in April 2026\, 60 developers worked on two projects under two conditions: standard AI assistance versus specification-governed AI assistance. The results have direct implications for data and analytics teams building on AI tooling.
CATEGORIES:3 - BUSINESS & TECHNICAL
LOCATION:(f) Bde Maka Ska\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:688d212a017b32807fc6a00bbb82f709
URL:http://datatech2026.sched.com/event/688d212a017b32807fc6a00bbb82f709
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T194500Z
DTEND:20260515T201500Z
SUMMARY:Systematic Innovation as Context Architecture: Matrix Morphology and the Future of Model Augmentation
DESCRIPTION:This presentation introduces a novel paradigm for augmenting large language models—not with more data\, but with better-structured reasoning. Drawing on recent benchmark evidence that structured knowledge graphs dramatically out-perform conventional augmentation architectures in both grounding quality and production cost\, the presentation suggests that the next frontier in model augmentation is morphological—encoding not just what we know\, but how we think. It introduces Matrix Morphology\, a domain-agnostic framework for systematic innovation that is rooted in ancient philosophy and forms the basis of a patented invention in Web architecture. Refined through a multitude of applications across technical\, organizational\, and social domains and manifested in a primitive AI implementation\, the method is a candidate architecture for this new class of external augmentation.\nParticipants will explore how identification\, definition\, and resolution of contradictions—the core mechanism of Matrix Morphology—can be encoded as a structured retrieval schema for ingesting cognitive work that the model would otherwise perform poorly or in-consistently. Key takeaways include: Why knowledge form determines reasoning quality as much as knowledge content\; how systematic innovation methodologies might translate into a new class of augmentation layer\; and how this approach could resolve a fundamental tension in current AI implementations between generative fluency and structured problem-solving.The session will invite audience participation around two open questions:\n What is the optimal direction in encoding architecture / schema structure for systematic knowledge—graph\, matrix\, hybrid\, or entirely some other approach?What contradictions in the participant's domain remain un-solved—and what might it mean to explore those problems in an AI implementation that already knows how to navigate their dimensionality?
CATEGORIES:3 - BUSINESS & TECHNICAL
LOCATION:(g) Minnetonka\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:af10bcc776090869b2510ad6a263b960
URL:http://datatech2026.sched.com/event/af10bcc776090869b2510ad6a263b960
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T194500Z
DTEND:20260515T201500Z
SUMMARY:When AI Stops Asking and Starts Acting: The Rise of Autonomous Intelligence
DESCRIPTION:For the last decade\, AI has been very good at answering questions. We ask it for insights. We ask it for predictions. We ask it what might happen next. But that’s not autonomy. Autonomous intelligence begins when AI stops asking us what to do—and starts acting on trusted knowledge\, in real time\, within the business itself. Today\, we’re at that inflection point. Not because models got bigger—but because AI finally has access to enterprise knowledge it can reason over\, decide with\, and act on continuously. This is the rise of autonomous intelligence—and it’s changing what enterprises can do.
CATEGORIES:3 - BUSINESS & TECHNICAL
LOCATION:(a) Theater\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:10d48240d99b6470360c0fd97c3556e3
URL:http://datatech2026.sched.com/event/10d48240d99b6470360c0fd97c3556e3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T194500Z
DTEND:20260515T201500Z
SUMMARY:From Microphone to Insight: Extracting Structured Data from Voice
DESCRIPTION:Audio is one of the most valuable data sources your team isn't using. Not because the signal isn't there. Because getting it into a usable form has always been hard. Now we have better tooling and models.\n\nThis talk covers the full pipeline: raw audio capture\, preprocessing\, transcription\, and pulling structured insight out the other end. Decisions\, action items\, questions\, intent. Stuff you can actually query and act on.\n\nTranscription and voice control are having a moment right now. Every meeting\, support call\, and customer interview has signal in it that most teams never see. If you work in healthcare\, fintech\, or enterprise software\, audio data is closer to production-ready than you think. This talk shows you what the path actually looks like.
CATEGORIES:4 - MORE TECHNICAL
LOCATION:(c) Alaska\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:f493ac7ae3fe8ee169c13aa71b5fe454
URL:http://datatech2026.sched.com/event/f493ac7ae3fe8ee169c13aa71b5fe454
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T194500Z
DTEND:20260515T201500Z
SUMMARY:Using Utility Theory and Discrete Choice Modeling to Model Revenue Retention
DESCRIPTION:We will discuss the theory and practice of utility theory and discrete choice modeling. Essentially\, how consumers make decisions between different brands and within the same brand\, specifically for consumer electronics. We will also discuss how we use this at Best Buy to model consumer behavior and simulate what will happen to Best Buy's revenue if we close or open a store.
CATEGORIES:4 - MORE TECHNICAL
LOCATION:(d) Nokomis\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:5d2630341b75c89574eecc559a449807
URL:http://datatech2026.sched.com/event/5d2630341b75c89574eecc559a449807
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T203000Z
DTEND:20260515T210000Z
SUMMARY:Beyond the Hype: The Real Missing Layer in Agentic AI Analytics
DESCRIPTION:Everyone is talking about agents\, copilots\, and AI-native analytics. But as enterprises move from demos to deployment\, many are discovering the same thing: the missing piece isn’t another model — it’s context.\n\nThis talk cuts through the noise around agentic AI and explores what a true context engine actually is\, why many current approaches fall short\, and how the role of the analyst is evolving into that of an AI Context Manager. We’ll also discuss how companies at different stages of AI maturity approach this problem differently — from early experimentation to operationalized\, trusted AI systems at scale.
CATEGORIES:3 - BUSINESS & TECHNICAL
LOCATION:(e) Harriet\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:55e56948405f68e6e825f1e1d9bd9eb0
URL:http://datatech2026.sched.com/event/55e56948405f68e6e825f1e1d9bd9eb0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T203000Z
DTEND:20260515T210000Z
SUMMARY:Physics\, Not Clicks: 5 Realities of Industrial Data Science in the Age of Prompts
DESCRIPTION:Agentic AI and prompt engineering are dominating the headlines\, but in the industrial world\, a wrong prediction doesn’t just show a bad ad or a wrong purchase recommendation—it breaks a machine. Deploying AI in physical settings involving IoT\, sensor data\, and engineering constraints is fundamentally different from consumer tech\, and in this space\, physics still outranks prompts. If data science teams and business leaders don’t have a shared understanding of these unique industrial challenges\, project timelines can easily slip\, and quality can suffer\; conversely\, when technical teams miss the commercial context\, they risk building elegant models that solve the wrong problems.\n \nThis talk serves as a "two-way street" translation guide to bridge the gap between technical execution and business strategy. We will explore 5 timeless realities\, with examples\, required to make industrial AI work—ranging from the integration of domain knowledge and physics-based feature engineering\, to the critical necessity of industrial-grade MLOps and strict performance benchmarking.
CATEGORIES:3 - BUSINESS & TECHNICAL
LOCATION:(g) Minnetonka\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:4abd8c8c631027af03b43a9ad84d2bff
URL:http://datatech2026.sched.com/event/4abd8c8c631027af03b43a9ad84d2bff
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T203000Z
DTEND:20260515T210000Z
SUMMARY:Enhancing Warranty Claims Interpretation with Large Language Models at Trane Technologies
DESCRIPTION:This work highlights how Trane Technologies is applying Large Language Models (LLMs) alongside broader Natural Language Processing techniques to enhance the interpretation of unstructured warranty claim narratives. The approach enables clearer insight extraction from text based warranty claims\, supporting more consistent categorization and earlier visibility into emerging product issues.
CATEGORIES:4 - MORE TECHNICAL
LOCATION:(d) Nokomis\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:5312418b38aad015cb43046a68bf0273
URL:http://datatech2026.sched.com/event/5312418b38aad015cb43046a68bf0273
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T203000Z
DTEND:20260515T210000Z
SUMMARY:From Full Deployments to Selective Releases: Safer CI/CD for Data Pipelines
DESCRIPTION:As data platforms scale\, managing deployments across multiple teams and parallel feature development becomes increasingly complex in modern cloud platforms. Azure Data Factory (ADF)\, while widely used for data orchestration\, relies on a full-state deployment model—forcing entire pipeline states to be promoted across environments. This creates a critical challenge: how to safely release only intended changes without impacting in-progress work.\n\nIn this session\, we present a practical approach to enabling selective deployments in ADF using a manifest-driven packaging pattern integrated into CI/CD workflows. By introducing dependency-aware subset generation\, teams can deploy only required pipelines while safely managing related datasets\, triggers\, and linked services.\n\nWe will cover real-world implementation patterns\, including validation strategies\, incremental deployment\, and separation of application and infrastructure concerns. The approach has been applied in enterprise environments where data reliability\, governance\, and release independence are critical.\n\nAttendees will gain actionable insights and reusable patterns to improve deployment safety\, reduce operational risk\, and enable intent-driven delivery in modern data platforms.
CATEGORIES:4 - MORE TECHNICAL
LOCATION:(f) Bde Maka Ska\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:bf38a9109dfdfd11c1de7f82eb76ee43
URL:http://datatech2026.sched.com/event/bf38a9109dfdfd11c1de7f82eb76ee43
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T203000Z
DTEND:20260515T210000Z
SUMMARY:From Tylenol to Cancer Drugs: Demand Forecasting for 15\,000 Drugs at 26 Locations
DESCRIPTION:At Cencora\, we handle more than 15\,000 different medicines\, from everyday staples like Tylenol to the latest treatments for cancer\, all shipped out from 26 distribution centers across the country. To keep everything running smoothly\, we need to create a separate demand forecast for each product at each location. This isn’t just important for the common drugs you see at the pharmacy\, but also for special medications that might be needed suddenly. Making sure we have the right amount of everything\, at the right place and time\, is a tricky problem and really matters for the millions of patients who receive our drugs.\n\nIn my presentation\, I’ll talk through the real-world data and technical issues we face when trying to get these forecasts right. I’ll explain what we actually mean by “demand” in our world\, and share how we’ve moved past using just simple averages. Now\, we use a mix of time series models and machine learning to build much more accurate forecasts for over 300\,000 product and location combinations.
CATEGORIES:4 - MORE TECHNICAL
LOCATION:(a) Theater\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:0fb52d2980a61578208b0a5612e50f06
URL:http://datatech2026.sched.com/event/0fb52d2980a61578208b0a5612e50f06
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T203000Z
DTEND:20260515T210000Z
SUMMARY:The Calibration Blindspot: Why AI Models Can't See Their Own Errors
DESCRIPTION:Frontier AI models are achieving remarkable accuracy on standard benchmarks\, but accuracy alone does not tell us whether a model knows what it does not know. In safety-critical domains like healthcare\, finance\, and law\, a model's ability to signal uncertainty is just as important as its ability to answer correctly.\n\nThis session presents the Calibration Blindspot Benchmark (CBB)\, an original three-task metacognition evaluation suite tested across 8 frontier models from 6 vendors\, Anthropic\, Google\, OpenAI\, DeepSeek\, QwenLM\, and Z.ai. Results revealed a structural blindspot: models maintained 100% confidence on every prediction regardless of correctness\, never forecasted their own errors (Error Recall = 0.000)\, yet detected others' errors near-perfectly (0.972-1.000). Models can see everyone's mistakes except their own. DeepSeek-R1 was the only model to fail entirely\, suggesting reasoning-trained models have a distinct metacognitive failure mode.\n\nAttendees will learn why confidence calibration matters for production AI deployment\, how to evaluate metacognition in LLMs\, and what this blindspot means for anyone building AI workflows in healthcare\, finance\, or legal settings.
CATEGORIES:4 - MORE TECHNICAL
LOCATION:(c) Alaska\, Best Buy HQ\, 7700 Knox Ave S\, Richfield\, MN 55423
SEQUENCE:0
UID:64fcc671cc9cba7d46a87ea48bef757c
URL:http://datatech2026.sched.com/event/64fcc671cc9cba7d46a87ea48bef757c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260518T091751Z
DTSTART:20260515T211500Z
DTEND:20260515T230000Z
SUMMARY:Networking Social
DESCRIPTION:Bring your lanyard to gain entry for complimentary food and drinks!
CATEGORIES:
LOCATION:Mallards\, 2300 W 80th 1/2 St\, Bloomington\, MN 55431
SEQUENCE:0
UID:fd51347384e94934f81996c17fccec5a
URL:http://datatech2026.sched.com/event/fd51347384e94934f81996c17fccec5a
END:VEVENT
END:VCALENDAR