2026 is a big year for #DataFamRisingStars. Hard to believe it’s already the fifth year of celebrating up-and-coming leaders in the data viz community. Some of you might remember when this was called #TableauNext, but the goal has always stayed the same: shine a light on the folks who are pushing the #DataFam forward with their work, generosity, and leadership.

Rising Stars aren’t just dashboard pros. They’re teachers, advocates, and experimenters who give back and help others level up. Many past honorees have gone on to become Tableau Ambassadors, Visionaries, Vizzie winners, or even join #TeamTableau. But right now, it’s not about the titles; it’s about the impact they’re making.

This whole thing works because the community keeps showing up for each other. Every nomination is a little act of recognition, and it’s that collective energy that keeps the #DataFam moving forward. Huge thanks to everyone who took the time to nominate, support, and lift up others this year. And a special shoutout to Annabelle Rincon for teaming up with me for 2026, and to Lindsay Betzendahl for creating the logo!

The 2026 #DataFamRisingStars Cohort, by the numbers

• 120+ nominations
• 82 individuals selected
• 17 countries represented

Cohort by country
USA (24), Japan (22), Nigeria (9), UK (7), India (6), Canada (2), Germany (2), and one Rising Star each from China, Egypt, France, Ghana, Italy, Korea, Pakistan, Spain, UAE, and Ukraine.

Not every nominee makes it into the final cohort. Some are already Tableau Ambassadors, Visionaries, or part of Tableau/Salesforce, and others haven’t yet shared their work widely. Every contribution matters, but this initiative is all about spotlighting those who are making a visible, community-driven impact right now.

The 2026 Rising Stars are a perfect example of the global, collaborative spirit that makes the #DataFam special… they’re just getting started.

Note: All images below are Annabelle’s frame with the likenesses of the people mentioned immediately above them.

DataFam Rising Stars 2026

Please note that each cohort's summary may include one or more nominators, and they are paraphrased for clarity. Careful attention was given to ensure the tone was consistent with the nominators’ voice.

Ajay Vishnu Adalla — DataFam Rising Star 2026

Location: USA

Tableau Public: https://public.tableau.com/app/profile/ajay.vishnu.addala/vizzes

LinkedIn: https://www.linkedin.com/in/ajay-vishnu/

Summary: Ajay has been a huge inspiration for the nominator throughout 2025. He is always posting great content on Tableau Public and are a Makeover Monday Machine. His vizzies are inspiring and you can tell he really puts the work in. Moreover, he resurrected the Philadelphia Tableau User Group this year and has already hosted 2 great sessions—jumping into the deep end by hosting an event featuring Tableau Next for their very first TUG. I know he was part of the 2025 cohort, but I feel like his star continues to rise.

• • •

Angela Henderson — DataFam Rising Star 2026

Location: USA

Tableau Public: https://public.tableau.com/app/profile/angelahenderson

LinkedIn: https://www.linkedin.com/in/angelahenderson2/

Ten-Minute Tuesday Appearance: https://youtu.be/xk9Cxw6L5KM?si=fy3iG_Oy8S50xvnw

Summary: Angela is deeply grateful for the opportunities to serve this ecosystem. Her simple goal is to humanize data. People call it ‘Stakeholder Whispering,’ listening carefully to translate complex needs into Visual Best Practices. She loves creating bespoke metrics for specific sectors, as seen in their 2025 articles on data solutions for the Healthcare, CPG, and Manufacturing industries. She is humbled by the interest from non-technical users in their work with custom shapes and icons to make data intuitive for everyone. Her WBE leadership has enabled them to bridge the gap between business ownership and analytics. 2025 was a whirlwind: from the ’10 Minute Tuesday’ podcast, to serving as ‘Professor for a Day’ and mentoring STEM students. Through ‘Data for Good’ pro bono work for non-profits, they aim to continue building bridges that help this community grow. If a role requires shrinking, it’s the wrong role; she is here to help everyone grow.

• • •

Anindita Mitra — DataFam Rising Star 2026

Location: USA

Tableau Public: https://public.tableau.com/app/profile/anindita.mitra21/

LinkedIn: https://www.linkedin.com/in/aninditamitra-/

Summary: Anindita consistently creates diverse and highly inspirational visualizations. Her work stands out for its creativity and impact, and I’ve personally referenced her dashboards multiple times as examples to learn from. Anindita was highlighted for her creativity, insight, and inspiration she brings to the DataFam community.

• • •

Ankur Napa — DataFam Rising Star 2026

Location: India

Tableau Public: https://public.tableau.com/app/profile/ankur.napa/vizzes

LinkedIn: https://www.linkedin.com/in/ankur-napa/

Article: https://www.mid-day.com/mumbai-guide/things-to-do/article/mid-day-45th-anniversary-special-mead-of-the-future-23368067

Summary: Ankur Napa is a self-taught Tableau developer and a co-leader of the Tableau Newbies User Group and the Bengaluru Tableau User Group. He supports the community through leadership, mentoring, Tableau Public work, and his YouTube channel, Before Interview, where he shares practical Tableau learning content. In India, he also teaches Tableau to underprivileged students who lack access to formal training and reliable internet, helping make data skills more inclusive. His work brings real-world Food & Beverage use cases into the community while empowering newcomers to build confidence with Tableau.

• • •

Ann Stolzman — DataFam Rising Star 2026

Location: USA

Tableau Public: https://public.tableau.com/app/profile/astolzman/vizzes

LinkedIn: https://www.linkedin.com/in/annstolzman/

X-Twitter: https://x.com/whizgidget

Summary: Ann has been so active and inspiring in the DataFam. From completing every B2VB challenge in 2025 to being an active participant in Workout Wednesday and creating an amazing Iron Viz entry, she also shares her thoughts and learning journey behind each viz. She is always encouraging and supporting others in the community.

• • •

Anna Clara Gatti — DataFam Rising Star 2026

Location: Italy

Tableau Public: https://public.tableau.com/app/profile/anna.clara.gatti/

LinkedIn: https://www.linkedin.com/in/anna-clara-gatti-155114175/

Summary: This marks Anna’s 2nd year as a DataFam Rising Star. Anna continued to be exceptional, and this year alone, three of her visualizations were selected as Viz of the Day. Her ideas and execution are consistently outstanding, and her work continues to inspire the community. Anna’s growth, creativity, and impact truly stand out.

• • •

Anubha Gupta — DataFam Rising Star 2026

Location: Canada

Tableau Public: https://public.tableau.com/app/profile/anubha.gupta/vizzes

LinkedIn: https://www.linkedin.com/in/anubha-g/

Summary: She has consistently worked to strengthen her Tableau and analytics skills, explored new techniques, and pushed herself to build dashboards that are both meaningful and visually thoughtful. Every project has been an opportunity to learn, and she has made it a point to openly share those learnings so others can benefit as well. What motivates her most is the #DataFam itself. She actively engages with fellow creators, celebrates their work, and contributes wherever she can, whether through feedback, encouragement, or resource sharing. Being part of this community has helped her grow, and she has tried her best to give back by supporting others on their journey. She believes that her commitment to continuous learning, willingness to share and uplift others, and genuine enthusiasm for data storytelling make her a strong fit for the #DataFamRisingStars2026 feature. She is excited to keep improving, contributing, and showing up for this amazing community.

• • •

Aokky — DataFam Rising Star 2026

Location: Japan

Tableau Public: https://public.tableau.com/app/profile/aokky/vizzes

Website: https://note.com/powerofmusic777

X-Twitter: https://x.com/powerofmusic777

Summary: Aokky is a #DataFamRisingStars 2026 candidate for his contributions to the Japanese DataFam through community building and operational support. As a member of the DATA Saber Council Operation-Zero team, Aokky supported the DATA Saber program by contributing to data management and operational activities, organizing local initiatives such as Tableau Umauma Kai in Kansai, and serving as the organizer of the DATA Saber Camp in Kansai. Through his positive energy, attentiveness, and ability to create welcoming spaces, Aokky has helped foster a supportive and enjoyable environment for growth, and he is expected to play an increasingly important role in energizing the Kansai DataFam in the years ahead.

• • •

Austin Gardner — DataFam Rising Star 2026

Location: USA

Tableau Public: https://public.tableau.com/app/profile/austin.gardner7665/vizzes

LinkedIn: https://www.linkedin.com/in/austinmgardner/

Summary: Austin from the Next Level Tableau program recently led a session on how calculations and charts work in his industry, sharing insights from his Tableau use.

• • •

Cecily Santiago — DataFam Rising Star 2026

Location: USA

Tableau Public: https://public.tableau.com/app/profile/cecily.santiago/

LinkedIn: https://www.linkedin.com/in/cecily-santiago/

Summary: Cecily has been an active and valued member of the DataFam community. She does an amazing job of combining her personal passions and interests with Tableau to create thoughtful, engaging dashboards. Beyond her work, she is always supportive and generous in helping others, making a positive impact on the community. I’d love to shout out and recognize Cecily for the amazing work and encouragement she consistently brings to the DataFam.

• • •

Chie Sakaki — DataFam Rising Star 2026

Location: Japan

Tableau Public: https://public.tableau.com/app/profile/chie.sakaki/vizzes

LinkedIn: https://www.linkedin.com/in/chie-sakaki-b060b0309/

Website: https://note.com/chiyeah15

X-Twitter: https://x.com/chiyeah15

Summary: Chie Sakaki is a #DataFamRisingStars 2026 candidate, recognized for her unique impact at the crossroads of data visualization, art, and community engagement. Through the DATA Saber program, she built a solid foundation in helping others grasp data concepts, which she now shares via Tableau Public, social media, and her blog. Her work has earned mentions in DataFam Roundup (November 2025), Community Highlight, and she hosts VizSpace, a community exhibition showcasing Tableau art. By fostering dialogue, creativity, and kindness within the DataFam, Chie broadens how data is experienced and understood, making her a Rising Star who enriches the community beyond traditional analytics.

• • •

Chrissy Scott — DataFam Rising Star 2026

Location: USA

Tableau Public: https://public.tableau.com/app/profile/chrissy.scott/vizzes

LinkedIn: https://www.linkedin.com/in/chrissyscott/

Summary: Chrissy has been sharing her engaging Tableau visualizations on LinkedIn for some time and deserves recognition.

• • •

Cooper Wenhua — DataFam Rising Star 2026

Location: China

LinkedIn: https://www.linkedin.com/in/cooper-wenhua-04534816a/

GitHub: https://github.com/imgwho

Summary: He has investigated using AI to assist in data-to-Tableau dashboard transformations, highlighting how AI can help overcome barriers to visualizing data in Tableau.

• • •

Craig Heard — DataFam Rising Star 2026

Location: USA

Tableau Public: https://public.tableau.com/app/profile/craig.heard/vizzes

LinkedIn: https://www.linkedin.com/in/craig-heard-90a14891/

Website: https://dataheard.com/

Summary: Craig is an impressive data visualization artist with an eye for design.

• • •

Daniel Guerrero — DataFam Rising Star 2026

Location: USA

Tableau Public: https://public.tableau.com/app/profile/jose.daniel.guerrero.roman/vizzes

LinkedIn: https://www.linkedin.com/in/danielguerreromsba

Summary: Daniel has been eager to increase his presence in the community and helped initiate a branch of Comunidatos in Dallas. He is one of my mentees and is highly ambitious about advancing himself further.

• • •

Dom Brady — DataFam Rising Star 2026

Location: UK

Tableau Public: https://public.tableau.com/app/profile/dom.brady/vizzes

LinkedIn: https://www.linkedin.com/in/dom-brady-a93a68279/

Summary: Dom is just beginning his Tableau journey, but he's already created impressive, creative, and technical projects. It's great to see him earn his first Viz of the Day!

• • •

Ebenezer Sam — DataFam Rising Star 2026

Location: Ghana

Tableau Public: https://public.tableau.com/app/profile/ebenezer.sam/

LinkedIn: https://www.linkedin.com/in/ebenezer-sam-a34992159/

Summary: Sam’s visualization ideas are consistently outstanding! He is creative, unique, and highly inspiring. His work stands out for his originality and has a positive influence on how others approach data storytelling.

• • •

Emmanuel Fakayode — DataFam Rising Star 2026

Location: Nigeria

Tableau Public: https://public.tableau.com/app/profile/fakayode.emmanuel/vizzes

LinkedIn: https://www.linkedin.com/in/fakayode/

X-Twitter: https://x.com/de__techie

Summary: Emmanuel has demonstrated high-level expertise in Tableau, they have consistently been creating dashboards that solve business problems. Emmanuel has also spoken at the Lagos TUG, sharing his Tableau journey.

• • •

Fathima Shanavas — DataFam Rising Star 2026

Location: India

Tableau Public: https://public.tableau.com/app/profile/fathima.shanavas/

LinkedIn: https://www.linkedin.com/in/fathima-shanavas-2694681b0/

X-Twitter: https://x.com/Fash098

Summary: Fathima consistently produces clean, polished, and impactful dashboards. Her business-oriented visualizations are particularly inspiring, blending strong design with practical insights and innovative ideas.

• • •

Frederick Soh — DataFam Rising Star 2026

Location: USA

Tableau Public: https://public.tableau.com/app/profile/frederick2489/vizzes

LinkedIn: https://www.linkedin.com/in/fredericksoh/

Summary: Frederick is a #DataFamRisingStars2026 because he shown technical excellence, business impact, and community engagement that define the DataFam. He actively shares knowledge, mentors, supports peers, and contributes frameworks and best practices to elevate the community. His willingness to teach and collaborate reflects inclusion and generosity.

• • •

Gospel Chinedu K — DataFam Rising Star 2026

Location: Nigeria

Tableau Public: https://public.tableau.com/app/profile/gospel.chinedu.nwachukwu/vizzes

LinkedIn: https://www.linkedin.com/in/neduchinex/

X-Twitter: https://x.com/Gospelchinex

Summary: Over the years, Gospel has impacted young data enthusiasts through his amazing breakdowns, explanations, results, blog posts, Tableau Vizzes, and data insights. His work has been shared and reposted by great voices and Tableau visionaries. His objective is to give back to the Tableau and data community, help, collaborate, and inspire data famz. He has shown massive growth, received nominations, had trending Vizzes, earned VOTD, and added value to the community.

• • •

Hazem Elseify — DataFam Rising Star 2026

Location: Egypt

Tableau Public: https://public.tableau.com/app/profile/hazem.elseify/vizzes

LinkedIn: https://www.linkedin.com/in/hazemelseify/

Summary: Hazem is a Certified Tableau Consultant who has turned his extensive freelance experience into a passion for community support. He consistently goes above and beyond to help anyone who reaches out, offering guidance and mentorship that genuinely empowers others to succeed.

A collaboration on the “Ancient Egypt Attractions” visualization stands as a testament to Hazem’s collaborative spirit and technical excellence. This project showcases not only his advanced Tableau skills but also their ability to work seamlessly with others to create meaningful, impactful visualizations that push creative boundaries. Hazem represents the best of what the Tableau community stands for: technical expertise paired with generosity, innovation combined with mentorship, and individual excellence channeled into collective growth.

• • •

Hijaz Khan — DataFam Rising Star 2026

Location: Pakistan

Tableau Public: https://public.tableau.com/app/profile/hijazkhan/vizzes

LinkedIn: https://www.linkedin.com/in/hijazbinarif/

Summary: Hijaz is a young BI Developer from Karachi, Pakistan, with a deep passion for Tableau. He has been steadily honing his expertise by creating and publishing impactful visualizations on Tableau Public. His recent work has gained significant recognition, with many of his dashboards remaining among the trending visualizations on the platform.

• • •

Hironobu Yamada — DataFam Rising Star 2026

Location: Japan

Tableau Public: https://public.tableau.com/app/profile/yamacho/vizzes

LinkedIn: https://www.linkedin.com/in/yamacho/

X-Twitter: https://x.com/yamacho1111

Summary: Hironobu was recognized as the “Most Inspiring Community Leader” at the JTUG Community Star 2025 awards, presented at the JTUG Conference 2025, organized by JTUG (Japan Tableau User Group), the largest Salesforce-recognized Tableau user community in Japan.

• • •

Hiroyoshi Kamegaki — DataFam Rising Star 2026

Location: Japan

Tableau Public: https://public.tableau.com/app/profile/kamegaki.hiroyoshi/vizzes

X-Twitter: https://x.com/ksnjr24

Summary: Hiroyoshi inspires DataFam as an excellent, well-prepared community leader.

• • •

Imad Hamouchi — DataFam Rising Star 2026

Location: France

Tableau Public: https://public.tableau.com/app/profile/hamouchi/vizzes

LinkedIn: https://www.linkedin.com/in/imad-hamouchi/

YouTube: https://www.youtube.com/@bitipsboy650

Summary: Imad is an Analytics Engineer and BI & Dataviz Consultant (Tableau, Power BI, DBT, Databricks) currently at ADEO. He created his own YouTube channel to give back after learning Tableau through community content, focusing on making complex topics accessible and providing hands-on, real-world solutions. He’s also active on LinkedIn and Tableau Public, sharing projects and tips with the DataFam.

• • •

Izumi Kimura — DataFam Rising Star 2026

Location: Japan

Tableau Public: https://public.tableau.com/app/profile/izumi4389

X-Twitter: https://x.com/izumi_0901_tab?s=21&t=p0T2InK9d_NtMUaQn2AFMQ

Summary: Izumi consistently creates outstanding visualizations characterized by a refined aesthetic and powerful emotional resonance. Recently, they have emerged as leaders in promoting the adoption and effective use of Tableau in business processes.

• • •

Jennifer Eneh — DataFam Rising Star 2026

Location: Nigeria

Tableau Public: https://public.tableau.com/app/profile/jennifer.eneh/

LinkedIn: https://www.linkedin.com/in/jennifer-eneh

Website: https://jennifereneh22.wixsite.com/eneh

X-Twitter: https://x.com/ahmakah_twbx

Summary: Jennifer has shown consistent improvement in their work and has consistently released amazing Tableau dashboards, even winning the Viz of the Day (VOTD) multiple times in 2025.

• • •

Jinseong Heo — DataFam Rising Star 2026

Location: Korea

Tableau Public: https://public.tableau.com/app/profile/hote/vizzes

LinkedIn: https://www.linkedin.com/in/hote/

Website: https://h-ote.tistory.com/

Summary: Over the past year, he has shown innovation by expanding Tableau beyond traditional dashboards across manufacturing and finance sectors. He created over 20 production-ready Tableau dashboards for marketing, manufacturing (including OEE and sensor analytics), HR, and CRM, supporting more than 1,000 users in making informed decisions. His team explored API-driven development and Tableau MCP adoption to operationalize analytics and AI-assisted workflows. Additionally, he integrated Python-based machine learning models—such as classification, forecasting, and clustering—directly into Tableau through TabPy, cutting manual analysis time by 40 to 60% and enabling predictive insights within native dashboards. Recently, they participated in Iron Viz, developing dashboards with dynamically changing backgrounds using web page objects, which garnered over 1,000 views and 18 likes on Tableau Public.

• • •

Joel Reed — DataFam Rising Star 2026

Location: UK

Tableau Public: https://public.tableau.com/app/profile/joelreed/

LinkedIn: https://www.linkedin.com/in/joelreeduk/

Website: https://www.joelreed.co.uk/

Summary: Joel has been an incredible presence in the DataFam over the past year. His visualization ideas are consistently creative, thoughtful, and inspiring. They have been an active and committed participant in the Makeover Monday challenge, regularly sharing high-quality work with the community. Several of his visualizations have also been recognized as Viz of the Day, highlighting both his skill and impact.

• • •

John Johansson — DataFam Rising Star 2026

Location: USA

Tableau Public: https://public.tableau.com/app/profile/john.johansson/vizzes

LinkedIn: https://www.linkedin.com/in/johnsjohansson/

Summary: John is a multi #VOTD artist, highly active on Tableau Public, and a data leader within his professional network.

• • •

Jonathan Akpogbo Chris — DataFam Rising Star 2026

Location: Nigeria

Tableau Public: https://public.tableau.com/app/profile/jonathan.akpogbo.chris/vizzes

LinkedIn: https://www.linkedin.com/in/jonathan-akpogbo-chris/

Summary: Jonathan is a Data and Business Intelligence Analyst who got started through an online bootcamp and then self-taught through community content like YouTube videos and blogs. He is active in the Lagos Tableau User Group, publishes vizzes on Tableau Public, and participates in community projects like Makeover Monday and Back to Viz Basics.

• • •

Jordan Bullington-Miller — DataFam Rising Star 2026

Location: USA

Tableau Public: https://public.tableau.com/app/profile/jordan.bullington.miller/vizzes

LinkedIn: https://www.linkedin.com/in/jordancbullington/

Summary: Jordan’s Tableau Public portfolio is full of exemplary work, and she’s active on LinkedIn, where she shares her work and discusses her progress in learning Tableau. In higher education, she contributes to HETUG and assists other members by providing feedback on their work and answering questions in the Slack group. She’s a team player and regularly lifts others up.

• • •

Kapil Dhiman — DataFam Rising Star 2026

Location: India

Tableau Public: https://public.tableau.com/app/profile/kapil.dhiman/vizzes

LinkedIn: https://www.linkedin.com/in/kapildhiman97/

YouTube: https://youtube.com/@allaboutdata100?si=Til881irWJprZ4M6

Summary: Kapil consistently contributes to the Tableau community by creating impactful learning content and supporting data professionals at different stages of their journey. With 75+ Tableau certification topics covered and 4,600+ subscribers, his work has helped many learners successfully clear certifications and build confidence in analytics. He actively shares practical insights, real-world use cases, and interview-focused learning while mentoring freshers and supporting peers across domains such as healthcare, finance, and supply chain. His passion for learning, teaching, and community engagement reflects the true spirit of the DataFam.

• • •

Karrie Cardiff — DataFam Rising Star 2026

Location: USA

Tableau Public: https://public.tableau.com/app/profile/karrie.cardiff/vizzes

LinkedIn: https://www.linkedin.com/in/karriecardiff/

Summary: Karrie is an Enterprise Data & BI Manager at BECU who co-leads the Seattle Tableau User Group and mentors colleagues through an internal data viz center of excellence. A self-taught analyst, she built her skills through Workout Wednesday, Makeover Monday, and the DataFam. She’s driven by what she calls the community’s “secret sauce” — its selfless, helpful nature — and continues publishing on Tableau Public while working toward advanced features like Dynamic Zone Visibility and map layers.

• • •

Kelsey Oehrke — DataFam Rising Star 2026

Location: USA

Tableau Public: https://public.tableau.com/app/profile/kelsey.oehrke/vizzes

LinkedIn: https://www.linkedin.com/in/kelseyoehrke/

Summary: Kelsey consistently shares unique and innovative content on Tableau Public, showcasing strong technical expertise and a distinct creative voice. Her work advances new ideas and reflects a deep curiosity about Tableau's possibilities. With her talent and momentum, Kelsey has great potential as a valuable contributor to the DataFam community, and her ongoing involvement is sure to make a significant impact.

• • •

Kinsey Miller — DataFam Rising Star 2026

Location: USA

Tableau Public: https://public.tableau.com/app/profile/kinsey.n.miller/vizzes

LinkedIn: https://www.linkedin.com/in/kinseynicolemiller/

Summary: Kinsey represents the next generation of Tableau designers through her strong design discipline, thoughtful use of Tableau, and commitment to learning in public. Her Iron Viz submission demonstrated a mature and intentional design approach. Rather than relying on complexity, Kinsey focused on clarity, hierarchy, and narrative, using Tableau’s capabilities with restraint to support insight and audience understanding. Every design choice was purposeful, reflecting a strong grasp of visual best practice and human-centred design. Beyond the submission itself, Kinsey’s contribution through unDUBBED further sets her apart. She openly shared her Iron Viz journey, including feedback, iteration, and imposter syndrome, offering an honest perspective that resonated with the Tableau community. By discussing not just the outcome but the process, she helped normalise learning, critique, and growth for emerging designers. Kinsey combines technical skill with humility, storytelling, and reflective practice. Through both her work and her willingness to share her journey, she is helping shape what great Tableau design looks like for the next generation.

• • •

Kota Yoshihashi — DataFam Rising Star 2026

Location: Japan

Tableau Public: https://public.tableau.com/app/profile/lookupore/vizzes

Website: https://note.com/look_ore

X-Twitter: https://x.com/MilanoWindDoria

Summary: He motivates others by encouraging a playful, creative approach to using Tableau. Additionally, as a mentor for DATA Saber-Bridge, he actively helps grow a community of people who can use data effectively.

• • •

Lara Willson — DataFam Rising Star 2026

Location: USA

Tableau Public: https://public.tableau.com/app/profile/larawillson/vizzes

LinkedIn: https://www.linkedin.com/in/lara-willson/

Summary: Lara’s visualizations are always beautifully crafted! She regularly participates in B2VB, Makeover Monday, and Workout Wednesday challenges, inspiring many in the data community. Additionally, she has earned 3 Viz of the Day (VOTD) awards on Tableau Public.

• • •

Louis Phipps — DataFam Rising Star 2026

Location: UK

Tableau Public: https://public.tableau.com/app/profile/louis.phipps/vizzes

LinkedIn: https://www.linkedin.com/in/louis-phipps-7bb446267/

Summary: Louis shares awesome and inspiring vizzes on Tableau Public, including a Viz of the Day (VOTD) in 2025!

• • •

Madoka Fujita — DataFam Rising Star 2026

Location: Japan

LinkedIn: https://www.linkedin.com/in/madoka-fujita-31b9912b9/

Portfolio: https://techplay.jp/community/tableaupapamama

X-Twitter: https://x.com/mfujita2023

Summary: Makoda leads the DATA Kids (Tableau Parents User Group). She holds events once or twice a year to provide learning opportunities for parents who feel they don’t have enough time to study due to childcare.

• • •

Melissa Anino — DataFam Rising Star 2026

Location: USA

Tableau Public: https://public.tableau.com/app/profile/melissaanino/vizzes

LinkedIn: https://www.linkedin.com/in/melissa-anino/

Summary: Melissa has been proudly sharing her Tableau work on LinkedIn. She's been very engaged with the LinkedIn #DataFam community, actively contributing to conversations and connecting with others.

• • •

Michael Mccusker — DataFam Rising Star 2026

Location: USA

Tableau Public: https://public.tableau.com/app/profile/michael.mccusker/

LinkedIn: https://www.linkedin.com/in/michaelmccusker30/

Summary: A nominator first learned about Michael during a TUG onboarding call and has been following his work ever since. He has made outstanding contributions to TUG planning, often adding creativity, organization, and energy to the community. His visualization concepts are highly inventive, and the Advent Calendar he created was a wonderful project that added value and boosted engagement within the DataFam. Michael is an active and committed community member whose efforts merit recognition.

• • •

Michi — DataFam Rising Star 2026

Location: Japan

Tableau Public: https://public.tableau.com/app/profile/michiko.ohkubo4048/vizzes

Website: https://note.com/michi_tatotsu

X-Twitter: https://x.com/michi_TaToTsu

Summary: In 2025, Michi demonstrated remarkable dedication by completing Workout Wednesday (#WOW2025), deepening her understanding of data and applying advanced skills with Tableau Desktop and Prep. She also secured first place in the Open Data Battle at the JTUG General Meeting with a presentation on Osaka’s flood-control culture, illustrating how data can reveal local history and social context. By consistently learning, openly sharing her progress, and showing gratitude to others, Michi has fostered a positive and motivating learning environment, making her a clear example of a Rising Star in the Japanese DataFam.

• • •

Naruhiro Toya — DataFam Rising Star 2026

Location: Japan

LinkedIn: https://www.linkedin.com/in/%E6%88%90%E6%B4%8B-%E6%88%B8%E8%B0%B7-548b91202/

Tableau Public: https://public.tableau.com/app/profile/naruhiro.toya/vizzes

X-Twitter: https://x.com/nattoyacchi

Summary: Since joining DATA Saber as a Bridge 3rd in 2025, Nattoyacchi has been an active supporter of the community. He shares her learning challenges, promotes key events like the DATA Saber Conference 2025 and the JTUG General Meeting, and contributes significantly to behind-the-scenes work and outreach efforts. By consistently connecting people, events, and communities and creating a welcoming environment for newcomers, he has boosted participation and enthusiasm within the Japanese DataFam, exemplifying a Rising Star.

• • •

Natsumi Goto — DataFam Rising Star 2026

Location: Japan

Tableau Public: https://public.tableau.com/app/profile/natsumi2757/vizzes

X-Twitter: https://x.com/Natsumi_gt_data

Summary: Natsumi still has young children who require significant care, yet she has joined both the MoM and B2VB Complete Clubs. In 2025, they also participated in “Viz Games,” the Japanese version of Iron Viz, and she created a data bingo in only 20 minutes. Such dedication is something the community should always remember.

• • •

Nhung Le — DataFam Rising Star 2026

Location: Germany

Tableau Public: https://public.tableau.com/app/profile/le.nhung/vizzes

LinkedIn: https://www.linkedin.com/in/lecamnhung/

Summary: Nhung presented at TC25 and participated in the DataFam Slam in London (DataFam) and Frankfurt (Data+AI Summit) this year. She was featured in the DataFam highlight and received 2 VOTDs. Additionally, she presented at various user groups, including Analytics TUG and TUG Hamburg.

• • •

Nobuyuki (Nobu) Kimura — DataFam Rising Star 2026

Location: USA

Tableau Public: https://public.tableau.com/app/profile/nobu.kimura/vizzes

LinkedIn: https://www.linkedin.com/in/nobuyuki-kimura/

Nobu has become an increasingly active member of the #DataFam LinkedIn community. In 2025, he completed every single #MakeoverMonday visualization; a true show of consistency and craft. His portfolio is diverse, bold, and eye-catching, earning him two global Viz of the Day (VOTD) recognitions.

• • •

Nozomi Kokubo — DataFam Rising Star 2026

Location: Japan

Tableau Public: https://public.tableau.com/app/profile/nozopipi/vizzes

X-Twitter: https://x.com/nozopipi

Summary: Nozomi publishes engaging visualizations on Tableau Public and speaks at events, all while holding firm to their convictions.

• • •

Olawepo Abdulwarith Ayoola — DataFam Rising Star 2026

Location: Nigeria

Tableau Public: https://public.tableau.com/app/profile/olawepo.abdulwarith.ayoola/vizzes

LinkedIn: https://www.linkedin.com/in/abdulwarith-olawepo-21ba431a8/

Summary: Olawepo has created multiple impressive dashboards and is an active member of the Tableau community. He serves as a moderator for the Lagos TUG. His Udemy dashboard quickly gained popularity after its launch. Moreover, he shares a weekly VOTD roundup on LinkedIn.

• • •

Ollie Ross Russell — DataFam Rising Star 2026

Location: UK

Tableau Public: https://public.tableau.com/app/profile/ollie.ross.russell/vizzes

LinkedIn: https://www.linkedin.com/in/ollie-ross-russell-688314324/

Summary: Ollie’s standout contributions include producing fantastic sports visualizations with innovative design.

• • •

Oluwadunsin “Dunsin” Agbolabori — DataFam Rising Star 2026

Location: USA

Tableau Public: https://public.tableau.com/app/profile/oluwadunsin.agbolabori/vizzes

LinkedIn: https://www.linkedin.com/in/dunsinagb/

Summary: Dunsin is a Data Analyst based in Atlanta who co-leads the Atlanta TUG and volunteers with the World Disaster Centre, using data visualization to support humanitarian causes. She publishes on Tableau Public, maintains a blog, and is committed to using her data skills to drive social impact both locally and globally.

• • •

Oyesina Oyerinde — DataFam Rising Star 2026

Location: Nigeria

Tableau Public: https://public.tableau.com/app/profile/oyesina.oyerinde/vizzes

LinkedIn: https://www.linkedin.com/in/oyesina-oyerinde-anuoluwapo/

X-Twitter: https://x.com/OyerindeOyesina

Summary: Oyesina Oyerinde is a dedicated data supporter in Kaduna, actively contributing to the Tableau community. He writes insightful blogs, shares support, and volunteers at weekly Tableau Saturdays at CoLab Kaduna. He organized the Kaduna watch party for the Tableau + AI talk and engages in Lagos Tableau events, strengthening the ecosystem. His long-term service in Kaduna has often gone unrecognized, but his steady, meaningful impact makes this spotlight well deserved.

• • •

Pete Sime — DataFam Rising Star 2026

Location: UK

Tableau Public: https://public.tableau.com/app/profile/petesime/

LinkedIn: https://www.linkedin.com/in/pete-sime/

Summary: Pete has been a dedicated and enthusiastic learner, consistently sharing valuable insights and skills with the community. He frequently publishes impressive Tableau projects and actively participates in DataFam discussions. His Tableau portfolio is outstanding and continually provides new learning opportunities. The nominator aims to acknowledge his curiosity, generosity in sharing knowledge, and his ongoing contributions to the DataFam.

• • •

Peter “Pablo” Okwukogu — DataFam Rising Star 2026

Location: Nigeria

Tableau Public: https://public.tableau.com/app/profile/peter.okwukogu/vizzes

LinkedIn: https://www.linkedin.com/in/ipablo26/

Website: https://www.instagram.com/p/C2zTRkfsblr/

X-Twitter: https://x.com/iPablo26

Summary: Peter Okwukogu, known as Pablo, has a significant passion for Tableau. He has hosted Tableau Saturdays weekly for 2 years, teaching everything from basics to complex visualizations for free. He enjoys teaching, instilling values such as critical thinking, creativity, and a love of research. His students see him as community-oriented, zealous, and talented. Pablo’s consistent dedication over the years makes him a strong nominee.

• • •

Priscilla Siow — DataFam Rising Star 2026

Location: USA

Tableau Public: https://public.tableau.com/app/profile/priscilla.siow4471/

LinkedIn: https://www.linkedin.com/in/priscilla-siow/

Website: https://www.priscillasiow.com/#

X-Twitter: https://x.com/ehnehyah

Summary: Priscilla’s dashboards are truly captivating, with a strong emphasis on creativity and excellent UI design. Her work consistently stands out for its visual polish and thoughtful execution. In particular, her Iron Viz submissions have been especially impressive, creative, engaging, and highly inspiring.

• • •

Rajan Das — DataFam Rising Star 2026

Location: India

Tableau Public: https://public.tableau.com/app/profile/rajan.das/vizzes

LinkedIn: https://www.linkedin.com/in/rajandas978/

Summary: Rajan actively supports the Tableau community by sharing knowledge, guidance, and best practices to facilitate learning. He possesses extensive expertise in Tableau, covering advanced visual design, performance tuning, and data modeling, along with a solid grasp of business needs. His strength lies in connecting data insights with business decisions, making dashboards both visually appealing and meaningful.

Raisa Hannus — DataFam Rising Star 2026

Location: USA

Tableau Public: https://public.tableau.com/app/profile/raisa.hannus/vizzes

LinkedIn: https://www.linkedin.com/in/raisahannus/

Summary: Raisa is a Freelance Business Analyst who learned Tableau through an online course, where she first created charts and joined the Back to Viz Basics community project. Community projects are her favorite part of DataFam, but she is also active in Tableau User Groups and Tableau Public. She believes visualizing data makes it more accessible and is driven by that mission. A dedicated Workout Wednesday participant, known for her sharp attention to detail and creative problem-solving, she has received praise from DataFam members. Her Tableau Public portfolio features innovative chart work, including a notable Dorling map of world population, which was featured as a guest blog post on CJ Mayes' site. She was also a #DataFamRisingStars honoree in 2025.

• • •

Ravi Kumar N — DataFam Rising Star 2026

Location: UAE

Tableau Public: https://public.tableau.com/app/profile/ravi.kumar.n/vizzes

LinkedIn: https://www.linkedin.com/in/nravik/

X-Twitter: https://x.com/ravi071011

Summary: Ravi might not always be the loudest voice, but he consistently supports the DataFam through action. He enjoys mentoring others, providing thoughtful feedback, and helping community members prepare for Tableau certifications so they can grow confidently. He has been doing this on Twitter for many years. Currently, he volunteers as a co-lead of the Bangalore Tableau User Group and has hosted multiple virtual and in-person TUG events in Bangalore, creating spaces for learning, connection, and shared growth. He also shares Tableau tips weekly on LinkedIn and has published over 82 visualizations on Tableau Public as part of his learning and contribution journey. He was even part of Datafam Rising Stars in 2023. Additionally, he started his YouTube channel and posts content on Tableau concepts to help others on a similar path to mastering Tableau. He actively amplifies the work and successes of new Tableau users and stays engaged in DataFam conversations on Slack and Twitter.

• • •

Rosh Khan — DataFam Rising Star 2026

Location: UK

Tableau Public: https://public.tableau.com/app/profile/roshaan.khan/vizzes

LinkedIn: https://www.linkedin.com/in/rosh-k-/

Summary: Rosh shares educational vizzes, writes impactful blogs on Tableau Next, and is a bright spark in the community!

• • •

Rubén Martinez — DataFam Rising Star 2026

Location: Spain

Tableau Public: https://public.tableau.com/app/profile/rub.nm/vizzes

LinkedIn: https://www.linkedin.com/in/rub%C3%A9n-mart%C3%ADnez-5bb62320b/

Summary: Ruben made a huge impact in 2025 with his Mastering Tableau series on Tableau Public. Every few weeks, he releases a new dashboard focused on a key Tableau element or concept, presenting it in a simple, elegant, and clear way. His work has gained popularity, been featured in Datafam roundups, and earned a VOTD. He isn’t the loudest voice in the community, but his contributions speak loudly. Personally, he mentored me for Tableau certifications, and we are very lucky to have him as part of Datafam.

• • •

Satoko Fujiwara — DataFam Rising Star 2026

Location: Japan

Tableau Public: https://public.tableau.com/app/profile/satoko.fujiwara/vizzes

X-Twitter: https://x.com/pcpn_twt

Summary: Satoko was nominated as a #DataFamRisingStars 2026 candidate due to her exceptional dedication to mentoring and fostering the next generation within the Japanese DataFam. In 2025, she led several successful training programs as a mentor in DATA Saber, Bridge 3rd, and internal initiatives, helping 12 apprentices achieve certification through consistent, practical support, timely study sessions, and encouragement tailored to their learning paces. By highlighting her apprentices’ accomplishments while pursuing her own growth, Satoko exemplifies a “grow together” philosophy and has established a solid, sustainable foundation for future DataFam members, earning her the status of a true Rising Star.

• • •

Satyabrata Majhi — DataFam Rising Star 2026

Location: India

Tableau Public: https://public.tableau.com/app/profile/sattyabrat/vizzes

LinkedIn: https://www.linkedin.com/in/sattyabrat/

X-Twitter: https://x.com/satya_majhi

Summary: As the leader of the Tableau User Group (TUG) Bhubaneswar, Satya not only hosted meetings but also built a local ecosystem through community events and supporting technical queries, acting as a bridge between regional talent and the global #DataFam. Over the past year, he advanced their commitment via independent mentorship, teaching aspiring analysts Tableau visualization and emphasizing the importance of effective data storytelling to help newcomers find their voice.

• • •

Seiji Sunagawa — DataFam Rising Star 2026

Location: Japan

Tableau Public: https://public.tableau.com/app/profile/seiji.sunagawa/vizzes

Website: https://note.com/suna_tab

X-Twitter: https://x.com/Seiji_Suna

Summary: Seiji significantly impacted the Japanese Tableau community through community building, learning support, and exemplary leadership. In 2025, he was a core organizer of Tableau Umauma Kai, a monthly event that lowered barriers to joining the community. The group held 12 events that year (21 total), with 171 participants, expanding beyond Tokyo to Kanazawa and Osaka. Seiji also focused on personal growth, becoming a finalist in VizGames 2025 and Viz Tsukurima Show! 2025. He created the Tableau Desktop Chart Creation 100 Drills, a popular resource that has aided many Japanese DataFam members. His efforts have inspired many to join and learn in Tableau. His influence grows, positioning him as a future global DataFam leader.

• • •

Seun Adeyemo — DataFam Rising Star 2026

Location: Nigeria

Tableau Public: https://public.tableau.com/app/profile/bigxpt/

LinkedIn: https://www.linkedin.com/in/bigxpt/

X-Twitter: https://x.com/bigxpt

Summary: Seun regularly contributes impressive and highly inventive visualization ideas to the community, many of which reveal Tableau capabilities that many Tableau practitioners are not aware of. People were particularly captivated by his liquid-glass-themed dashboard, praised for its originality and execution. His work is genuinely distinctive, inspiring, and exemplifies the push to the limits of data visualization.

• • •

Sherzodbek Ibragimov — DataFam Rising Star 2026

Location: USA

Tableau Public: https://public.tableau.com/app/profile/sherzodbek.ibragimov/vizzes

LinkedIn: https://www.linkedin.com/in/sherzodibragimov/

Website: https://topmate.io/sherzodbek_ibragimov

Summary: Sherzodbek participated in community challenges such as #MakeoverMonday, #B2VB, and #30DayChartChallenge, creating over 40 visualizations. He frequently shares feedback with other Tableau users through LinkedIn posts and messages, helping them improve their skills. Additionally, he re-shares valuable content from creators and discusses its relevance to the #datafam community. He offers free visualization reviews and analytical sessions on Topmate, earning recognition as one of the top 50 Data Analytics mentors. Sherzodbek was also nominated for #Vizzie 2025 in categories including Notable Newbie and Best Designer, highlighting his contributions. He actively participates in the #Datafam community by reposting, commenting on, and liking posts about Tableau features, updates, and events, which helps him stay connected and share useful information.

Sri Kiran Surapaneni — DataFam Rising Star 2026

Location: Canada

Tableau Public: https://public.tableau.com/app/profile/srikiran.surapaneni/vizzes

LinkedIn: https://www.linkedin.com/in/sri-kiran-surapaneni/

Summary: Sri is very active on LinkedIn and has made significant progress on Tableau Public. He is active in the Toronto Tableau User Group and serves as a moderator for the RBC Bank internal Tableau User Group.

• • •

Stephanie N Anyama — DataFam Rising Star 2026

Location: Nigeria

Tableau Public: https://public.tableau.com/app/profile/neto.anyama/vizzes

LinkedIn: https://www.linkedin.com/in/stephanieanyama/

X-Twitter: https://x.com/Naeto__

Summary: Stephanie's Tableau Public profile showcases a strong collection of visualizations. Her recent impressive work on the business dashboard earned her a Viz of the Day!

• • •

Sumit Rathore — DataFam Rising Star 2026

Location: USA

Tableau Public: https://public.tableau.com/app/profile/sumit.rathore2985/

LinkedIn: https://www.linkedin.com/in/rathoresumit/

Summary: Sumit, as co-lead of the Philadelphia Tableau User Group, has been an outstanding contributor both to TUG and the DataFam community. He consistently creates impressive dashboards and generously shares tips, tricks, and insights with the community. A nominator said they learned a lot from him and that he has been their go-to person for any Tableau-related questions.

• • •

Taisuke Kiya — DataFam Rising Star 2026

Location: Japan

Tableau Public: https://public.tableau.com/app/profile/taisuke.kiya8884/vizzes

Website: https://note.com/kyast5510

X-Twitter: https://x.com/kyaaaatsk

Summary: Shortly after starting Tableau, he participated in a movie data dashboard contest hosted by Salesforce Japan and received the Newcomer Award. Since then, despite being a Tableau beginner, he has actively created and shared dashboards, including ones inspired by his personal interest in keyboards, establishing himself as a promising newcomer to watch.

• • •

Takeshi Hiratsuka — DataFam Rising Star 2026

Location: Japan

Website: https://note.com/marreta27

Blog: https://medium.com/@marreta27

X-Twitter: https://x.com/marreta27_jp

Summary: Takeshi is highly knowledgeable about Tableau’s AI and actively contributes to the Japanese Tableau community based on that expertise.

• • •

Takeshi Hizukuri — DataFam Rising Star 2026

Location: Japan

Tableau Public: https://public.tableau.com/app/profile/takeshi.hizukuri/vizzes

Website: https://note.com/hizukuri3

X-Twitter: https://x.com/hizukuri3

Summary: Hizukuri showed remarkable growth and impact in the Japanese Tableau community in 2025. He completed MakeoverMonday, published diverse high-quality visualizations, and placed second at the Viz Tsukurima Show! during the JTUG General Meeting. By sharing his learning, embracing new techniques, and engaging with communities, Hizukuri has inspired others and exemplifies a Rising Star in the Japanese DataFam.

• • •

Takumi Saito — DataFam Rising Star 2026

Location: Japan

Tableau Public: https://public.tableau.com/app/profile/.23205503/vizzes

X-Twitter: https://x.com/_kakunisan

Summary: Takumi’s contributions were not highly visible but meaningful. He quietly dedicated himself to completing the “Sample Super Factory” project within the Tableau Manufacturing User Group. Since community activities are in addition to regular work, members often encounter scheduling difficulties. He personally managed all coordination efforts. Without his dedication, finishing the Sample Super Factory would have been impossible.

• • •

Tanya Fischer — DataFam Rising Star 2026

Location: Germany

Tableau Public: https://public.tableau.com/app/profile/tanya.fischer/vizzes

LinkedIn: https://www.linkedin.com/in/tanya-fischer/

Summary: Tanya is a BI Consultant at The Information Lab Deutschland and co-organizer of the Hamburg Tableau User Group. She’s known for her German-language Tableau blog posts and for making dashboard best practices accessible to a broader European audience, covering container structures, dynamic features, and more. She also presents at TUG events and hosts structured dashboarding sessions.

• • •

Tesshin Takagi — DataFam Rising Star 2026

Location: Japan

Tableau Public: https://public.tableau.com/app/profile/tesshin/vizzes

X-Twitter: https://x.com/tesshin_tableau

Summary: He contributes significantly to DATA Saber’s community outreach efforts to further spread Tableau community activities in Japan.

• • •

Tokiko Mizuguchi — DataFam Rising Star 2026

Location: Japan

Tableau Public: https://public.tableau.com/app/profile/mizuguchi.tokiko7544/vizzes

X-Twitter: https://x.com/toko_tabo

Summary: Besides developing visualizations, they organized the “Open Data Battle" JTUG event, giving users a platform for active participation. They also assisted Tableau users by engaging with participants and providing guidance on visualization creation.

• • •

Toludoyin Shopein — DataFam Rising Star 2026

Location: UK

Tableau Public: https://public.tableau.com/app/profile/t.shopein/vizzes

LinkedIn: https://www.linkedin.com/in/toludoyin-shopein-433672173/

Summary: Toludoyin is an Analytics Engineer at Rise Vest Technologies who publishes Tableau Public vizzes focused on social and economic topics, using data storytelling to raise awareness on issues that matter. She participates in community projects and is an active DataFam presence, including at DataFam Europe events.

• • •

Tsutomu Ikeda — DataFam Rising Star 2026

Location: Japan

Tableau Public: https://public.tableau.com/app/profile/ikeda.tsutomu/vizzes

X-Twitter: https://x.com/TIkeda93820944

Summary: Tsutomu frequently publishes truly outstanding visualizations and shares them, and is active with the DATA Saber community on X-Twitter.

• • •

Unais M K — DataFam Rising Star 2026

Location: India

Tableau Public: https://public.tableau.com/app/profile/mkunaismkd/vizzes

LinkedIn: https://www.linkedin.com/in/mkunaismkd/

Blog: @mkunaismkd

Summary: The nominator highlights Unais MK as a standout for #DataFamRisingStars2026, praising his dedication to innovative data storytelling. As a data visualization enthusiast, he doesn’t just build dashboards; he also explores and applies visualization best practices that challenge norms. His key contributions include developing experimental Tableau ideas and sharing them via detailed blog posts to demystify technical builds, actively educating newcomers, and demonstrating leadership within the Tableau community. Through LinkedIn and Tableau Public, he consistently shares high-quality content that inspires others. Unais exemplifies the “Rising Star” spirit by transforming his learning into a resource, fostering a more collaborative, visually literate data community.

• • •

Vlad Horbachenko — DataFam Rising Star 2026

Location: Ukraine

Tableau Public: https://public.tableau.com/app/profile/vlad.horbachenko/vizzes

LinkedIn: https://www.linkedin.com/in/vladyslav-horbachenko-30a89a234/

Summary: Vlad received nominations because his work consistently showcases exceptional storytelling and creativity. The nominiators have followed his projects for a while, and he always exceeds expectations by offering fresh perspectives, thoughtful narratives, and a level of detail that transforms data visualization from merely informative to genuinely impactful.

• • •

Yo Morikawa — DataFam Rising Star 2026

Location: Japan

Tableau Public: https://public.tableau.com/app/profile/yochan/vizzes

X-Twitter: https://x.com/elc_small

Summary: Yo has consistently made positive contributions to the Japanese Tableau community. After finishing DATA Saber and Bridge 2nd in early 2024, he remains actively involved in various online and offline events, including JTUG, Tableau Umauma Kai, regional user groups, and casual Viz showcases. He also creates and shares visualizations that support community initiatives. Through his ongoing presence, encouragement of others, and genuine enthusiasm for connecting people, Yo helps maintain a welcoming and lively community atmosphere, earning recognition as a true Rising Star within the Japanese DataFam.

• • •

Yusuke Nishikawa (insight Nishikawa) — DataFam Rising Star 2026

Location: Japan

Tableau Public: https://public.tableau.com/app/profile/.26773494/vizzes

X-Twitter: https://x.com/Tableau_Nissy

Summary: Through his explanatory content on “what dashboards are” and the workshops he organizes, he effectively communicates the true essence of dashboard creation. He consistently emphasizes the importance of designing and building dashboards with clear objectives, rather than treating them merely as tools, which is highly commendable.

Thank You, Nominators

#DataFamRisingStars wouldn’t be what it is without our nominators. They really capture what makes our #DataFam special; always lifting each other up and paying it forward. If you see a linked name, please follow them on LinkedIn. Some folks wanted to stay behind the scenes, so you won’t see everyone here. Here are 55 nominators and a little more than a handful that wished to remain anonymous.

Nominators:

Ai Sugiyama, Ajay Vishnu Addala, Angela Henderson, Anindita Mitra, Ankur Napa, Anna Clara Gatti, Anubha Gupta, Cooper Wenhua, Cristian Saavedra, Dan Wade, Deborah Simmonds, Devanshu Tayal, Emmanuel Fakayode, Fiona Crocker, Frederick Soh, Frederik Egervari, Ginny Moench, Giulio D’Errico, Gokul, Gospel Chinedu K, Hideaki Yamamoto, Hiroaki Nakajima, Hiroki Uetake, Hironobu Yamada, Hue Vuong, Iris Sun, Jinseong Heo, Joy Victor, Jude Raji, Kapil Dhiman, Kevin Wee, Kyeongseok Min, Madoka Fujita, Martin Louis M, Michael Mccusker, Olawepo Abdulwarith Ayoola, Pooja, Rafael Guevara, Ravi Kumar N, Sam Cohn, Sarah Pallett, Satyabrata Majhi, Serena Purslow, Sho Suzaki, Shreya Arya, Shweta K, Sri Kiran Surapaneni, Tanya, techiepooja, Tesshin Takagi, Unais Mk, Waqar Ahmed Shaikh, Will Perkins, and Zyad Wael

Prior Cohorts

• DataFamRisingStars: 2025
• TableauNext: 2024
• TableauNext: 2023
• TableauNext: 2022

Note: The link to the GPT at the bottom of this article

For years, I kept stumbling over something that everyone else seemed to just get: the vague language we toss around at work.

Being autistic, picking up on indirect signals, reading between the lines, and keeping up with ever-changing workplace lingo takes a lot out of me. I can handle the technical stuff. I can deliver great work. But when it comes to decoding phrases like these? That’s where I hit a wall:

• “Be more strategic.”
• “Show more ownership.”
• “This isn’t landing.”
• “Let’s align on priorities.”

But what do any of those actually look like in real life?

For the longest time, I figured this was just my problem; a gap in reading social cues or picking up on what wasn’t said. I’d walk out of performance reviews scratching my head, still not sure what I was supposed to change. The feedback was so vague, it felt impossible to do anything with it.

Then I realized something that changed everything.

The 10x Realization: This Isn’t Just a Neurodivergent Thing

Corporate language is often intentionally fuzzy.

Ambiguity gives leaders an easy out. If feedback is vague, no one can really call them out for unclear expectations. Jargon fills the gaps where clarity should be… not because it helps, but because it sounds official while actually saying less.

All those conflicting, fuzzy terms? They just hide what’s really being said. And honestly, they confuse way more people than anyone wants to admit.

I started asking colleagues (neurotypical ones) whether they understood the jargon. Most of them couldn’t explain in a way that’s easily understood. They’d nod, say “got it,” and then guess. Hope they were doing the right thing. Maybe ask a trusted peer quietly, away from their manager.

The problem isn’t that some people can’t crack the code. The problem is that workplace communication is set up to be decoded, not easily understood.

Then Add Global Context, And It Gets Even Trickier

Add in the global workplace, and things get even messier.

The same phrase can mean something totally different depending on where you are:

• In the UK, “quite good” actually means “acceptable to solid, not exceptional”
• In Germany, direct professional feedback is expected and valued. What Americans call “blunt” is just normal
• In Japan, “no” is often said without the word. Silence or “I’ll consider it” can signal polite disagreement
• In France, heated debate isn’t conflict. It’s intellectual engagement
• In Australia, tall poppy syndrome means standing out too much will get you cut down

My community and professional work have allowed me to collaborate with people around the world. I’ve seen messages get totally lost in translation because no one thought about cultural context. What sounds supportive in one country can come off as passive-aggressive in another. I also researched, curated, and pressure-tested these items to build into the tool’s knowledge base.

And none of this I would consider intuitive or widely documented.

So I Built Something I Wish I’d Had Many Years Ago

I decided to build an AI-powered Workplace Communication Decoder, not just for me, but for everyone who’s ever stared at a vague email or sat through a performance review wondering, what are they really trying to say?

To make this more than just another generic AI, I packed it with real-world knowledge based on:

• Carefully reviewed communication patterns across industries, countries, and company sizes
• Tested principles from navigating workplace challenges I’ve faced over two decades
• Deep research into how implicit expectations vary by role level, cultural context, and organizational dynamics
• Frameworks for empathy, intersectionality, and power imbalances that generic AI simply doesn’t have

What the Unwritten Rules Decoder Actually Does

It breaks down what was actually said versus what’s usually implied.

When a leader says “be more strategic,” the decoder explains:

Surface meaning: They want you to be more strategic (not helpful).

Common implicit expectations: Anticipate problems before they happen, connect your work to business outcomes, reduce need for oversight, think beyond your immediate tasks

It doesn’t pretend to know what the instruction means. Instead, it shows you what that phrase usually signals in most workplaces, so you can decide what to do next.

It flags common risk signals

The decoder recognizes patterns like performance concerns (vague feedback that could affect reviews or advancement), restructuring indicators (hiring freezes, consultant arrivals, “efficiency reviews”), and escalation signals (HR suddenly involved, formal documentation, tone shifts).

It helps you spot what might be coming, so you’re not caught off guard.

It identifies what’s missing from feedback

Often, the problem isn’t what’s said. It’s what’s not said: no timeline for when they expect change, no specific examples of what “strategic” looks like, no metrics for how success will be measured, no mention of support or resources to help you improve.

The decoder highlights what’s missing, so you know exactly what to ask next.

It adjusts interpretation by industry, role level, and geography

“Ownership” means different things in tech startups (taking initiative without asking permission), finance (managing risk and ensuring compliance), healthcare (patient outcomes and safety protocols), Germany (clear accountability within a defined scope), or Japan (ensuring group harmony while delivering results).

The decoder takes these differences into account instead of just giving cookie-cutter advice.

It offers response options, only if you want them

Some people just want to understand the pattern. Others want concrete next steps.

The decoder always asks before providing solutions.

If you want options, it gives you 3 to 5 different approaches with trade-offs: ask for clarification (gets you information but may signal confusion), propose your interpretation (shows initiative but risks being wrong), document understanding in writing (creates a paper trail but feels formal), or demonstrate through action (shows ownership without discussion).

You pick what works for you. No prescriptions. No “just do this.”

What It Doesn’t Do (And Why That Matters)

The decoder does not mind-read what your department is thinking, diagnose personalities or psychological states, tell you what to do, provide therapy or legal advice, or make assumptions about intent or emotion.

It interprets patterns, not people.

That line matters. The goal is to help you navigate the workplace, not guess what’s going on in someone’s head.

Who This Is Really For

Yes, it’s for autistic professionals

The decoder does what I desperately needed years ago: translates implicit expectations into explicit patterns. It removes the exhausting guesswork and gives you a framework to work from.

But it’s also for:

• Anyone facing vague feedback they can’t act on
• International professionals navigating cultural communication differences
• People sensing layoffs or reorgs who need to read the signals
• Marginalized professionals facing coded language about “culture fit”
• Everyone who’s ever left a meeting thinking “wait, what did they actually mean?”

These Rules Are Frequently Not Written Down

That’s the real problem right there.

Every workplace has unwritten rules. Every industry has jargon that insiders understand and outsiders don’t. Every culture has implicit communication norms.

People who already know the rules have an advantage. They get promoted faster. They navigate politics better. They avoid landmines the rest of us step on.

This decoder levels the playing field.

It won’t turn you into an insider overnight. But it lets you see what insiders see. It cracks the code so you can make smarter choices about your career, your wellbeing, and your next step.

Why I’m Sharing This

I spent many years struggling with something that didn’t need to be this hard. I watched talented people (autistic and not) get passed over for promotions because they didn’t understand unspoken expectations. I saw international colleagues misinterpret feedback because the cultural context wasn’t considered.

If I can help even a fraction of those people navigate this more easily, it’s worth sharing.

The decoder isn’t perfect. No AI is. But it’s built on the kind of frameworks I wish someone had handed me on day one: here’s what that phrase usually means, here’s what could be at stake, here’s what you might want to try. The choice is yours.

Try It Yourself

The Unwritten Rules Decoder is available now as a custom GPT.

It won’t make workplace communication less frustrating. It won’t fix unclear direction or ambiguous corporate cultures.

But it will give you a fighting chance to understand what’s really being said so you can respond on your terms, not theirs.

Because everyone deserves to know the rules of the game they’re playing.

Even the unwritten ones.

▶️Access the Unwritten Rules Decoder

Adam Mico

LinkedIn | Product Decision Partner | Tableau Virtuoso GPT | VizCritique Pro GPT | Data Mockstar GPT | Writing Tone Clone GPT | Genetic Genealogy Researcher Partner Pro GPT | DataQuest: Choose Your Own Data Adventure GPT | TableauQuest: Real-World Tableau Decisions | GPT Architect Pro 5.2 | Unwritten Rules Decoder GPT

Note: My book, Tableau Desktop Specialist Certification (no AI assistance), is available for purchase here.

Adam Mico’s Introduction

On the surface, cwprep is a Python library that generates Tableau Prep flows programmatically, which is already valuable for automation and repeatability. But the bigger shift is what happens when prep logic becomes code instead of clicks: flows become inspectable and versionable rather than locked Tableau Prep files, lineage and business intent are easier to understand, and analytics work is less siloed on individual laptops. That creates a path toward stronger governance, better cataloging, and eventually AI that can reason about data preparation, not just produce fragments of syntax.

I love Cooper’s passion for solving real workflow pain points, his humility in sharing the journey, and his innovative mindset throughout the project. Even more, that he uses his unique skillset to pay-it-forward.

Hearing that my GenAI work inspired him (and seeing what he did with that spark) was a real reminder of why I build in public. You never really know who’s going to pick up on your work or how they’ll run with it.

Notes: He also used GenAI to help translate this post into English, with a few light edits from me, which feels fitting for a project built on thoughtful human–AI collaboration. Connect with Cooper on LinkedIn! A direct link to the tool is at the bottom of this article.

First of all, I’d like to thank Adam for inviting me to write this article and introduce the cwprep project; I’m truly delighted. I also hope more people will get to use this project to boost their productivity. In short, this project lets us write Tableau Prep data flows using Python code. Since we can build data flows in code, we can use AI to understand business requirements and automatically generate them.

Why I Built This Project

Tableau Prep is the data preparation (ETL) tool within the Tableau ecosystem. It provides a visual, drag-and-drop interface for building workflows for data cleaning, joining, aggregation, and more. While the GUI is intuitive, there are several clear pain points in real-world work:

• No version control: Data flows are saved as binary .tfl files, making it impossible to track changes with Git
• No batch generation: If you need 10 similar flows, you have to build each one manually by dragging and dropping
• No AI collaboration: AI can understand business requirements and help write code, but it can’t drag and drop a GUI for you
• High recall cost: I had built many data flows on our server, and after a while, I’d forget the business logic behind each one

cwprep was born to solve these problems; transforming data flow construction from GUI operations into code, making version control, batch generation, and AI collaboration possible.

My Journey Collaborating with AI

I started using AI extensively at work in 2024. Initially, I was hesitant to rely on AI for my work, but I later realized it’s an inevitable trend. What should truly be embarrassing is blindly trusting AI results without verification. My experience has shown that human-AI collaboration can consistently deliver quality work. Your boss doesn’t care whether you use AI — they only care about results. The prototype for this project began in June 2025, driven by my own needs: I had built many data flows on the server, and both my AI assistant and I needed a quick, convenient way to understand and recall the business and code logic behind them. Inspired by the fact that Tableau workbook files (TWB) are essentially XML, I tried renaming a TFL file to .zip and extracting it, which revealed its true structure. I then used Gemini to design and implement a function that deconstructs TFL files into JSON.

In late January this year, while collaborating with AI on data flows and then manually building them in Tableau Prep, I thought: why not just have AI generate the data flow files directly? To validate this idea, I gave Gemini CLI a simple 2-table join TFL file, renamed it to .zip, extracted it, and handed it over. It could fully understand the structure. I observed that the ZIP contained multiple files, but only 3 files without extensions were actually needed for it to work. I then asked Gemini CLI to try reproducing these 3 files using Python code, auto-packaging them into a ZIP file, and renaming the ZIP file to TFL. Initially, the generated TFL wouldn’t open. I kept having the AI relearn the original working flow, but after repeated attempts, it still wasn’t quite right. That’s when I switched to Antigravity’s Claude 4.5; its generated TFL opened correctly on the first try. I was thrilled and immediately created a GitHub project with version control. With the simplest generation validated, I continued creating more test cases, having Claude reference them and implement features until all tests passed completely. During testing, in addition to automated tests that checked file structure integrity, I would also open the files to verify that the content matched the original logic, and I’d review both the Python code and the TFL file’s JSON data. Only when everything passed would I consider the test successful.

Project File Structure

Here is the complete file structure of the cwprep project:

SDK Feature Overview

How TFL Files Work

A .tfl file is essentially a ZIP archive. When extracted, it contains 3 core files without extensions:

• flow — The complete data flow definition (nodes, connections, SQL queries, join conditions, etc.) — the most critical file
• displaySettings — UI layout information (node coordinates, version numbers, display settings) — controls visualization in Tableau Prep
• maestroMetadata — Metadata (version info, error list, flow entry name) — ensures the file is correctly recognized by Tableau Prep

The SDK’s core job is to correctly construct the content of these 3 JSON files in Python, then package them into a standards-compliant ZIP file (renamed to .tfl) so that Tableau Prep can open and execute them seamlessly.

Technical Challenges During Development

Reverse-engineering the TFL file format wasn’t smooth — I hit quite a few pitfalls. Here are some key technical challenges:

1. File Corruption Issues

The initially generated .tfl files would show “File is corrupt” when opened in Tableau Prep. After repeatedly comparing working files with generated ones, I discovered several hidden requirements:

• The error list field in maestroMetadata must be kept as an empty array [] — you can’t just delete the field, or Tableau Prep won’t parse the file
• maestroMetadata must include complete four-segment version information (major.minor.patch.build)
• Incomplete field sort mappings (fieldSortMappings) in displaySettings would prevent the file from opening — the solution was to leave them empty and let the software auto-refresh fields

2. Tableau Prep Calculation Syntax Differences

Tableau Prep’s calculation syntax differs significantly from SQL, which caused many issues during filter and calculated field development:

If you don’t pay attention to these differences, the generated flows will throw calculation errors in Tableau Prep. I compiled the complete syntax rules into docs/tableau_prep_calculation.md and also included them in the AI Skill instructions to ensure AI-generated expressions don’t produce errors.

3. Value Filter Special Format

add_value_filter() uses Tableau’s internal .v1.FilterOperation format, where string values must be wrapped in single quotes (e.g., ‘Same Day’). This format isn’t documented in official materials — it was entirely reconstructed through reverse engineering.

AI Skill System

The project includes a special design: skills/tfl-generator/SKILL.md. This is a “user manual” written specifically for AI Agents. An AI only needs to read this file to fully understand how to call the SDK to generate data flows.

SKILL.md contains:

• Complete API reference with parameter descriptions
• Code examples for each operation type
• Tableau Prep calculation syntax notes and workarounds
• Best practices for building flows (Input → Clean → Join → Aggregate → Output order)

With this Skill, in AI tools that support the Skill system (such as Gemini CLI), AI can automatically generate complete Python scripts to create data flow files based on a user’s business requirement description — the user doesn’t even need to know Python.

Configuration System

cwprep features a flexible three-tier configuration system, with priority from high to low:

1. Code parameters — Passed directly when calling methods, highest priority
2. Environment variables (.env file) — Store sensitive info like database passwords, never committed to Git
3. YAML config file (config.yaml) — Store non-sensitive info like database addresses and Tableau Server URLs

The benefit of this design is that it separates sensitive from non-sensitive information. For team collaboration, you only need to share config.yaml and .env.example, while each person maintains their own .env file; secure and convenient.

# config.yaml example

database:

host: localhost

port: 3306

dbname: superstore

type: mysql

tableau_server:

url: http://your-server

default_project: Default

It took about 5 days of spare time in total to implement all the code logic, with extensive testing to ensure correctness in MySQL scenarios. I also published it as a Python package so anyone can easily install and use it via pip.

pip install cwprep

I tested and screen-recorded the entire process of pip installing and using this project. Given table schemas and business requirements, AI can correctly implement and generate data flow files with full accuracy.

Quick Start Example

Here’s the simplest usage example, showing how to create a data flow with a two-table join in just a few lines of code:

from cwprep import TFLBuilderu, TFLPackager

# Create builder

builder = TFLBuilder(flow_name=”Order Analysis”)

# Add database connection

conn_id = builder.add_connection(

host=”localhost”,

username=”root”,

dbname=”superstore”

)

# Add input tables

orders = builder.add_input_table(“orders”, “orders”, conn_id)

customers = builder.add_input_table(“customers”, “customers”, conn_id)

# Join two tables

joined = builder.add_join(

name=”Orders + Customers”,

left_id=orders,

right_id=customers,

left_col=”customer_id”,

right_col=”customer_id”,

join_type=”left”

)

# Add output

builder.add_output_server(“Output”, joined, “Order_Analysis_Datasource”)

# Build and save

flow, display, meta = builder.build()

TFLPackager.save_to_folder(“./output”, flow, display, meta)

TFLPackager.pack_zip(“./output”, “./order_analysis.tfl”)

After running, a .tfl file will be generated that can be opened directly in Tableau Prep.

Here’s is a short series of 5 brief GIFs that illustrates some of the key steps of the Python library.

1. Install the library via pip (installs cwprep)

2. Verifying the data structure

3. Review that AI understands the Python library and business requirements

4. AI generating the Python code

5. Final check that AI generates the .tfl file

Here is the prompt for the presentation (edited for English):

Please find a detailed example of the mysql data table structure and data import script in this path, corresponding to mysql localhost:3306 root password is empty https://github.com/imgwho/cwprep/tree/main/examples/demo_data

I installed cwprep with pip. Check the pip File directory (Lib\site-packages) to learn how to use it. The md file is the data structure. The database is named Superstore. The database is MySQL,host is localhost:3306. Username: root. Password: empty. Please understand my business requirements and then generate tfl file.

1、please use this library to help me connect the orders table and the returns table to the left according to order_id, filter the sales > 500, output to the server,

2、Help me generate a regional sales report. I need to connect the order table with the area table and the product table. Statistics are made on the total sales and average discount rates for each product category that each regional manager (Manager Name) is responsible for. Just look at the data for 2024 and finally output it to the server.

3、I want to find orders with serious losses. Please associate the order form and returns form (note: orders without returns must also be retained). Create a calculated field ‘Actual Sales’: if it is a return order, the amount is recorded as 0, otherwise it is left as is. Finally, I filter out those orders that have no returns but have a profit (Profit) less than 0. I want to see which products are losing money.

4、Clean up a VIP client list for me.

Based on orders table and customers table.

Exclude all records where returns have occurred.

Exclude slow orders whose shipping method is ‘Standard Class’.

Total sales by customers.

Only retain those customers whose total spending exceeds 5000 and belong to the ‘Corporate’ type. Output fields: customer name, total amount, customer type.

Future Plans

Looking ahead…

Feature-wise: I plan to implement more of Prep’s original features, enhance the skills functionality, and implement MCP (Model Context Protocol), which will make it even easier for AI to use the SDK. I may also implement parsing of TFL files into more readable documentation and connect to Tableau Server for automated publishing.

Additional Testing: Test with more databases (PostgreSQL, Oracle, SQL Server, etc.), more business requirement scenarios, and more extreme cases such as larger data volumes and concurrent generation processing.

Reflections

Looking back at this project, going from idea to publishing on PyPI in 5 days of spare time would have been unimaginable before. This was made possible by deep human-AI collaboration — I was responsible for defining requirements, designing architecture, and validating results, while AI handled understanding the TFL format, writing code, and debugging compatibility issues.

The whole experience has reinforced my belief:

AI won’t replace people, but people who use AI well will replace those who don’t.

The key isn’t whether you use AI, but whether you can accurately define problems, verify results, and correct AI when it makes mistakes. In this project, AI had issues with the initial TFL file generation (the files generated by the Gemini CLI wouldn’t open), but I didn’t give up. I switched to Claude 4.5 and fed it the correct files to learn from, and it succeeded on the first try. This humans define direction, AI executes details, humans verify results collaboration model is the most efficient way of working I’ve found so far.

I’m also seeking better environments to help me amplify this value. Finally, special thanks to Patrick Theirriault for inviting me to share at the Northeastern New England TUG (YouTube video presentation) last year, which led me into the community and gave me the motivation and confidence to keep sharing.

cwprep is an open-source project (MIT License), hosted on GitHub (a direct link to the tool). Feel free to try it out and contribute, or give feedback.

Connect with me on LinkedIn!

From Tableau Prep Clicks to Code (Without Becoming a Python Expert) was originally published in Bootcamp on Medium, where people are continuing the conversation by highlighting and responding to this story.

AI isn’t just a buzzword anymore. It’s woven into the tools we use, how we work, and the choices we make, no matter the industry.

The real question isn’t whether AI has a place at work. It’s about how we design, manage, and support it so it actually delivers value.

This isn’t limited to one company or role. The same patterns show up again and again in AI, data, and analytics conversations. As more people and organizations jump in, the same challenges keep popping up.

Governance Is Infrastructure, Not Overhead

Governance is the backbone of any successful AI rollout, but it’s often treated like an afterthought. In reality, it’s the foundation that everything else depends on.

AI lives and dies by data and context. If your data is messy, redundant, or unclear, trust falls apart fast. Confident answers built on shaky ground just make things more confusing.

You see this even more when AI sits on top of shared knowledge or semantic models. If definitions, relationships, or ownership aren’t clear, AI turns up the volume on confusion instead insight.

Governance isn’t about slowing AI down. It’s what keeps confusion from outpacing understanding.

Tool Sprawl Undermines Trust

Many organizations rush to launch internal AI tools but skip plans for evaluating them or who will own them long-term.

Experimentation is important. The trouble starts when people can’t tell what’s just a test and what’s actually safe to use for real work.

It’s not unusual to find several versions of the same AI assistant floating around. Some may be for training purposes, some are outdated, and maybe one or two are actually maintained and treated as production-ready products. But to most users, they all look the same.

Usage numbers can make this even messier. Sometimes a tool looks “successful” just because its creator(s) use it a lot or share it with many teammates. By this metric, a rough prototype is more popular than a well-governed solution.

The result is predictable. Trust drops, strong tools get buried, and adoption slows, not because AI lacks value, but because the ecosystem feels chaotic.

Enablement Is the Missing Multiplier

Even with solid technology, many organizations struggle with adoption. The issue is rarely the tools themselves. It’s that people aren’t being set up to succeed.

If leaders don’t highlight what good AI use looks like or leverage it as a growth area, most people just stick to what they know. A handful of enthusiasts might experiment, but real adoption takes more than curiosity.

There’s also real fear out there. For some, AI means nonstop change. For others, it feels like a threat to their job or even who they are. Ignoring that just makes resistance stronger.

Enablement isn’t just about training. It’s about building confidence and trust so people actually feel safe bringing AI into their daily work.

Without enablement, AI spreads confusion faster than it delivers insight.

Overreliance Is a Real Risk

As AI becomes easier to use, more context-aware, and more confident-sounding, there’s a new risk: people stop thinking critically.

AI works best as a partner, not a replacement for judgment. But too often, people just accept the output without really checking it.

This is a big reason why I build tools, when relevant, that force partnership rather than a one-prompt “solutioning.”

The end result? Polished content that’s missing depth, accuracy, or real relevance.

Research on cognitive offloading suggests that heavy reliance on AI for complex reasoning can reduce engagement and retention. While this research is still evolving, it raises a necessary caution (Nataliya Kosmyna, Pattie Maes, et al. (2025), Your Brain on ChatGPT: Accumulation of Cognitive Debt When Using an AI Assistant for Essay Writing Tasks, arXiv).

Moving fast without thinking isn’t real progress.

That’s why good AI systems need people in the loop. Users have to understand enough to challenge what comes out, not just take it at face value.

If AI takes away the need to think, it also takes away the value it’s supposed to bring.

Designing for Engagement, Not Dependency

The best AI tools encourage you to interact. They invite you to refine, ask questions, and stay mentally engaged.

They add just enough friction to slow you down and help you get better results.

As tools get more powerful, patience gets shorter. Confident answers are easy to mistake for correct ones. Take away all the friction, and people stop reviewing altogether.

AI maturity isn’t about getting instant results. It’s about using AI on purpose, in ways that scale without piling up technical or decision debt.

Quality Enforcement Is Non-Negotiable

Productivity alone is not the goal.

If AI outputs are sloppy, repetitive, or made up, any productivity gains are just an illusion.

Organizations need clear quality standards for AI-generated work, just as they do for anything people create (and even more so in some cases). That means having review processes for higher-risk cases and feedback loops to keep improving results.

Skip this step, and you start piling up technical, knowledge, and decision debt (usually faster than you think).

The Cost Argument Misses the Point

One of the most common pushbacks to formal AI governance and enablement is cost.

On paper, these roles might look like overhead. In reality, not having them costs a lot more.

Without clear ownership, teams end up duplicating work, rebuilding the same tools, and wasting time fixing bad outputs. These inefficiencies add up fast.

Governance and enablement cut down on rework, boost reuse, and build confidence. The real wins come from making fewer corrections and better decisions.

The real question isn’t if you can afford these roles; it’s if you can afford the mess that comes from not having them.

Dedicated Roles and Reframed Ones

As AI becomes part of the foundation, organizations need to act on two fronts.

First, they need dedicated owners for AI governance and enablement. These roles are there to support consistency and confidence, not to slow things down. Just to reiterate, these roles can be leveraged as a growth incentive. They can be further enhanced with supported teams, committees, or guilds to extend reach.

Second, existing roles must evolve. Data, BI, subject matter experts, learning partners, and product roles are now at the heart of AI success. The same skills that keep data and platforms trustworthy are what make or break AI adoption at scale.

What AI Maturity Looks Like

AI maturity is not measured by access.
It is measured by confidence, competence, and trust.

Organizations that get this will move past endless pilots and start building real, lasting value. Others will keep chasing tools and hype, never really unlocking what AI can do.

AI is here to stay.

The real work now is building the structures that help people use AI well.

Adam Mico

LinkedIn | Product Decision Partner | Tableau Virtuoso GPT | VizCritique Pro GPT | Data Mockstar GPT | Writing Tone Clone GPT | Genetic Genealogy Researcher Partner Pro GPT | DataQuest: Choose Your Own Data Adventure GPT | TableauQuest: Real-World Tableau Decisions | GPT Architect Pro 5.2

Note: My book, Tableau Desktop Specialist Certification (no AI assistance), is available for purchase here.

What AI in the Workplace Should Be was originally published in Bootcamp on Medium, where people are continuing the conversation by highlighting and responding to this story.

This has been on my mind a lot lately.

In product strategy, you can keep running into the same uncomfortable moment. You have research, context, intuition, and pressure to move, but when you sit down to make the decision, you are staring at a blank page. You know something important needs to be decided, yet it is not obvious where to start.

There is nothing worse than that feeling. Frameworks exist. Tools exist. Templates exist. But none of them actually help you bridge the gap between ambiguity and judgment (especially when you receive vague requirements and objectives from the real world). They give you structure, but not clarity.

Hot Take: Over time, I have noticed the hardest part of product work is not execution or alignment. It is making good decisions when uncertainty, signals, and conflicting priorities surround you.

Most product failures do not come from bad documentation. They come from treating weak evidence as a strong signal, mistaking guesses for facts, or making big, irreversible decisions when we only have a little confidence. Usually, we only realize this after the feedback (or the deafening silence) from stakeholders.

I’ve noticed product teams often fail in several predictable ways: moving too fast in the wrong direction, lacking the cohesion to move decisively, making decisions without clear ownership or evidence, and misalignment with customers and business stakeholders. That gap is what got me thinking about building Product Decision Partner.

This is not just another product management tool. It is a thinking partner. Something that helps you move from a blank page to a clear sense of what decision you are actually making.

Product Decision Partner is designed to sit with you in the messy middle. Bring your half-finished research, your conflicting signals, the pressure from stakeholders, or even just a gut feeling you cannot quite explain. Instead of jumping to answers, it helps you slow down in the right way.

It focuses on a few core behaviors I keep seeing missing in product and strategy work:

• Making the real decision explicit instead of hiding it behind artifacts
• Separating signal from noise in research and inputs
• Forcing confidence to be stated, not implied
• Naming tradeoffs instead of letting them remain implicit

Where Product Decision Partner Shows Up in Practice

I’ve noticed these behaviors pop up in way more places than most of us realize. Some are right in your face. Others are sneaky and easy to miss, but when we overlook them, the cost can be huge.

I like to break the use cases into two buckets.

Core Use Cases

(What most people expect it to help with)

1. Prioritizing competing initiatives
When everything feels like a top priority, Product Decision Partner makes you spell out what really matters and puts the tradeoffs front and center. Suddenly, it’s not just about who has the loudest opinion.

2. Making high-stakes strategic decisions
Big moves like entering a new market or making a major roadmap bet can feel overwhelming. This approach helps you get clear on what has to be true, what you can walk back if needed, and how sure you really are before you pull the trigger.

3. Turning research into decisions
It helps you cut through the noise, whether it’s interviews, metrics, or random stories, and ties the real insights straight to the decisions, not just to another slide deck.

4. Framing decisions for executives
You can use the same clear thinking every time: what you recommend, why, what could go wrong, and how sure you are. No need to reinvent the wheel for every exec meeting.

5. Learning from past decisions
When you track your predictions and check back on how things turned out, you start to see where your gut is on point (and where it needs work).

Surprising Use Cases

(Where it quietly creates the most leverage)

6. Catching bias before commitment
Before you get too attached to an idea, it helps you spot the hidden assumptions, sunk-cost traps, and wishful thinking that are easy to miss when you’re in the thick of it.

7. De-escalating team disagreements
It helps you see that most arguments are really about different assumptions, comfort with risk, or how confident people feel. A lot of the time, the fight isn’t even about the actual decision.

8. Deciding what not to decide yet
Sometimes, the smartest move is to hold off. Product Decision Partner helps you spot when the evidence just isn’t there yet, and when it’s better to learn a bit more before jumping in.

9. Detecting false urgency
It pushes you to ask: what really happens if we wait? This helps teams avoid rushing into decisions just because it feels urgent in the moment.

10. Improving judgment over time
When you look back at your predictions versus what actually happened, you start to notice patterns… where you’re too confident, where you miss risks, and where your gut actually gets it right.

From Confidence Theater to Real Judgment

One thing I have become quietly obsessed with is confidence calibration. In product work, everything sounds high confidence once it is written down. But most decisions actually live somewhere in the middle. Product Decision Partner treats confidence as a first-class concept. High confidence decisions deserve commitment. Medium-confidence decisions should be reversible. Low confidence decisions need learning, not priority production.

This simple shift changes how teams behave.

Clarity does not come from having all the answers, but from knowing how confident you should be in them.

Another challenge I see all the time is audience mismatch. The same insight should not be shared the same way with an executive, an engineer, and a designer. Yet most teams end up rewriting the same thinking over and over, losing precision each time. A good decision partner should keep the thinking intact while adapting how it is expressed.

Context matters too, especially over time. Real partners remember what you are working on, what constraints you are under, and what you have tried before. They learn how you tend to reason and where your blind spots are. Product Decision Partner is built around this idea. It is not just about the next decision, but about learning from the last ones.

When clarity finally shows up, it can turn that thinking into whatever artifact you need: an executive summary, a PRD, a research synthesis. But the artifact comes last. Documentation is the result of good thinking, not a substitute for it.

At its core, Product Decision Partner isn’t meant to replace human judgment. It exists to strengthen it by making tradeoffs clearer and assumptions explicit.

Product Decision Partner is opinionated, but honest about uncertainty. It will tell you when the evidence is not there. It will say not yet. It will slow you down when slowing down is the right move, and help you move faster when the decision is reversible, and the upside is worth it.

I keep coming back to this idea because, in strategy work, decisions compound. So do bad ones. What has been missing is not another framework or tool, but a consistent way to turn ambiguity into defensible, well-calibrated decisions.

That is what Product Decision Partner is trying to be.

Not more process.
Not more artifacts.
Better judgment, starting from the blank page.

Adam Mico

LinkedIn | Product Decision Partner | Tableau Virtuoso GPT | VizCritique Pro GPT | Data Mockstar GPT | Writing Tone Clone GPT | Genetic Genealogy Researcher Partner Pro GPT | DataQuest: Choose Your Own Data Adventure GPT | TableauQuest: Real-World Tableau Decisions | GPT Architect Pro 5.2

Note: My book, Tableau Desktop Specialist Certification (no AI assistance), is available for purchase here.

I used to think I was just writing good structured instructions, but really, I was building systems.

I realized this while working with ChatGPT’s Model 5.2. It didn’t just make my tools better. It showed me how I really think. When I paid more attention to intent, context, and judgment rather than getting every word perfect, my results got more consistent. Looking back, I’ve always let structure take shape as I go, instead of trying to force it from the start.

A lot of people think in systems, even if they don’t call it that. Many of us aren’t drawing diagrams or mapping out flowcharts (me included, unless I am tasked with it). For years, I thought the way I saw things was just how my brain worked. But here’s the thing: some of us naturally let systems form as we work. Structure shows up when we pay attention to what’s happening, write, pause, think, and then write some more. We don’t force order. We let it reveal itself.

Looking back, I can see the signs were always there. As someone who is an autistic person in a neurotypical world, I slowly learned to watch first and act later. I pay attention to how decisions get made, what is valued, and which rules are real but never said out loud. If I don’t understand the system, I feel like I’m guessing and out of sorts, so I try to figure it out piece by piece. I notice when things don’t add up. When the rules change or expectations shift, it jumps out at me. These details matter. They tell me when something isn’t stable.

When things get chaotic, I don’t try to add more rules. I start asking questions. What changed? What broke? What’s missing?

While others might fix the surface problems, I look for what’s really going on underneath. I didn’t choose this approach because it looks good. I needed it to get by.

Thinking in systems means figuring out how something should work before you get into the details. Instead of starting with step-by-step instructions or worrying about every exception, you start with the basics. Who is this for? What really matters? What decisions count? What limits are real? The words and details come later. Start with the system.

With older AI models, you had to spell out every instruction. The more you controlled the words, the better things worked. Chaining prompts together felt clever. But with newer models like ChatGPT 5.2, things changed. When I stopped fussing over every word and focused on intent, context, and workflows, the models got more flexible, not less. That’s when it hit me. This is how I’ve always worked. The models just made it clear.

If you find yourself figuring out how things work before you jump in, or you spot inconsistencies right away, or you look for what’s broken when things get messy, you’re probably already thinking in systems. You didn’t pick this up from AI. AI just helped you see it.

Next time you’re stuck on a tough problem, don’t pile on more rules or string together more prompts. Watch for the moments when things start to make sense. If you notice structure showing up while you write, pause, or revise, trust that. You might already be thinking in systems. Now you know what to call it.

Adam Mico

LinkedIn | Writing Tone Clone GPT | Tableau Virtuoso GPT | VizCritique Pro GPT | Data Mockstar GPT | Genetic Genealogy Researcher Partner Pro GPT | DataQuest: Choose Your Own Data Adventure GPT | TableauQuest: Real-World Tableau Decisions | GPT Architect Pro 5.2 | Instagram

Note: My book, Tableau Desktop Specialist Certification (no AI assistance), is available for purchase here.

You Might Already Be a Systems-First Thinker was originally published in Bootcamp on Medium, where people are continuing the conversation by highlighting and responding to this story.

Quick link to GPT Architect Pro 5.2.

In early December, something broke.

GPTs that had worked well for months began to feel unstable. The jump from GPT-4o to GPT-5.1, then GPT-5.2, wasn’t just an update… it was a fundamental shift in how the model processes instructions.

Old approaches stopped working. GPTs built on long lists of rules and defensive prompting became brittle overnight. Meanwhile, GPTs designed as structured systems not only survived, they got better.

GPT-5.2 doesn’t reward tighter control. It rewards better structure.

That realization changed everything about how I build custom GPTs.

The Real Problem

Old approaches stopped working. GPTs built on long lists of rules and defensive prompting became brittle almost overnight. Meanwhile, GPTs designed as structured systems not only survived, they improved.

GPT-5.2 does not reward tighter control. It rewards better structure.

By “systems-first architecture,” I mean designing a GPT the way you would design software, not the way you would write a clever prompt.

A systems-first GPT starts with intent, not instructions. It defines what the GPT exists to do, how work should flow, where knowledge lives, and when actions should be triggered. All of that happens before a single line of wording is written. Instructions are produced by the system. They are not the system itself.

Prompt-first GPTs evolve differently. They grow by accumulation. More rules. More exceptions. More patches. They can work well at first, but they do not age well. When the model changes or edge cases arise, the underlying structure is too weak to adapt.

That realization of how the new model reacts to each type of GPT fundamentally changed how I approach building custom GPTs.

People are not failing to build GPTs. They are failing to build GPTs that hold up over time.

I have seen the same pattern play out again and again. A custom GPT launches and performs beautifully. Then a model update lands, or a new edge case appears, and the system starts to wobble. Fixes pile up. Each one introduces new side effects. Confidence fades.

The problem is not talent or effort. It is durability.

Most builders start by writing loosely connected prompts and calling them instructions. When something breaks, they add more rules. They upload knowledge files without organizing them. They attach actions without thinking through how work is supposed to move from one step to the next.

The hardest part of building a custom GPT isn’t writing the instructions. It’s making smart, intentional decisions before you write anything at all.

And there’s been no tool designed to guide those decisions, especially not for GPT-5.2.

Three Things (but this is not a keynote)

Just to set the table for this section… I’m not Steve Jobs. This isn’t a product reveal with a spotlight and a turtleneck.

With that, I always loved that moment during the 2007 iPhone launch where Jobs said he was introducing three products, then casually revealed it was actually one. Brilliant delivery. Great memory hook.

So, with all due respect, I’m borrowing the structure (not the drama).

If I were introducing GPT Architect Pro 5.2, I’d say I’m launching three things:

1️⃣ A builder for creating custom GPTs with clear workflows and system-first architecture

2️⃣ An evaluator that gives honest, actionable feedback on why your GPT behaves the way it does

3️⃣ A production system that ensures your GPT is stable, intentional, and ready to ship

But it’s not three tools.

It’s one complete system. GPT Architect Pro 5.2.

How This Came to Exist

One reason this framework stood out to me is personal (though not in any dramatic sense). As an autistic person, I naturally approach complex work through systems. Some non-autistic people do as well, but it is often situational rather than a default. In my case, it is a default. I look for structure before instructions, intent before rules, and workflows before jumping into tasks. Over time, I realized this was not just how I think. It is also how GPT-5+ works best. I did not assume that upfront. I tested it repeatedly. The more I leaned into a system-first approach, the more stable and resilient the GPTs I built became, even as models changed from 5.1 to 5.2 and edge cases accumulated.

Over the past month and a half, I built and rebuilt more than 30 custom GPTs. About two-thirds were for work, the rest were public.

I wasn’t just building. I was testing how GPT-5.2 actually processes instructions. What holds up. What breaks. What patterns emerge when you stress-test a GPT across edge cases and model updates.

Every build revealed something new about durability. Patterns started to emerge around workflows, knowledge architecture, and action logic. Small decisions made early determined whether a GPT would stay reliable or fall apart later.

Those patterns became a framework. That led me to write Guide to Building Effective GPTs Part 2 (because my April 2025 post was already obsolete). That framework and other bits became GPT Architect Pro 5.2.

Then I used the tool itself to quietly update a few of my public GPTs. They’re more stable now. More intentional. Better aligned with how GPT-5.2 actually works.

This isn’t theory. It’s battle-tested.

What It Actually Does

GPT Architect Pro 5.2 does three things that used to require multiple tools and hours of iteration:

1. Builds new GPTs from scratch in a fraction of the time
Instead of starting from a blank instruction box, it guides you through a GPT-5.2–optimized design process: define purpose, design workflows, structure knowledge, evaluate actions, then generate production-ready instructions.

2. Evaluates any GPT independently
Paste your instructions and knowledge files. It audits architecture, identifies gaps, recommends missing knowledge, and helps create knowledge indexes and documentation; it’s like having an expert review your GPT.

3. Enables in-context self-iteration
Run an evaluation, get concrete feedback, and iterate immediately without switching tools or starting over. The same framework that builds also evaluates and refines.

The result isn’t just faster. It’s stronger architecture, more efficient performance, and GPTs that remain stable as models evolve.

Why Integration Matters

Most tools force you to build in one place, evaluate somewhere else, and manually translate feedback into fixes.

GPT Architect Pro 5.2 does all three in the same conversation using the same framework.

Build → evaluate → iterate. No context switching. No translation layer. No starting over.

That’s not just convenient. It’s what makes optimization possible. When the tool that evaluates your GPT uses the same principles that built it, feedback is actionable immediately. You don’t just save time. You compound quality with every iteration.

And because the same framework can be applied reflectively, instructions can be evaluated and refined in context, keeping the system aligned with GPT-5.2 as a living standard rather than static documentation.

Ready to Use

GPT Architect Pro 5.2 is live and free to use.

It’s built for people creating real tools, not novelty bots. For builders who want their GPTs to feel stable, intentional, and ready to work, not just today, but across model updates and real-world usage.

If you’re tired of endlessly tweaking instructions, or if you’ve watched a GPT you built fall apart after a model update, this is for you.

👉 Try GPT Architect Pro 5.2 →

GPT-5.2 doesn’t reward clever tricks. It rewards structure, clarity, and intention.

This is how you build for that.

Adam Mico

LinkedIn | Writing Tone Clone GPT | Tableau Virtuoso GPT | VizCritique Pro GPT | Data Mockstar GPT | Genetic Genealogy Researcher Partner Pro GPT | DataQuest: Choose Your Own Data Adventure GPT | TableauQuest: Real-World Tableau Decisions | GPT Architect Pro 5.2 | Instagram

Note: My book, Tableau Desktop Specialist Certification (no AI assistance), is available for purchase here.

Introducing GPT Architect Pro 5.2 was originally published in The Generator on Medium, where people are continuing the conversation by highlighting and responding to this story.

This post introduces TableauQuest and the thinking behind its design. It explains why TableauQuest exists, what problem it’s designed to explore, and why that problem couldn’t be addressed until recently.

For the past couple of years, I’ve been building a suite of custom Tableau GPTs to support Tableau practitioners across the full analytics lifecycle. Tools for generating realistic mock data. Getting reliable product guidance. Critiquing dashboards. Creating clear documentation. Helping viewers understand dashboards when documentation is missing.

Note: all custom models, including this one, are covered at the bottom of the blog.

Together, they support creation, review, delivery, and consumption. They answer questions, reduce friction, and make good work easier.

But there was always one gap.

None of those tools addressed what happens when the dashboard is technically right, and things still go wrong.

The judgment calls.

The tradeoffs under pressure.

The decisions that seem reasonable in isolation but unravel inside real organizations.

That gap existed not because it wasn’t important, but because it was hard to model. I tried with previous models but couldn’t get it right. Teaching features is straightforward. Teaching judgment is not.

Recent advances in ChatGPT’s reasoning capabilities finally made that possible. Specifically, the model’s ability to maintain complex state across multi-turn conversations (tracking decisions, their ripple effects, and emerging patterns) without requiring explicit prompting or external memory systems. This allowed TableauQuest to remember how you operate under pressure and adapt the organizational response accordingly.

TableauQuest was built to fill that final gap by turning Tableau learning into a decision-driven experience rather than an instructional one.

The dashboard was right… but it still went wrong.

That sentence captures the problem TableauQuest was built to explore.

Most Tableau training rests on a quiet assumption: if people learn the features, the work will go well. In practice, that assumption rarely holds. Dashboards fail even when the analysis is sound. Projects stall despite clean logic. Trust erodes for reasons no tutorial ever addresses.

TableauQuest exists for that space between correctness and consequence.

It is not a course, a simulator, or a certification tool. It is a choose-your-own-adventure experience designed to teach Tableau through decisions, pressure, and outcomes; this is how I found analytics unfolds in real organizations.

Technically correct work can still fail socially, politically, or organizationally.

What TableauQuest Does Differently

TableauQuest does not teach by explaining.
It teaches by placing you in situations.

Requirements are incomplete. The data mostly works until it does not. Performance, publishing, governance, and trust introduce friction at exactly the wrong moments. You choose what to do next, and the world responds.

Nothing is explained.
Nothing is optimized for success.
The story continues and remembers.

What makes TableauQuest distinct is not the game format itself, but what it emphasizes. It does not focus on how to build a view. It focuses on how decisions behave once they leave the workbook and enter an organization.

It surfaces the tension between speed and trust, clarity and performance, visibility and risk. It shows how technically reasonable choices can still fail once people, power, and context enter the picture.

Here is what that looks like in practice

You published the dashboard on Thursday afternoon. By Monday morning, three executives are citing different revenue numbers in the same meeting. You used a parameter to allow users to switch between fiscal and calendar-year views. It was a reasonable choice for flexibility. But no one reads tooltips. The documentation was never opened. Now, finance is questioning whether your team understands basic accounting.

Do you add a prominent warning label that clutters the interface?
Schedule training sessions that delay other projects?
Restrict the parameter to reduce confusion but lose the flexibility stakeholders requested?

Each option is technically reasonable. Each one creates different organizational consequences.

This is what TableauQuest teaches: living with the consequences of your decisions.

From Scenarios to Systems

Early versions of TableauQuest were intentionally simpler. Scenarios were self-contained. Decisions led to outcomes. Endings arrived quickly. The learning was real, but local.

That’s not how analytics work; it accumulates risk or trust. In real organizations, decisions echo. Reputations linger. Trust doesn’t reset just because a new project starts.

Earlier versions couldn’t hold onto that continuity; the underlying reasoning capability wasn’t strong enough yet. As the model improved, TableauQuest evolved.

As the model improved, TableauQuest evolved with it.

Today, TableauQuest doesn’t just respond to what you choose. It adapts to how you tend to choose. Over time, the system forms a quiet picture of how you operate under pressure.

Do you favor speed when trust is fragile?

Do you treat governance as friction or protection?

Do you repair mistakes when they surface, or move on and leave fallout behind?

The system tracks these patterns without displaying them. There are no meters, profiles, or feedback screens. Instead, the organization inside the story adapts. Stakeholders become more cautious or more trusting. Oversight tightens or loosens. Recovery paths widen or narrow. Some failures leave scars that persist well beyond the moment they occur.

This mirrors how real analytics organizations work.

Your reputation doesn’t reset at the start of each project.

Quest Play and the Learning Path

TableauQuest can be played as a single scenario or as a longer campaign.

In single-scenario play, you step into one Tableau situation and follow it to a clear ending. This mode works well for exploring a specific type of problem or stress-testing a decision in isolation.

The ending doesn’t tell you whether you succeeded. It shows you the state of the world now; what held together and what remains fragile.

Campaign play transforms the experience into a learning path. Scenarios connect across time. Earlier decisions quietly shape later constraints. Trust accumulates or fades. Scrutiny increases or recedes.

By the time you reach the capstone scenario (focused on publishing, governance, and organizational exposure), you are not starting fresh. You are carrying a history.

The game remembers you, even though it never tells you what it remembers. Your choices create an invisible profile that shapes which challenges surface, how much grace you’re given when problems emerge, and whether stakeholders approach your work with curiosity or suspicion.

Endings, Replay, and Reflection

Each scenario ends with a clear end screen but no verdict.

Outcomes are framed as patterns of tradeoff, not wins or losses. Some endings look stable while hiding risk. Others surface failure early and visibly. Others contain damage without resolving underlying tension.

Replay is treated as exploration, not improvement. Players can view alternative timelines with short narrative sketches of how the situation might have unfolded differently.

These aren’t hints. They don’t reveal which choices lead where. They exist to broaden perspective, not narrow behavior.

Campaign finales offer a different kind of closure. Rather than declaring mastery, they reflect identity. They summarize the decision patterns that defined the journey and describe the kinds of decisions the world would now trust this person with.

The ending is descriptive, not aspirational.

Realism Without Instruction

TableauQuest is grounded in real Tableau behavior. Official Tableau documentation is used behind the scenes to determine what is possible, what is constrained, and what fails under certain conditions.

That knowledge never surfaces as instruction. It shapes the world, not the player.

Performance issues, calculation-context problems, publishing constraints, and governance failures emerge naturally rather than being taught explicitly. The system has been stress-tested as a learning experience — not a game or a tutorial — focused on coherence under failure, meaningful consequence over time, and realism under pressure.

Who It’s For

TableauQuest is for people who already know how to use Tableau but want to understand why things still go wrong.

It works well for practicing analysts, analytics engineers, BI leads, data leaders, and educators who want to teach judgment rather than syntax. It is intentionally uncomfortable for anyone looking for step-by-step guidance or quick fixes.

Many Tableau failures are not technical failures. They are decision failures made under pressure, uncertainty, and incomplete authority.

Traditional training prepares people for clean datasets and clear questions.

TableauQuest prepares people for everything else.

Try TableauQuest

👉 TableauQuest

If you’ve ever found yourself saying, “The dashboard was right, but it still went wrong,” this experience was built for you.

It’s not here to replace training.
It exists to cover the part of the work that training leaves out.

Tester shoutout

Big thanks to Will Sutton, Priya Padham, and Joy Victor for providing pre-release feedback.

The Full Tableau GPT Ecosystem

TableauQuest is the final piece in a broader suite of custom Tableau GPTs designed to support practitioners across the entire lifecycle of analytics work from data creation to decision-making in production.

Each tool focuses on a specific stage of the journey:

Data Mockstar
Early development
Generates realistic mock data with constraints, domains, and custom formats. Best used when real data doesn’t exist yet, is inaccessible, or can’t be shared safely. Major Upgrade: December 2025. (4.8/5 rating with 40+ ratings, over 1,000 conversations)

Tableau Virtuoso
Build and platform guidance
Acts as a secure Tableau product assistant, providing clear, trustworthy guidance across Tableau Server, Cloud, Desktop, Prep, and Blueprint. Major Upgrade: December 2025. (4.5/5 rating with 50+ ratings, over 5,000 conversations)

VizCritique Pro
Review and iteration
Analyzes dashboards and provides professional, actionable critique focused on clarity, effectiveness, and integrity-first design. Major Upgrade: December 2025. (4.5/5 rating with 40+ ratings, over 1,000 conversations)

Dashboard Documentation Pro
Delivery and handoff
Helps creators produce clear, ethical, and user-centered dashboard documentation that supports long-term understanding and responsible use. Major Upgrade: January 2026. (4.2/5 rating with 5+ ratings, over 200 conversations)

Dashboard Detective GPT
Consumption and interpretation
Helps viewers understand dashboards when documentation is limited or missing by explaining visual elements, metrics, and intent from a screenshot alone. Major Upgrade: January 2026. (not enough ratings, over 100 conversations).

TableauQuest
Decision-making in context
Focuses on judgment, tradeoffs, and consequences after the dashboard leaves the workbook — where technical correctness meets organizational reality. (5/5 rating with 5+ ratings, over 100 conversations)

Together, these tools are not meant to replace training or experience. They are designed to support real practitioners at the moments where friction, ambiguity, and risk actually show up.

TableauQuest completes the spectrum by addressing the part of the work the others can’t: making decisions when the rules are unclear and the stakes are real.

Adam Mico

LinkedIn | Writing Tone Clone GPT | Tableau Virtuoso GPT by Adam Mico | VizCritique Pro GPT | Data Mockstar by Adam Mico GPT | Genetic Genealogy Researcher Partner Pro GPT | DataQuest: Choose Your Own Data Adventure GPT | TableauQuest: Real-World Tableau Decisions | Instagram

Note: My book, Tableau Desktop Specialist Certification (no AI assistance), is available for purchase here.

TableauQuest: When Learning Tableau Becomes a Game of Decisions was originally published in Geek Culture on Medium, where people are continuing the conversation by highlighting and responding to this story.

When I tested out MagicPost, a third-party tool that’s supposed to give a year in review of your LinkedIn activity, I couldn’t help but think of all the other tools that churn out snappy insights, eye-catching visuals and a polished summary.

Earlier today I shared a shorter version of this analysis on LinkedIn (after reviewing my full platform export ) because something about the summary felt off. The response confirmed what I was starting to realize: people aren’t just amused by confident AI summaries. They trust them. Read the original LinkedIn post here.

Well-known as a LinkedIn tool, MagicPost’s attractive, refined, and shareable presentation caught my attention, but its analysis didn’t sit well with me. The year-long themes and the analysis in particular, didn’t accurately portray the essence of my work, or didn’t even come close. The forecasted 2025 ‘Data Science year’ basically seemed reasonable, but didn’t feel at all like the real thing, nor was it what I’d often spoken out about.

Coming from a place that’s not LinkedIn, I would’ve been completely unable to check the accuracy of any of MagicPost’s story. But, I already had done so, and when I found out the location of my data, I requested a copy and about 24 hours later, I was looking at my 2025 activity, and I had no other option but to re-evaluate my online performance, now knowing that I had 129 posts and 2,499 brand-new followers in that year. MagicPost’s attempt at a summary, on the other hand, had me posting 41 posts and gaining 819 followers, essentially rewriting my story of consistency, engagement and growth.

Even the thematic breakdown presented by MagicPost and what I see in my own LinkedIn activity, shows almost no posts and falls apart from the narrative given by the tool. Since I could see that lots of my posts covered multiple subjects, I rechecked my year and it looks completely different. I often featured community leadership (76.7%), GenAI and AI tools (61.2%), and Tableau, data visualization & strategy (56.6%) in my posts, with these topics frequently overlapping. When I reviewed the year through my own data, I saw work that cut across building, visual storytelling, and community projects. That nuance was missing from the dashboard summary, and that gap is what concerned me.

This is not just because the dashboard looked odd, but its lack of accuracy can be dangerous.

If I had not had an understanding of my output, I would have received a totally skewed view of what really happened. The issues with these kinds of tools are that they’re very adept at identifying patterns, but completely disregard the background behind, the motivations, and the weighing of positive and negative points, can’t even tell which indicators are significant and why specific posts connected so well with the audience and how consistency develops over time.

They’d rather gloss over things and make the information look orderly rather than being completely honest.

My relatively mild consequence, a somewhat amusing year-in-review picture, was a good wake-up call for me, yet, these errors can have much more severe implications in systems that have greater significance, such as hiring screens, financial credit decisions, healthcare triage and risk analysis, all of which can come across as crystal-clear but are really based on a flimsy comprehension of the situation. I’m not bashing AI tools or dashboards, they can be very useful but they can’t solve the puzzle of giving you real insight.

If a system, one that can affect people’s livelihoods, careers, money and trust, is based on wrong assumptions, you can expect a complete misfire and a whole lot of incorrect information being presented as the truth.

This isn’t an anti-AI post. I use these tools. I build with them. They are incredibly useful with human oversight.

But polish isn’t insight. Confidence isn’t accuracy.

Any system that influences people, careers, money, or trust needs human review, context, validation, and accountability when it gets things wrong. Otherwise, we’re just shipping assumptions and calling them analysis.

Adam Mico

LinkedIn | Tableau Public | Writing Tone Clone GPT | Tableau Virtuoso GPT by Adam Mico | VizCritique Pro GPT | Data Mockstar by Adam Mico GPT | Genetic Genealogy Researcher Partner Pro GPT | DataQuest: Choose Your Own Data Adventure GPT | Instagram

Note: My book, Tableau Desktop Specialist Certification (no AI assistance), is available for purchase here.

Partial Context, Full Authority was originally published in Bootcamp on Medium, where people are continuing the conversation by highlighting and responding to this story.

Growing up, there was one kind of fiction book I could always finish: Choose Your Own Adventure.

Not because the writing was better than anything else, but because my decisions mattered. I wasn’t just reading a story; I was shaping it. If something went wrong, it was because of a choice I made. That made the experience feel real in a way most books didn’t.

Looking back, I think that was the first time learning felt interactive to me, even though I didn’t have the language for it then.

Data education often misses this

Most data learning still looks like slides, definitions, and best practices delivered out of context.

But real data work is full of tradeoffs, uncertainty, and consequences. You don’t learn good judgment by memorizing rules. You learn it by making decisions and seeing what happens.

That gap stayed with me for a long time.

Bringing that old energy into a new medium

I kept coming back to a simple question: What if learning data felt more like those books I loved growing up?

Where you’re dropped into a situation, you make a choice, the world responds, and you reflect and try again.

GPT technology finally made that possible.

Instead of just answering questions, large language models are good at simulating scenarios, reasoning about outcomes, and adapting to context. Used thoughtfully, they’re a powerful vehicle for experiential learning.

So I built DataQuest, a choose-your-own-adventure-style GPT for data literacy. You can try it here.

What DataQuest does

It offers decision-driven learning where every step is a choice with consequences. The experience adapts to your age level, whether that’s kid-friendly, teen-appropriate, or professional. You can focus on visualization, analytics, data science, data engineering, coding logic, or a mix of everything. Choose from themes like workplace scenarios, fantasy worlds, sci-fi settings, noir mysteries, school environments, and more. Your choices carry real stakes: soft failures, hard failures, and partial success. And because different choices lead to different outcomes, you can replay paths to explore alternative decisions.

Why I wanted to build this

Those old books worked for me because they offered agency, consequence, and replayability.

DataQuest is my attempt to bring that same energy to learning data, using GPTs not as answer machines, but as experience generators.

It’s not meant to replace courses or projects. It’s meant to complement them, especially for people who learn best by doing.

Sample completion

In this adventure, I chose data visualization and storytelling. I started with a couple of minor errors in judgment but completed strongly. Here are the screen prints of its result.

Also note: There is a pixel art description that can be easily generated into an image (I don’t automatically enable this for time). Here is the image of this odyssey.

Closing thought

Learning sticks when you feel responsible for what happens next.

Choose-your-own-adventure books figured that out decades ago. GPTs finally give us a way to bring that idea into modern data education.

That’s what I wanted to explore, and this is just the beginning.

Adam Mico

LinkedIn | Tableau Public | Writing Tone Clone GPT | Tableau Virtuoso GPT by Adam Mico | VizCritique Pro GPT | Data Mockstar by Adam Mico GPT | Genetic Genealogy Researcher Partner Pro GPT | DataQuest: Choose Your Own Data Adventure GPT | Instagram

Note: My book, Tableau Desktop Specialist Certification (no AI assistance), is available for purchase here.