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50 data leaders from groundbreaking companies across the globe were asked one question:
What are we doing today as data teams that we'll look back on in 20 years as cringe?
The things that will
make our future selves
shake our heads at
how we used to work.
make our future selves
shake our heads at
how we used to work.
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Data & Analytics
Migrations
Data teams spend a shocking amount of time simply moving existing functionality onto new platforms.
Tristan Handy
Founder & CEO of dbt Labs
AI
Trying to get LLMs to write SQL
Carlin Eng
Product Manager at Meta, working on Malloy
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Data & Analytics
Dragging and dropping data fields to build a dashboard
Kate Strachnyi
Founder of DATAcated
Data Culture
The very idea of "data" teams
That there is a central team that specializes just in data, rather than domain-oriented information professionals that combine data and technical skills with domain knowledge to produce cohesive knowledge products.
Jenna Jordan
Sr. Consultant at Analytics8
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AI
Allowing end users to write queries
That's what agents are for!
Bob Muglia
Entrepreneur & Former CEO of Snowflake
Data & Analytics
Storing data in tables
Like... actual rows and columns?
Ravit Jain
Founder & Host of The Ravit Show
Data Culture
Central data teams
Having one team manage all analytics needs for an entire business without domain expertise.
Ruth Onyekwe
Data & Analytics Manager at Spaulding Ridge
Data Culture
Defining what a data product is
In the future, all data will be treated as products.
Tiankai Feng
Author of Humanizing Data Strategy
AI
Having to write code in order to leverage ML models for data analyses
Assaf Levinson
Sr. Director of Product Analytics at Gong
Data Culture
Sending users "pictures of their data"
Sharing data in PowerPoint or PDF rather than having them interact with their data and answer new questions with it.
Mark Nelson
Venture Partner at Madrona, former President & CEO of Tableau
Data & Analytics
SQL
Sarah Levy
Co-Founder & CEO of Euno
AI
The work we put in to get value out of data
We're about to go through a condensed equivalent of the Industrial Revolution in knowledge workers. The velocity of this change will displace a whole swath of tasks we do today and open doors we didn't think were possible for putting our data to work.
Ross Helenius
Dir. AI Transformation Eng. & Architecture at Mimecast
Data & Analytics
Excel
And we'll probably still be using it in 2045.
Gio Granato
Sr. Director of Data, ML & AI at Checkr
Data & Analytics
Table naming conventions
Lidor Avitan
Data Engineer at Wiz
Data & Analytics
The modern data stack
Many of the tools we use now will either become features of other tools or be replaced entirely by AI agents. So spending money on learning and integrating dozens of tools to build highly decoupled data stacks with 15-20 different tools will soon feel absurd (within the next few years, if I were a betting woman).
Lindsay Murphy
Head of Data at Hiive
AI
Trying to replace analysts with AI
Richard Makara
Co-Founder & CEO of reconfigured
Data Culture
Data silos
When data teams worked completely disconnected from business teams.
Eva Schreyer
Head of Data & Analytics at Neugelb Studios GmbH
Data Culture
Being called a data analyst
Data tooling and literacy keeps improving so anyone can do data analysis.
Richard Cotton
Senior Data Evangelist at DataCamp
Data & Analytics
Deploying AI models WITHOUT observability
Barr Moses
Co-Founder & CEO of Monte Carlo
Data & Analytics
Cluttered dashboards
Future data teams will laugh at how we drowned our users in endless dashboards, instead of crafting self-adapting data products that deliver the few insights truly driving decisions and impact.
Nir Smilga
Data Viz Manager at monday.com
AI
Writing SQL manually by hand like it's a love letter
Ankur Batra
Analytics Eng. Lead at Wolt
Data & Analytics
Batch ETL
They barely make sense anymore, but letting go is still too damn hard.
Doron Porat
Podcast Co-Host of The Data Swamp & Data Builders
AI
Unused dashboards
We spend weeks building dashboards that are never looked at. We'll shift from imperative (current dashboards) to declarative. Future leaders will be skilled at asking the right questions, and technology won't just provide answers—it will differentiate causality from correlation and make inference much easier. Ultimately, this will lead to highly productive collaboration between humans and AI.
Tatsiana Maskalevich
Data Science & Eng. Leader, Former Dir. at Netflix
Data Culture
Data contracts
Just ping the engineer on Slack if something breaks.
Harry Gollop
Podcaster & Co-Founder of Cognify Search
AI
Writing queries!
AI will do it. We just approve.
Tom Abraham
Data & Analytics Manager at Caring
Data & Analytics
Data modeling doesn't matter
Joe Reis
Bestselling Author & Host of The Joe Reis Show
Data Culture
Hiring data leaders based on their ability to code
Dylan Anderson
Head of Data Strategy at Profusion
AI
Relying on human intuition for data modeling
AI will generate adaptive schemas in real time, and we'll wonder why we didn't trust robots with the boring stuff.
Sharath Chandra
Data Eng. Leader at Figma
Data & Analytics
Merging data-related PRs and hoping nothing breaks
Looking back, we'll find it hard to believe how blindly we operate today. In the future, data testing practices will be in place, giving us a clear picture of the effects in advance.
Silja Märdla
Senior Analytics Engineer at Bolt
Data & Analytics
Dashboards
It's 2025, we build dashboards like it's the 2010s, and our stakeholders download them as CSVs to power their Excel files like it's the 2000s.
Tim Hiebenthal
Lead Analytics Engineer at Project A
Data Culture
"Influencing" on "LinkedIn"
Benn Stancil
Founder, Chief Analytics Officer, CTO of Mode
Data & Analytics
How reactive data teams are
Does a stakeholder need something immediately? Do you drop everything to calculate that metric for them? Spend hours adding it to one of your core dashboards? You'd be surprised how many requests work themselves out without taking critical time away from the core work of a data team. Sure, we exist to serve stakeholders, but it's also our job to move the business in a direction that's more sustainable and dependable. Even if it means slowing down to uncover the root cause of the problem.
Madison Schott
Senior Analytics Engineer at Kit
AI
Thinking analysts will be replaced by AI
Dan-ya Shwartz
Dir. of Product Growth & Marketing at Meta
Data & Analytics
Thinking the data model will disappear
We keep predicting that data modeling is 'dead' because new tools or AI-driven transformations will take over. In reality, we'll still be using Kimball foundations 20 years from now, but with a futuristic twist of automated documentation, analytics bots, and AI-driven data governance layered on top.
Omar Brid
Senior Analytics Engineer at Scopely
Data & Analytics
Thinking you need to build a data warehouse so everyone has one source of truth
Inna Weiner
VP Product at AppsFlyer
AI
"Head of AI"
Just like Chief Innovation Officer was a thing for a hot second and is now such a cringe title. All people were basically doing was signaling that they were focusing on disruption, but as it turns out, making a central function for innovation is kind of useless. Innovation is a value that needs to be embedded in everything you do. AI will be the same.
Kaitlyn Henry
Chief of Staff at dbt Labs
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Data Culture
Touching data at all
Data will flow seamlessly into decision-making and we'll just believe it.
Timo Dechau
Event Data Wizard at deepskydata ApS
Data & Analytics
Data connectors
Picking data connectors from 500-strong directories for ETL. As someone old enough to have built websites with Macromedia Dreamweaver back in 2000, I suspect data engineers will chuckle at those good old days. Soon, millions of data pipelines will be created, shared, and deployed instead.
Matthaus Krzykowski
Co-Founder & CEO of dltHub
Data & Analytics
Static dashboards
We'll cringe at how we once treated them as the end goal instead of focusing on delivering real insights. We spent hours perfecting charts, only for them to sit untouched. The future isn't in prettier reports—it's in insights that surface exactly when and where they're needed, driving action without another dashboard login.
Shenhav Lavie
Sr. Director of Data Development at Fiverr
Data & Analytics
Data lakes
Everyone insisted on using them, and we ended up with data swamps.
Yair Weinberger
Entrepreneur & Investor, former Dir. Eng. at Google
AI
Ad-hoc data pulls
Analytics Engineers will deliver cleaner data faster, with AI agents supporting stakeholder queries.
Anurag Gangal
Data Science Manager at Spotify
Data & Analytics
Schema-on-Read
We made collecting data too easy and using it too hard. Things like schema-on-read, change data capture, and data lakes make it easy and cheap to write data, but incredibly difficult and expensive to transform it into something usable.In 20 years, we'll cringe at those costs and be thankful we now apply more discipline to data collection.
Andrew Jones
Data Consultant, Creator of Data Contracts
Data Culture
Data engineers who don't understand business context
The modern data engineer will no longer 'simply' execute what needs to be done but will actively work toward improving day-to-day operations.
Senne Vermassen
Senior Analytics Engineer at ABN AMRO
Data & Analytics
Data cleaning
Raz Widrich
Curator of Future Data Cringe
Data & Analytics
Obsessing over tooling
dbt or SQLMesh? Tableau, PowerBI, or Looker?
Jeremy Chia
Analytics Engineer at Vinted
Data & Analytics
How little we tested our analytics workflows
One day, we'll cringe at how we prioritized user-friendliness and speed over maintainability and bug prevention in our rush to make analytics accessible. Using tools without a robust testing framework is like driving without a seatbelt.
Juan Manuel Perafan
Podcaster & Co-Author of Fundamentals of Analytics Engineering
Data & Analytics
Silos and artificial fragmentation
Countless times, they lead to obsessions without business context and the need to justify basics like Data and AI Governance.
Jovita Tam
Data & AI Advisor
AI
Building dashboards for unasked questions
We create visualizations and dashboards in the hope that they will help users figure out answers to questions they haven't yet asked. In 20 years, we'll look back and say, "That's crazy! That never works." You have to ask the question first; only then can you determine the right visualization.
Eyal Firstenberg
CTO & Co-Founder of Euno
Data Culture
Tech debt.
We won't believe how long we went without a clear strategy for it
Rachelle Calip
Analytics Engineer
Data & Analytics
Endless dbt model bloat
Every minor transformation gets its own model because we fear editing anything upstream. "Our dbt project has 5,000 models... it made sense at the time!"
ChatGPT
A data leader in the making (no edits made!)
