Bigeye Staff
bigeye-staff
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June 1, 2026

The Slack emoji pack for data and AI teams

4 min read

Bigeye Staff
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To celebrate what we’ve been building, we made a free Slack emoji pack for data teams, AI builders, and anyone who needs a better way to react when the dashboard looks perfect, the pipeline breaks, or someone says “quick question” five minutes before standup.

The pack includes a set of custom Bigeye frog emojis for the full emotional range of modern data work: magical data moments, confusing alerts, clean charts, messy incidents, and a few things that are best expressed with a tiny frog being abducted by a UFO.

What’s inside the pack

The download includes custom Slack-ready PNGs featuring:

  • The output looks suspicious
  • The data behind an answer needs a closer look
  • “Everything is fine” frogs
  • “Everything is not fine” frogs

Use them to celebrate wins, mark mysteries, react to incidents, or make your team’s Slack just a little more charming.

The pack will open in Google Drive. Click the download icon to save the full set.

[Download the emoji pack]

How to add the emoji pack to Slack

Once you download the ZIP file, unzip it so you can access the individual PNG files.

Then add each emoji to Slack:

  1. Open Slack on desktop.
  2. Click the smiley face icon in the message field.
  3. Click Add Emoji.
  4. Click Upload Image.
  5. Select one emoji PNG.
  6. Give it a short name, like :magic_frog: or :data_frog:.
  7. Click Save.
  8. Repeat for the rest of the pack.

A few tips:

  • Keep names short and easy to remember.
  • Use underscores instead of spaces.
  • Pick names your team will actually type, like :frog_fire:, :data_magic:, or :clean_data:.
  • If you do not see the option to add custom emoji, your workspace owner or admin may need to enable it or upload the files for you.

Why we made these

Trusting AI agents is not just about whether an answer sounds right. It is about knowing what the agent did, what data it used, whether that data was reliable, whether sensitive information was involved, and whether the right policies were followed.

AI trust is serious work. Your Slack does not have to be.

Use them when things are going well. Use them when they really are not. Use them when the data looks magical, the model is acting mysterious, or the team needs a tiny frog to say what words cannot.

Download the pack, add it to Slack, and react accordingly.

Want to see what we’re building?

Teams need a better way to understand what AI agents are doing, what data they’re using, and whether the right controls are in place.

We’re launching something new for that tomorrow.

Join the waitlist to be first in line.

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Resource
Monthly cost ($)
Number of resources
Time (months)
Total cost ($)
Software/Data engineer
$15,000
3
12
$540,000
Data analyst
$12,000
2
6
$144,000
Business analyst
$10,000
1
3
$30,000
Data/product manager
$20,000
2
6
$240,000
Total cost
$954,000
Role
Goals
Common needs
Data engineers
Overall data flow. Data is fresh and operating at full volume. Jobs are always running, so data outages don't impact downstream systems.
Freshness + volume
Monitoring
Schema change detection
Lineage monitoring
Data scientists
Specific datasets in great detail. Looking for outliers, duplication, and other—sometimes subtle—issues that could affect their analysis or machine learning models.
Freshness monitoringCompleteness monitoringDuplicate detectionOutlier detectionDistribution shift detectionDimensional slicing and dicing
Analytics engineers
Rapidly testing the changes they’re making within the data model. Move fast and not break things—without spending hours writing tons of pipeline tests.
Lineage monitoringETL blue/green testing
Business intelligence analysts
The business impact of data. Understand where they should spend their time digging in, and when they have a red herring caused by a data pipeline problem.
Integration with analytics toolsAnomaly detectionCustom business metricsDimensional slicing and dicing
Other stakeholders
Data reliability. Customers and stakeholders don’t want data issues to bog them down, delay deadlines, or provide inaccurate information.
Integration with analytics toolsReporting and insights
about the author

Bigeye Staff

Bigeye Staff represents the collective voice of the Bigeye team. Each article is informed by the expertise of individual contributors and strengthened through collaboration across our engineers, data experts, and product leaders, reflecting our shared mission to help teams build trust in their data.

about the author

about the author

Bigeye Staff represents the collective voice of the Bigeye team. Each article is informed by the expertise of individual contributors and strengthened through collaboration across our engineers, data experts, and product leaders, reflecting our shared mission to help teams build trust in their data.

Get the Best of Data Leadership

Subscribe to the Data Leaders Digest for exclusive content on data reliability, observability, and leadership from top industry experts.

Want the practical playbook?

Join us on April 16 for The AI Trust Summit, a one-day virtual summit focused on the production blockers that keep enterprise AI from scaling: reliability, permissions, auditability, data readiness, and governance.

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