Migrating from Tableau Prep to Mammoth

This guide is for Tableau Prep users who want to know whether Mammoth should complement or replace it. Both tools use a visual approach to data preparation. Mammoth adds an AI Prompt bar, built-in dashboards, and Automations.

The central question is whether dashboard and data preparation requests keep going through a few trained specialists, or whether more of your team builds its own pipelines and dashboards. The guide covers the decision framework, a feature comparison, and a phased migration plan.

Strategic positioning: Replace or complement?

The right approach depends on your current tooling and dashboard needs. Use this decision framework:

Path 1: Complement strategy

Choose this when:

• You have invested heavily in Tableau training
• You need advanced analytics workspaces occasionally
• Dashboard creation takes too long and frustrates stakeholders
• You want more people to access data without losing Tableau expertise

How it works:

• Keep Tableau for sophisticated analytics
• Use Mammoth for faster dashboard creation
• Use Mammoth for data preparation requiring business user access
• Send prepared data from Mammoth to Tableau (Export → Tableau) when advanced analytics are needed

Path 2: Replacement strategy

Choose this when:

• You're evaluating dashboard tools for the first time
• Most users struggle with Tableau Prep's technical requirements
• Dashboard needs are primarily operational reporting
• Cost reduction is a priority

How it works:

• Full migration to Mammoth for data prep + dashboards
• Single platform from raw data to shared dashboards
• Reduce the number of Tableau licenses you need
• Export complex scenarios to other BI tools as needed

Tip: Quick test
Ask yourself: "How long does it take to create a new dashboard?" If the answer is "too long", consider the complement strategy. If you do not currently use Tableau Desktop, consider replacement.

Feature comparison: Technical capabilities

This table compares core data preparation and visualization capabilities:

Feature category	Tableau Prep	Mammoth	Notes
Interface	Visual flow designer	Visual Pipeline in the browser	Both are visual; Mammoth runs in the cloud
Data preparation	Flow steps and calculated fields	Pipeline steps, plus the AI Prompt bar that generates steps from a description	AI-generated steps are reviewed before you apply them
Dashboard creation	None (requires Tableau Desktop)	Built in: Dashboards with an AI Dashboard Assistant	Mammoth covers preparation and dashboards in one product
Connectors	Tableau Prep's connector list	Mammoth's connector list	Compare both lists against your sources
AI features	Check current Tableau documentation	Extract Text, Bulk Replace, Generative AI, AI SQL Query	See the transform reference
Deployment	Desktop application	Cloud, in the browser
Dashboard sharing	Tableau Server or Cloud	Publish & Share with Authenticated or Public access	Public means anyone with the link can view

Data preparation workflow comparison

Tableau Prep workflow:

1. Connect to a data source
2. Visual flow designer with drag-drop steps
3. Data cleaning through calculated fields
4. Union, join, pivot operations
5. Export to file or publish to Tableau Server

Mammoth workflow:

1. Connect through a connector, or upload a file
2. Profile the data with Explore Cards
3. Describe a transformation in the AI Prompt bar (for example, "Remove duplicates and standardize names"); it generates pipeline tasks you can review
4. Fine-tune each task in its function panel
5. Export to BI tools or databases, or create a dashboard from the View
6. Schedule refreshes with an automation

AI-powered transformations compared

Extract Text with natural language:

• User types: "Give me the first word that begins with a number"
• System generates: Regex pattern automatically
• Tableau Prep: Requires manual calculated field creation with regex knowledge

Bulk Replace with AI grouping:

• Groups many unique values in a single operation
• Three grouping algorithms: Smart, Spelling Match, Common Patterns
• Tableau Prep: Manual find-replace only

Generative AI:

• Writes a new column row by row from a prompt and the context columns you pick (for example, sentiment or categories)
• Uses your own AI provider key, with limits on output length and rows processed
• Review the Preview before applying, because output can vary between runs
• Tableau Prep: Requires custom calculated fields

Dashboard creation in Mammoth versus the Tableau ecosystem

Tableau Prep handles data preparation only, so dashboards need Tableau Desktop, and sharing needs Tableau Server or Tableau Cloud. Mammoth builds and shares dashboards in the same product.

Tableau ecosystem requirements

Current pain points:

• Tableau Prep does data preparation only
• Still need Tableau Desktop for dashboard creation
• Separate training for dashboard authoring
• Separate licensing for sharing with stakeholders

Mammoth integrated workflow

Workflow:

• Data preparation and AI-assisted dashboard creation in one product
• Create a dashboard from a View: pick the View, describe what you want, and refine it in the Dashboard Assistant editor
• No separate dashboard tool to learn

Sharing (Publish & Share, with an Access Type):

• Public: Anyone with the link can view, including all of the dashboard's data
• Authenticated: Viewers sign in to Mammoth

Tip: Prototype first
Mammoth suits rapid dashboard prototyping before you commit to full Tableau development, and users who would otherwise wait on IT.

Migration pathway: Step-by-step plan

Assessment phase

Current state analysis:

1. Flow inventory: List current Tableau Prep flows and their business purposes
2. User assessment: How many users actively create flows vs consume outputs?
3. Dashboard requirements: Frequency and complexity of dashboard creation needs
4. Integration map: Data sources, export destinations, sharing requirements

Readiness questions:

• How much time does your team spend in Tableau Prep each month?
• How many dashboard requests wait in IT queues?
• Which Tableau Prep flows could benefit from AI-powered automation?
• How often do you need external stakeholder dashboard sharing?

Migration strategy selection

Option A: Complement strategy (suits teams with an existing Tableau investment)

• Timeline: Run both tools in parallel while you validate
• Risk: Low (Tableau expertise preserved)
• Benefits: Faster dashboard creation for more users
• Best for: Teams with significant Tableau training investment

Option B: Replacement strategy (suits cost reduction)

• Timeline: Depends on how many flows you recreate
• Risk: Medium (requires workflow recreation)
• Benefits: One vendor for preparation and dashboards
• Best for: Teams frustrated with technical complexity

Technical migration steps

Phase 1: Parallel operation

1. Environment setup:

   • Create Mammoth workspace and projects
   • Connect your existing data sources
   • Set up user accounts and permissions

2. Proof of concept:

   • Replicate 3-5 representative Tableau Prep flows in Mammoth
   • Compare output quality, processing time, and user experience
   • Validate AI-generated transformations against manual flows

3. User training:

   • Mammoth interface orientation
   • Dashboard creation workshop
   • Best practices for AI transformation prompts

Phase 2: Workflow translation

1. Flow recreation:

   • Document Tableau Prep transformation logic
   • Implement using the AI Prompt bar where possible
   • Use each step's function panel for precise control where needed
   • Set up Automations for scheduled workflows

2. Dashboard development:

   • Identify dashboard requirements from Tableau Desktop backlog
   • Create dashboards with the Dashboard Assistant
   • Configure sharing modes for different stakeholder groups
   • Test external sharing workflows

3. Integration testing:

   • Validate data quality matches Tableau Prep outputs
   • Test refresh schedules
   • Verify export destinations work correctly

Phase 3: Production deployment

1. Cutover planning:

   • Schedule production workload migration
   • Communicate changes to stakeholders
   • Establish support procedures

2. Go-live support:

   • Monitor initial production runs
   • Address user questions and workflow optimization
   • Measure performance against your baseline

3. Optimization:

   • Fine-tune pipelines
   • Expand user access based on success
   • Plan Tableau Prep license reduction (if replacing)

Success criteria

Measure migration success by:

• Time to create equivalent workflows: Compare against your Tableau Prep baseline
• Self-service: Number of users building their own pipelines
• Dashboard turnaround: Time from request to published dashboard
• User satisfaction: Survey your team
• Cost: Total cost of ownership for both approaches

Next steps after reading this guide

Immediate actions

1. Assessment: Inventory current Tableau Prep flows and dashboard creation bottlenecks
2. Strategy decision: Use the decision framework above to choose complement vs replacement
3. Proof of concept: Recreate one representative flow in Mammoth with your own data
4. Cost analysis: Calculate total cost of ownership for both approaches

Suggested sequence

• Start with the complement strategy, which needs the least change
• Focus on the highest-impact dashboard use cases first
• Expand access based on early results
• Consider the replacement strategy after team adoption

Start with your biggest pain point. If dashboard creation is the bottleneck, begin with dashboards. If data preparation complexity frustrates business users, start with the AI Prompt bar.

Tip: Expand in stages
Prove the approach on one workflow, then expand scope. Do not try to replace everything at once.