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# Migrating from Tableau Prep to Mammoth

> Compare Tableau Prep and Mammoth, choose a complement or replace strategy, and follow a phased plan to rebuild flows as pipelines and dashboards.

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.

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Source: https://docs.mammoth.io/migration/from-tableau-prep.md · Updated: 2026-10-13