> AI agents: this is one page from Mammoth Analytics documentation. The index of all pages as Markdown is https://docs.mammoth.io/llms.txt. Append `.md` to any docs URL, or send `Accept: text/markdown`, to get Markdown.

# Getting started as a data analyst

> A 30-day plan for data analysts moving from manual Excel workflows to automated Mammoth pipelines, with the five core transformations to learn first.

This guide is for data and business analysts who spend most of their day in Excel and want to replace repetitive copy-paste work with automated pipelines, without coding. It is a 30-day plan: start with Day 1 below, then move to Week 1 and Month 1.

The guide covers your first 30 days with Mammoth, from uploading your first file to replacing manual workflows with automated pipelines.

## The 30-day learning path

The plan has three milestones:

**Day 1** → First transformation and first complete pipeline\
**Week 1** → Replace a manual workflow\
**Month 1** → Multiple automated workflows and a shared dashboard

## Day 1: Build your first pipeline

Start by automating one repetitive task. Pick a file you work with regularly.

### Upload your Excel or CSV file

**Prerequisite**: A Mammoth account and an Excel or CSV file.

Drag and drop your Excel file or CSV into Mammoth. The data opens in a spreadsheet-like interface.

### Explore columns with Explore Cards

Click the **Explore** icon on a column to open its Explore Card. These interactive visualizations show you:

- **Text columns**: Bar charts displaying the most common values
- **Numeric columns**: Distribution charts revealing patterns and outliers
- **Date columns**: Timeline views showing data spread

![View with Explore Cards for unit_price, product_name, and order_date above the data grid, filtered to unit_price 200 to 300](https://docs.mammoth.io/api/v1/images/20260408_153452_Explore_card.jpg)

Explore Cards show data quality issues, patterns, and distributions without formulas.

Double-click any value in an Explore Card to filter your data. These quick filters help you investigate issues and can be converted into permanent pipeline tasks later.

### Remove test rows with a Conditional Filter

The most common starting point is removing unwanted rows. This example excludes test data:

1. Go to **Describe your transformation** section in the toolbar
2. Select **Filter, Label & Replace → Conditional Filter**
3. Choose your column (like "Status")
4. Select "does not contain"
5. Type "test"
6. Click **Apply**

**Expected result**: The filter appears as a task in the Pipeline panel and runs on every future data refresh.

**Your Excel process:**\
Open file → Apply filter → Copy visible cells → Paste to new sheet → Save as new file

**Mammoth process:**\
Open View → Add Conditional Filter → Done

The Mammoth pipeline saves this logic, so next month's data uses the same pipeline.

## Week 1: Replace a manual workflow

Choose the workflow that matches your situation.

### Option A: Monthly report preparation (most common)

**Current state**: Monthly downloading, cleaning, combining, calculating, and formatting of data for reports.

**Mammoth solution**: One-time pipeline setup, then run and export each month.

**Workflow steps:**

1. **Connect Your Data**\
   Upload your Excel file or connect directly to your database with a Live Connection. With a Live Connection, a Dataset Refresh automation can pull new data on a schedule, so you don't download files manually.

2. **Build Your Pipeline**\
   Add the transformations you typically do manually:

   - **Conditional Filter**: Remove test accounts or old data
   - **Bulk Replace**: Standardize company names (Mammoth suggests groups of similar values)
   - **Convert Column Type**: Fix dates and numbers that imported as text
   - **Math Functions**: Calculate totals, percentages, growth rates
   - **Group & Pivot**: Summarize by category or time period

3. **Save as View**\
   Your pipeline automatically saves. Every transformation runs the same way every time.

4. **Set Up Automation**\
   Create a **Dataset Refresh** automation and choose your schedule. The pipeline runs automatically on every refresh.

### Option B: Daily sales dashboard

**Current state**: Daily extracting from CRM, cleaning, pivoting, and emailing results.

**Mammoth solution**: Automated refresh with a live dashboard, so stakeholders see current data without waiting for your email.

### Option C: Weekly metrics email

**Current state**: Weekly consolidating sources, calculating KPIs, and formatting for stakeholders.

**Mammoth solution**: A scheduled Messaging automation emails the view to stakeholders.

## Essential transformations for business users

Start with these five transformations to automate most manual workflows.

### 1. Conditional Filter: remove unwanted data

**Common uses:**

- Filter to current year only: `Order_Date is later than 2024-01-01`
- Remove test accounts: `Email does NOT contain @test.com`
- Focus on specific regions: `Region is East OR West`
- Exclude cancelled orders: `Status is NOT Cancelled`

### 2. Bulk Replace: standardize messy data

**Common uses:**

- Company names with variations: "iPhone 15 Pro", "iphone 15 pro", "iPhone15Pro" → all become "iPhone 15 Pro"
- Product categories: many variations → a short list of standard categories
- Department names: "HR", "Human Resources", "HR Dept" → "Human Resources"

When you use Bulk Replace, Mammoth groups similar values and suggests standardization. You review and approve.

### 3. Join: combine data from multiple sources

**Common uses:**

- Add customer names to order IDs: No more VLOOKUP formulas
- Enrich sales data with product details from master list
- Lookup pricing information for quotes

![Join panel in a View showing two Views side by side, with key column pickers and Left, Right, Inner, and Outer join types](https://docs.mammoth.io/api/v1/images/20260408_153540_Lookup.jpg)

![Excel VLOOKUP with #N/A errors above the equivalent Mammoth Join panel, which matches rows by chosen key columns](https://docs.mammoth.io/api/v1/images/20260408_153610_Side-by-sidecomparisonshowing.jpg)

### 4. Math Functions: calculations

**Common uses:**

- Calculate profit margins: `(Revenue - Cost) / Revenue`
- Year-over-year growth: `(This_Year - Last_Year) / Last_Year`
- Running totals for cumulative analysis
- Percentage calculations for reporting

### 5. Group & Pivot: summarize data

**Common uses:**

- Sales by region showing geographic performance
- Monthly totals for trending analysis
- Category summaries for executive reports
- Top 10 customers for account management

## View Insights and View Data Quality

Business analysts often need to answer one question before building anything:\
**“Can I trust this data, and what should I fix first?”**

**View Insights** and **View Data Quality** answer this without manual checks or formulas.

### View Insights: business-level observations

**View Insights** automatically surfaces meaningful observations that help business analysts understand how the data behaves and where analysis may be blocked.

Typical observations include:

- Inconsistent naming that fragments analysis (for example, the same product appearing under multiple formats)

- Sparse or uneven date coverage that prevents trend analysis

- Customer purchase patterns indicating low repeat behavior

- Pricing spreads that suggest review or standardization

- Signals of missing or incomplete tracking

These insights allow analysts to **quickly assess readiness**, identify risks, and decide which transformations are required—before investing time in pipeline design.

---

### View Data Quality: formal validation

**View Data Quality** provides a structured evaluation of dataset health using standard data quality dimensions.

The assessment covers:

- **Completeness**: Identification of missing values

- **Validity**: Verification of format and rule compliance

- **Uniqueness**: Detection of duplicate identifiers

- **Consistency**: Identification of value mismatches and variations

- **Accuracy**: Flagging values outside expected ranges

- **Timeliness**: Measurement of data freshness

Each view receives:

- An overall **Data Quality Score**

- Clearly categorized **critical issues and warnings**

- **Recommended actions** to improve quality

This enables business analysts to validate data **upfront**, rather than discovering issues after dashboards or reports are built.

## AI features for business users

Mammoth's AI features create transformations from a plain-language description, so you don't write syntax or code.

### AI Prompt (natural language transformations)

Instead of clicking through menus, just type what you want:

**Type:** "Remove orders from test accounts"\
**AI creates:** Conditional Filter excluding test emails

**Type:** "Standardize company names"\
**AI creates:** Bulk Replace with smart grouping

**Type:** "Calculate profit margin percentage"\
**AI creates:** Math Function with correct formula

### Create a dashboard with AI

**Scenario:** You need an executive dashboard showing sales performance.

**AI approach:** Describe the dashboard you want, such as "Create sales dashboard showing revenue by region", and Mammoth generates dashboard options from your data.

## Month 1 milestones

Track your progress with this 30-day checklist:

### Week 1 achievements

- [ ] First dataset uploaded or connected
- [ ] First pipeline created (3+ transformations)
- [ ] First manual workflow automated
- [ ] Improved data quality

### Week 2 progress

- [ ] Second workflow automated
- [ ] First Join transformation (combining sources)
- [ ] First scheduled automation setup
- [ ] Shared first View with colleague

### Week 3 expansion

- [ ] First AI Prompt used successfully
- [ ] First dashboard created
- [ ] Third workflow automated

### Week 4 mastery

- [ ] Advanced pipeline (7+ tasks)
- [ ] Multi-source consolidation
- [ ] Dashboard shared with stakeholders
- [ ] Training colleague on first workflow

## Common analyst scenarios and solutions

### Scenario 1: "I have 12 Excel files to consolidate monthly"

**Old way:** Open each file, copy data, paste into master, delete headers, fix formatting, save.

**Mammoth way:** Upload files to same Dataset as Batches, View automatically combines them, run the pipeline, and export or create a dashboard.

### Scenario 2: "My VLOOKUP keeps breaking"

**Old way:** VLOOKUP formula across sheets breaks when columns move, #N/A errors everywhere, long debugging sessions.

**Mammoth way:** Join transformation with a visual interface that matches rows by the columns you choose.

### Scenario 3: "I need to send the same report weekly"

**Old way:** Download new data, run through manual steps, format for executives, email manually.

**Mammoth way (automated):** Schedule dataset refresh, pipeline runs automatically, dashboard updates, automated email notification sent.

### Scenario 4: "Data quality issues waste my time"

**Old way:** Discover issues after analysis started, backtrack to fix, rebuild analysis.

**Mammoth way:** Use **View Insights** to surface potential issues early and **View Data Quality** to formally validate data before building. Problems are identified upfront, recommended actions are provided, and fixes can be applied once and automated through pipelines.

## Where to get help

**In-app resources:**

- Hover tooltips on features
- AI Prompt generates transformations from descriptions
- The **Help (book icon)** in the Function Panel opens the documentation for that transformation

**Documentation:**

- Getting started guides, like this one
- Transformation function reference
- Common workflow examples

**Support:**

- Use the Support option in the sidebar to ask questions, report issues, or share feedback

## Next steps after month 1

Once your first workflows are automated, here's how to continue growing:

### Months 2 to 3: Intermediate skills

- Advanced Join patterns (multiple sources, complex conditions)
- Complex conditional logic with AND/OR operators
- Custom aggregations and calculations
- Dashboard interactivity and filters

### Months 4 to 6: Advanced capabilities

- API integrations for real-time data
- Advanced automation, such as running Dataset Refreshes in a sequence
- Performance optimization for large datasets

### Becoming a team resource

As you gain expertise, you can:

- Train colleagues on workflows you've automated
- Document best practices for your team

## Next step

Follow the [Quick start](https://docs.mammoth.io/learn/getting-started/quick-start) to upload a file and build your first pipeline, or read [Core concepts](https://docs.mammoth.io/learn/getting-started/core-concepts).

---
Source: https://docs.mammoth.io/learn/getting-started/as-a-data-analyst.md · Updated: 2026-10-03