Missed our recent webinar? Here’s what you need to know.
If you missed the live session, here’s your chance to catch up. In this webinar, we went through a series of challenges, real-world demos, and platform capabilities that showed how teams today can move from scattered data to actionable insight — faster, without code, and without waiting on IT.
Here’s a section-by-section breakdown of the full session, in the order it was presented, with direct insights and examples from each video.
1. Welcome to the Mammoth Webinar
“We’ve shifted more towards the product side — and that shift was shaped by solving real-world data problems.”
In this brief opening, CEO Gaurav welcomes viewers and introduces the session. He shares that Mammoth began as a hybrid service–product company and has since shifted fully toward product. The focus? Solving complex data challenges — and doing it with clarity, speed, and transparency.
What to expect:
- A quick intro to Mammoth’s evolution
- What the webinar will cover (slides + demo)
- Why this isn’t “just another data tool” session
2. Product Overview: One Platform, Built for the Entire Data Journey
“The goal is to empower the right people — and those people often aren’t technical.”
This section sets the foundation for what Mammoth actually is. It’s not just another ETL tool or dashboard layer — it’s an end-to-end platform that covers the entire data workflow, from warehousing to cleaning to export. The focus? Reducing fragmentation and shortening the feedback loop between questions and answers.
Most of the time in analytics is lost to data prep. Mammoth changes that by giving business users the tools to explore, transform, and connect their data — without needing SQL or IT support.
What’s covered:
- Why traditional data stacks are fragmented
- How Mammoth reduces reliance on technical teams
- The compounding value of shorter feedback loops
3. Hands-On Demo: Exploration + Cleaning
“Let’s take a building management dataset and clean it — no code, no SQL.”
This live demo showed how users can:
- Explore datasets via point-and-click
- Clean messy data (e.g., inconsistent labels, typos)
- Use AI-powered bulk replace and auto-suggestions to standardize values
- Create automated pipelines that run on incoming data
Outcome: A clean, ready-to-analyze dataset with transformation steps fully visible and reusable.
4. How IT Wins with Visibility & Governance
This section covered IT-friendly features like:
- Automated Datas Pipelines
- Audit logs of all changes
- Role-based access control
The key point: Mammoth sits alongside IT infrastructure, enables decentralized data ops, and frees up engineers to focus on higher-value projects.
5. AI-Driven Functions Without the Mystery
“We don’t want a black box. We want visibility into how data transforms — even with AI involved.”
This session focused on AI capabilities built directly into Mammoth:
- Use GenAI to generate new fields (e.g., city/state from phone numbers)
- Bulk-clean or categorize using pattern detection
- Consolidate columns using natural language prompts that auto-generate SQL
6. Joins & Lookups, Simplified
“We did a left join across two tables with a few clicks — that’s it.”
Joins, lookups, and column-level transformations are common pain points in tools like Excel or Power Query. This segment showed:
- How to unify disparate tables using a clean visual interface
- Regex-powered transformations
- Automation
7. Key Benefits: Speed, Transparency, and Scale
“Mammoth lets analysts own their workflows. That’s powerful.”
This wrap-up highlighted the core benefits organizations report after switching to Mammoth:
- 10x faster reporting cycles
- 94% fewer manual touchpoints
- 1B+ rows processed per month
- Data transformations become visible, repeatable, and scalable across departments
A client story from Starbucks illustrated how Mammoth handled fragmented retail data from 17 countries and turned a 4-week reporting cycle into 4 hours.
Q&A: Will AI Replace Platforms Like Mammoth?
“You care how insights are produced. That’s why black-box AI doesn’t cut it.”
In this question, we address a common assumption: can AI just do it all? The answer is no — not if you need transparency. Mammoth integrates AI features, but never hides what’s happening. You always see each transformation step, making the process auditable and repeatable — key for trust, quality, and scale.
Q&A: Can Mammoth Handle Large Datasets?
“We pick up where Excel stops — and go well beyond it.”
A viewer asked if Mammoth can manage enterprise-scale data. Yes — the platform easily handles millions to billions of rows. While it’s not a replacement for your data lake, it sits between raw data and dashboards, transforming massive volumes without sacrificing speed or control.
Q&A: How Independent from IT Can Mammoth Be?
“We’re not disrupting your systems. We’re enabling your departments.”
This segment tackles IT dependency. Mammoth supports a decentralized approach where business teams build and maintain workflows themselves. IT gets transparency and security — but not the maintenance burden. It’s a win-win model that complements your existing stack and speeds up delivery.
Q&A: Why Use Mammoth if You Already Have Power BI or Power Query?
“It’s not about charts — it’s about preparing the data for them.”
If you’re already using Power BI or Power Query, Mammoth becomes your prep layer. Unlike technical tools that require scripts or M code, Mammoth lets business teams clean, transform, and maintain data flows themselves — with full transparency and minimal friction.
Final Takeaway: A Platform That Matches the Pace of Business
Whether you’re buried in spreadsheets or waiting on the next batch job from IT, this webinar demonstrated a different way: a single, transparent layer that unifies your data, empowers your team, and scales with your needs.
Want to see Mammoth in action for your team?
