Augmented analytics tools use AI and machine learning to automate data preparation, generate insights automatically, and let non-technical users query data in plain English.
These platforms reduce the 80-90% of time typically spent on data prep, helping teams focus on actual analysis and decision-making.
If you’ve ever spent three weeks cleaning a dataset just to create one report, you’ll understand why these tools exist. Let’s dive into the best options that can actually save your sanity.
What Are Augmented Analytics Tools? (And Why You Need Them)
Remember the last time you opened a CSV file and immediately wanted to cry? That’s where augmented analytics comes in.
These platforms use AI to handle the soul-crushing parts of data work:
- Cleaning messy data without writing a single formula
- Answering questions by typing “show me Q3 sales by region”
- Finding patterns you’d never spot manually
- Creating charts that actually make sense
- Predicting trends without a statistics degree
The game-changer? You don’t need to know Python, SQL, or any other intimidating acronyms. Just ask your question and get answers.
The 13 Best Augmented Analytics Tools
Tool | Best For | Learning Curve | Pricing | Standout Feature |
---|---|---|---|---|
Mammoth | Non-technical teams | 15 minutes | $19/month | 94% reduction in manual work |
ThoughtSpot | Large enterprises | Moderate | Enterprise | Google-like search for data |
Power BI | Microsoft shops | Easy | $10-20/month | Seamless Office integration |
Domo | Custom data apps | Moderate | $83+/month | Interactive data applications |
Tableau | Beautiful visualizations | Steep | $70-125/month | Best-in-class charts |
Oracle Analytics | Global enterprises | Steep | Custom | 28-language support |
Qlik Sense | Exploratory analysis | Moderate | $30+/month | Associative data model |
Sisense | Mid-market companies | Moderate | Custom | Single-stack solution |
AnswerRocket | Conversational analytics | Easy | Enterprise | Voice recognition |
Pyramid Analytics | Detailed explanations | Moderate | Custom | AI explains the “why” |
Tellius | Analytics + ML | Moderate | Custom | AutoML capabilities |
Yellowfin | Mixed skill teams | Easy | Custom | Guided queries |
DataRobot | Data science teams | Steep | Enterprise | ML model automation |
Pricing is per user per month unless noted. “Custom” means contact sales (usually expensive).
1. Mammoth
We built Mammoth after watching too many smart people waste weeks on data prep instead of getting insights. The platform automates 80% of data cleaning and transformation with AI that actually works.
Here’s what makes it different: you can be productive in 15 minutes, not 15 weeks.
The AI suggests fixes for common data issues (like those annoying duplicate entries), groups similar values automatically, and creates reusable workflows that run on autopilot.
“We now have real-time insights at our fingertips, and decision-making has never been easier.” – Starbucks
Best for: Teams who want powerful automation without the complexity
Pricing: $190/year per user (try it free for 7 days)
Why it works: 94% reduction in manual work, 1400% ROI improvement
Real talk: most augmented analytics tools require months of training. Mammoth gets you results on day one because it’s built for business people, not data scientists.
2. ThoughtSpot
ThoughtSpot feels like having Google for your company data. Type a question, get an instant chart. Their SpotIQ feature automatically analyzes billions of rows to find insights you’d miss.
Best for: Large companies with massive datasets
Pricing: Enterprise-only (expensive)
The catch: Learning curve is steeper than they claim
3. Microsoft Power BI
If your company lives in Microsoft 365, Power BI makes sense. The new Copilot feature helps build reports automatically, and everything integrates with Excel and Teams.
Best for: Microsoft shops
Pricing: $10-20/user/month
The reality: Great for basic stuff, but complex analyses still require expertise
4. Domo
Domo lets you build actual data applications, not just dashboards. Their AI cleans data and identifies trends, but the real value is creating interactive tools your team will actually use.
Best for: Companies wanting custom data apps
Pricing: $83+/user/month
The trade-off: Powerful but expensive for smaller teams
5. Tableau
The visualization king added AI features like natural language queries. If you need stunning charts that impress executives, Tableau delivers.
Best for: Organizations prioritizing beautiful visualizations
Pricing: $70-125/user/month
The downside: Still requires significant training for advanced features
6. Oracle Analytics Cloud
Oracle supports natural language queries in 28 languages and handles enterprise-scale complexity. Built for companies with global operations and complex compliance needs.
Best for: Massive enterprises with international requirements
Pricing: Custom (read: very expensive)
The reality: Overkill for most teams
7. Qlik Sense
Qlik’s associative model automatically finds connections between data points you’d never think to explore. It’s like having a curious analyst who never sleeps.
Best for: Teams needing flexible, exploratory analysis
Pricing: $30+/user/month
The challenge: Takes time to understand the associative approach
8. Sisense
Sisense handles both data preparation and visualization in one platform. Their AI automates the boring stuff so you can focus on finding insights.
Best for: Mid-sized companies with diverse data sources
Pricing: Custom
The appeal: Single platform eliminates tool juggling
9. AnswerRocket
Built specifically for conversational analytics. Ask questions out loud or type them naturally—AnswerRocket understands context and provides detailed explanations.
Best for: Teams who prefer talking to their data
Pricing: Enterprise-focused
The bonus: Voice recognition works surprisingly well
10. Pyramid Analytics
Pyramid’s AI explains why your metrics changed, not just what happened. Their “explain” feature breaks down the drivers behind every data point.
Best for: Teams needing detailed explanations for changes
Pricing: Custom
The value: Answers the “why” behind every number
11. Tellius
Combines automated machine learning with natural language search. Build predictive models without coding, then query results in plain English.
Best for: Teams wanting both analytics and ML capabilities
Pricing: Custom
The strength: AutoML that doesn’t require a PhD
12. Yellowfin
Offers guided natural language queries with contextual help. Great for teams with mixed technical abilities—beginners get guidance, experts get flexibility.
Best for: Organizations with varied skill levels
Pricing: Custom
The benefit: Adaptive interface grows with user expertise
13. DataRobot
Primarily an AutoML platform, but includes augmented analytics for building and deploying machine learning models automatically.
Best for: Data science teams wanting automation
Pricing: Enterprise-level
The focus: Machine learning first, analytics second
How to Actually Choose (Without Getting Overwhelmed)
Start with your biggest pain point:
Drowning in data prep? Mammoth or Alteryx handle the grunt work.
Need gorgeous charts for board meetings? Tableau wins on aesthetics.
Want everything to work with Microsoft? Power BI is the obvious choice.
Prefer talking to your data? ThoughtSpot and AnswerRocket excel here.
Then consider the practical stuff:
- Can your team actually learn it in a reasonable time?
- What’s the real total cost (training, support, implementation)?
- Does it play nice with your existing systems?
- Will people actually use it daily?
Why Simple Usually Wins
Here’s what we’ve learned from customers like Starbucks and Bacardi: the fanciest tool isn’t always the best tool.
Starbucks was drowning in data from 17 countries, taking 20 days to generate basic reports. After switching to automated workflows, they achieved 1400% ROI improvement and 53% reduction in maintenance work.
“What once took weeks is now done in hours—it’s a game changer for us.” – Bacardi
The secret? They chose data automation that actually worked instead of enterprise software that required a dedicated team to manage.
Bacardi saved 40 hours per month on manual data consolidation. Everest Detection’s research team could finally focus on cancer detection instead of fixing Excel files.
The pattern? Simple tools that solve real problems beat complex platforms every time.
Getting Started Without Going Crazy
Week 1: Pick one painful data task and automate it
Week 2: Train your team on that one feature
Week 3: Expand to the next most annoying process
Month 2: Start building more sophisticated analyses
Don’t try to revolutionize everything on day one. That’s how augmented analytics projects fail.
The Bottom Line
The augmented analytics market is exploding because everyone’s tired of spending 80% of their time on data prep and 20% on actual insights.
The right tool can transform your team’s productivity—we’ve seen 94% reductions in manual work and 1400% ROI improvements. But only if people actually use it.
Whether you go with an enterprise platform or something focused like Mammoth’s no-code automation, the key is finding something that works for your team’s actual workflow.
Ready to stop drowning in spreadsheets? Try Mammoth free for 7 days. You’ll have clean, analysis-ready data in 15 minutes, not 15 hours. No contracts, no complicated setup, no regrets.