The BI Promise vs Reality

When comparing text-to-SQL vs BI tools, the promise was the same: democratized data access. Tableau, Power BI, Looker, Metabase. Beautiful dashboards. Drag-and-drop interfaces. “Anyone can analyze data.”

Reality: The same small group still controls data access. They just build dashboards instead of writing SQL directly. This is why we built LILA.

Everyone else? Still waiting.

How Traditional BI Works

Business has questions
Analyst builds dashboard
Dashboard answers those specific questions
New question arises
Back to step 2

The dashboard answers the questions it was designed to answer. Nothing more.

“Show me revenue by region” - if someone built that chart, you’re covered.

“Show me revenue by region for customers who ordered more than twice in Q3” - sorry, that’s not on the dashboard.

How Text-to-SQL Works

With LILA, the workflow is simple:

You have a question
You type the question in plain English
AI generates SQL
You get the answer

No pre-built dashboard required. No waiting for someone to anticipate your question. Any question about data in your database is answerable.

The Fundamental Difference

BI tools are anticipatory. Someone predicts what you’ll want to know and builds it in advance.

Text-to-SQL is responsive. You ask whatever you need, whenever you need it.

Both have value. But for ad-hoc questions - the ones nobody anticipated - only one approach works.

Where Traditional BI Wins

Let’s be fair. BI tools excel at:

Recurring reports. Weekly sales dashboards that run automatically. Monthly executive summaries. Consistent format, automatic updates.

Data exploration by analysts. Professionals who understand data modeling and want to build complex visualizations.

Sharing pre-defined insights. Distributing the same view to hundreds of people who all need the same information.

Complex calculated metrics. DAX formulas, calculated fields, custom measures that require careful construction.

If you need the same report every week, BI tools deliver it reliably.

Where Text-to-SQL Wins

Natural language queries excel at:

Ad-hoc questions. “How many orders came from California last Tuesday?” Nobody built a dashboard for that.

Non-technical users. Marketing, sales, support - people who have questions but don’t build dashboards.

Rapid exploration. Follow-up questions without waiting. “What about Texas?” “What about this week?”

Time-sensitive answers. Questions that arise in meetings. Decisions that can’t wait for dashboard development.

Unpredictable queries. The questions you’ll think of tomorrow that nobody can predict today.

The Real Comparison

Scenario	BI Tools	Text-to-SQL
Weekly sales report	Better	Works
”How many orders from Dubai?”	If built	Instant
Complex YoY analysis	Better	Good
Question during a meeting	Impossible	Instant
Explore new hypothesis	Slow	Fast
Share with 100 people	Better	Works
Ad-hoc customer support	Can’t	Easy

The Implementation Gap

BI tool implementations take weeks or months:

Install and configure platform
Connect data sources
Model the data properly
Build initial dashboards
Train users
Iterate on dashboard design

LILA implementation takes hours:

Connect database
Add business context
Embed widget
Start asking questions

For enterprises with BI teams and budgets, the long BI implementation is fine. For everyone else, it’s a barrier. Text-to-SQL vs BI comes down to speed and accessibility.

Cost Comparison

Tableau Server: $35-70/user/month, plus servers, plus analysts to build dashboards.

Power BI Pro: $10/user/month, but effectiveness depends on someone building content.

Text-to-SQL: Typically $25-150/month total, not per-user. Effectiveness immediate.

For small teams, traditional BI often costs more than the value delivered. Text-to-SQL inverts this - low cost, high accessibility.

The Hybrid Approach

Smart organizations use both:

BI dashboards for standardized, recurring reports
LILA for ad-hoc questions and non-technical users who wait days for reports

They’re not competitors. They’re complementary tools for different use cases.

The question isn’t “which should I use?” It’s “which questions does each answer?”

When to Choose Text-to-SQL

Choose natural language queries if:

You don’t have dedicated BI analysts
Users have diverse, unpredictable questions
Speed of implementation matters
Budget is limited
Questions come up in real-time

When to Keep Traditional BI

Keep your BI tools if:

You already have significant dashboard investment
You need complex, polished visualizations
Same reports serve many people
Analysts are available to maintain dashboards

The Bottom Line

Traditional BI answers the questions someone predicted.

Text-to-SQL answers the question you have right now.

Both are valid. But if you’re waiting days for reports because nobody built the right dashboard, you might need the second option.

Ready for questions without waiting? Try LILA free