If you've ever asked your analytics tool a simple question only to get back confusing results, or worse, an error, you know the frustration of working with unprepared data. The truth is, great analytics doesn't start with fancy algorithms or powerful LLMs. It starts with clean, well-structured data.

At Scavenger, we've seen firsthand how properly prepared data sources unlock dramatically better analytics. When your data is BI-ready, you spend less time debugging queries and more time extracting insights. Here's your practical checklist to get there.

Why This Matters

Think of your data source as the foundation of a building. You can have the most sophisticated analytics tools in the world, but if the foundation is shaky (inconsistent formats, broken relationships, cryptic column names), everything built on top will be unstable. Conversely, a well-prepared data source makes every query faster, every insight clearer, and every analysis more reliable.

The Checklist

1. Standardization & Encoding

Start with the basics. Set a consistent encoding standard like UTF-8 across your data. This prevents those frustrating character corruption issues that can derail an analysis.

Next, standardize your data types. Integers should be integers, not strings that look like numbers. Floats should have consistent decimal precision. And crucially, make your units consistent: don't mix currencies, blend metric and imperial measurements, or switch between percentages and decimals within the same column.

For dates and times, always prefer ISO 8601 format (YYYY-MM-DD). It's unambiguous, sortable, and universally recognized. Your future self will thank you.

2. Database Integrity

This is where many databases fall apart. Ensure every table has proper primary keys that are truly unique. Set up foreign keys that actually reference valid records in other tables. Nothing breaks analytics faster than orphaned foreign keys pointing to non-existent data.

Take time to remove redundancies. Duplicate tables or fields aren't just inefficient; they create confusion about which data source is the "source of truth." Consolidate where it makes sense, and after any major restoration or migration, rebuild your indexes. This isn't just about performance; it's about ensuring your data relationships remain intact.

3. Data Modeling & Schema Design

Here's where good databases become great ones. Design for a star schema: central fact tables that record events (sales, transactions, interactions) surrounded by dimension tables that provide business context (customers, products, dates, locations).

But the real magic is in the details. Use descriptive, self-explanatory names. Instead of "col_4," call it "team_name." Instead of "fk_1," use "customer_id." Clear naming makes your database intuitive for both humans and AI tools.

Document everything. Write down what each table represents, what each column means, and how they relate to each other. Include the "why" behind your design decisions. This documentation becomes invaluable as your team grows or when you're training new analytics tools.

In your dimension tables, try to include more descriptive attributes, even if they seem slightly redundant. These rich descriptions dramatically improve LLM-analytics quality because they give your queries more context to work with.

4. Latency Optimization

Nobody likes waiting for slow queries. Set up indexes (or composite indexes) for your most frequent and slowest queries. The performance gain is usually worth the small storage overhead.

Here's a pro tip: define querying rules in natural language that help avoid large, complex joins. And sometimes, it makes sense to denormalize slightly by adding calculated columns from other tables. Yes, this introduces some redundancy, but if it eliminates expensive joins in 80% of your queries, it's worth it.

5. Data Quality Checks

Garbage in, garbage out. Before your data ever reaches an analytics tool, run comprehensive quality checks:

Missing values: Decide your strategy upfront. Will you impute, remove, or flag them?

Plausibility: Check for outliers, validate against known domains, and ensure logical consistency.

Duplicates: Find and eliminate them systematically.

Pre-processing: Extract numeric values from free text fields. For example, if you have ages buried in description fields, create a dedicated age column.

Validate your database against internal data sources regularly. Inconsistencies between systems are a common source of confusion and mistrust in analytics.

6. Vectorization

For data sources with rich textual content (product descriptions, customer reviews, support tickets), enable vectorization in Scavenger. This allows Scavenger to intelligently cluster and search your data based on semantic meaning, not just exact keyword matches.

Select which columns should be vectorized thoughtfully. Typically, these are textual or categorical features where the content carries significant meaning beyond just being a label.

7. Prompt Engineering for Your Schema

This is where traditional database design meets modern AI-powered analytics. Document a comprehensive data dictionary within your analytics tool within Scavenger. For each table and column, check the automatically generated descriptions, how it should be used, valid value ranges, and business logic that might not be obvious.

Set up examples of how fields should be interpreted, especially for columns with non-obvious business rules. And crucially, specify how tables should be joined. This single step can drastically improve both the accuracy and performance of natural language queries.

8. Testing

Finally, test everything with real-world scenarios. Run queries in natural language that mirror how your team actually thinks about the data. Verify that your data source is accessible, that joins work as expected, and that aggregations return sensible results.

Simulate common BI scenarios in Scavenger or your preferred tool. Check not just that the results are correct, but that they're returned quickly. If a simple query takes 30 seconds, something in your schema or indexing needs attention.

A Real Example: The Star Schema