I was recently asked why it’s not advisable to query raw data directly. The question caught me off guard at first, but it made me think deeply. Raw data is often viewed as the foundation of analytics, yet querying it without proper processing can lead to inefficiencies, inconsistencies, and potential misinterpretations. In this post, I’ll attempt to explain why querying raw data should generally be avoided, when it’s necessary to do so, and best practices for managing data access.
Why You Shouldn’t Query Raw Data Directly
- Raw Data Is Often Incomplete and Unstructured: Raw data is typically generated by transactional systems, APIs, logs, or external sources with little consideration for analysis. As a result, it often lacks proper formatting, naming conventions, and relationships between datasets.
- Example: A CRM system logs customer interactions across multiple tables, but timestamps might be in inconsistent formats, and user IDs may differ between sources. Querying this data directly can lead to incomplete or misleading reports.
- Better Approach: Use a transformation layer to clean, standardize, and structure the data before making it available for analysis.
- Performance Issues and Query Inefficiencies: Raw data tables are often large, unindexed, and not optimized for analytical queries (GA4 raw data is notorious for these reasons). Running complex joins or aggregations on raw data can slow down performance, affecting other users and systems.
- Example: Running a revenue analysis on millions of raw transaction records without aggregating sales at the customer or product level can result in sluggish queries and unnecessary computational overhead.
- Better Approach: Use aggregated tables or materialized views to precompute common metrics, improving both query speed and efficiency.
- Lack of Business Logic and Standardization: Raw data doesn’t include business logic. When different teams query raw tables, they may apply their own calculations, resulting in varying definitions of key metrics (e.g., revenue, churn rate) and inconsistent reporting.
- Example: One team defines “active users” based on logins within 30 days, while another defines it based on transactions. This can lead to discrepancies in reporting across teams.
- Better Approach: Establish centralized data models that encode business logic and ensure consistent metric definitions across teams.
- Data Governance and Security Risks: Raw data often contains sensitive information that may not be governed appropriately. Querying it without applying the necessary security measures and access controls can lead to compliance risks and unauthorized exposure of data.
- Example: A user queries raw customer data and inadvertently accesses personally identifiable information (PII) that should be restricted.
- Better Approach: Implement role-based access control (RBAC) and ensure that only governed, anonymized datasets are available for general analysis. Apply masking to the raw data values.
- Poor Data Quality and Increased Risk of Errors: Raw data may include duplicates, missing values, or incorrect entries, which can lead to inaccurate analysis. Without proper validation, analysts might unknowingly use flawed data.
- Example: A sales dataset includes multiple entries for the same transaction due to a system error. Querying the raw data leads to inflated revenue numbers.
- Better Approach: Apply data validation and quality checks in a structured ETL (Extract, Transform, Load) process before making data available for reporting.
- Lack of Historical Tracking and Time-Stamped Changes: Raw data often represents the most recent state of a system, without tracking historical changes. Querying it directly makes it difficult to conduct trend analysis or compare data year-over-year.
- Example: A subscription service updates user plan changes in place, meaning historical subscription levels are lost. An analyst querying raw data wouldn’t be able to track plan migrations over time.
- Better Approach: Implement slowly changing dimensions (SCDs) or snapshot tables to maintain historical records.
When Is It Appropriate to Query Raw Data?
While structured, processed data should be your primary source for analysis, there are scenarios where accessing raw data is necessary. Here are a few instances where querying raw data is appropriate:
- Data Exploration and Discovery: Raw data queries are useful for understanding dataset structures, completeness, and anomalies before integrating data into a model.
- Example: A data engineer explores raw tables to check for column formats, missing values, and inconsistencies before onboarding a new external data source.
- Best Practice: Use sandbox environments or read-only access to explore raw data without affecting production systems.
- Debugging Data Issues: When discrepancies appear in reports, raw data can help identify errors, missing records, or inconsistencies introduced during data transformation.
- Example: An analyst notices a sudden drop in reported revenue and queries raw transaction logs to determine if records were mistakenly excluded due to an ETL error.
- Best Practice: Use raw data queries for diagnostic purposes only, and document findings to improve future data quality processes.
- One-Time Deep Dives for Ad Hoc Analysis: Sometimes structured tables don’t exist for a specific analysis, and querying raw data may be necessary to extract insights or validate assumptions.
- Example: A business leader wants to analyze customer behavior for a newly launched product, and no predefined tables exist for the required metrics.
- Best Practice: If raw data queries become repetitive, request the creation of structured tables or views for better efficiency.
- Data Science and Machine Learning Use Cases: Data scientists often need access to raw data for advanced modeling, feature engineering, or training machine learning algorithms. Pre-aggregated tables may strip away the details needed for building predictive models.
- Example: A data scientist working on fraud detection queries raw transaction logs to identify anomalies, including timestamps and metadata.
- Best Practice: Store cleaned and labeled versions of raw data in a dedicated data science environment to ensure consistency.
- Audit and Compliance Investigations: Regulatory or security audits may require raw data access to verify report accuracy or investigate historical records.
- Example: A financial institution undergoing an audit queries raw transaction logs to confirm compliance with reporting standards.
- Best Practice: Maintain immutable logs and enforce access controls to prevent tampering while ensuring auditability.
- Real-Time Data Needs Before ETL Processing Completes: When immediate access to the latest data is required before processing and modeling, querying raw data may be necessary.
- Example: A fraud prevention team queries raw transaction data in real time before the nightly ETL process completes to detect anomalies.
- Best Practice: Where possible, implement real-time streaming analytics and incremental updates to reduce reliance on raw data queries.
Final Thoughts: Balancing Flexibility and Governance
Raw data can be incredibly valuable, but it shouldn’t be the default source for analytics. Structured, processed datasets ensure data integrity, consistency, and efficiency. However, in certain cases—such as troubleshooting, data science modeling, and real-time analysis—querying raw data is necessary.
Organizations should aim to:
- Prioritize structured, cleaned data for analysis.
- Use raw data queries selectively for exploration, debugging, and specialized use cases.
- Implement governance and security controls to protect sensitive data.
- Foster self-service analytics while maintaining consistency in reporting.
By striking the right balance between flexibility and governance, organizations can maximize both data integrity and innovation.