Data export

When organizations export data to maintain compliance, they need to understand exactly what they’re exporting and why. Many organizations export analytics data as a checkbox exercise without a clear use case, creating security risks and storage costs for data they’ll never actually use.

Data export refers to transferring data collected through analytics platforms to external systems for further analysis, reporting, or storage. This capability is important for businesses seeking to leverage their analytics data beyond the confines of the platform itself, particularly in regulated industries with specific data retention, audit, or analysis requirements.

Types of data exports

Companies typically use two types of data exports, each serving different purposes:

Raw data export involves transferring data in its most granular form – individual user interactions, complete with all captured attributes and timestamps. This allows analysts to access detailed information about user interactions, enabling in-depth analysis and customized reporting beyond what’s available in the analytics platform. For instance, Piwik PRO Analytics Suite allows you to export raw data to platforms like BigQuery, facilitating advanced data processing, machine learning applications, and integration with other data sources.

Aggregated data export provides summarized information, which is easier to manage and interpret. It’s helpful for high-level overviews and executive reporting but lacks the detail necessary for deep analysis, debugging specific user issues, or conducting forensic investigations into data quality problems. Many analytics platforms offer this export type as a standard feature, allowing users to download reports in formats like CSV for easy sharing and visualization.

When organizations need data export

For compliance and audit requirements: Healthcare organizations subject to HIPAA may need to export and retain analytics data for specific periods as part of their audit trail. Financial services firms may need to export data to demonstrate compliance with regulations around customer interactions and marketing practices.

For advanced analysis: If your analytics platform doesn’t support the specific calculations, segmentation, or machine learning models you need, exporting raw data to specialized tools may be necessary. This is common for organizations building predictive models, conducting survival analysis, or performing statistical tests not available in standard analytics interfaces.

For system integration: Combining analytics data with CRM data, product data, financial data, or other sources often requires exporting analytics data into a data warehouse or lake where these sources can be joined and analyzed together.

For long-term storage: Most analytics platforms have retention limits (e.g., 36 months, 25 months). If you need historical data for trend analysis, year-over-year comparisons, or regulatory requirements, you’ll need to export and store data independently.

Common mistakes

The biggest mistake is exporting everything “just in case” without a specific use case. This creates several problems:

Storage costs accumulate: Raw data volumes can be massive, particularly for high-traffic sites. Storing years of granular data in cloud warehouses like BigQuery can cost thousands of dollars monthly, even if you never query it.

Security surface area expands: Every copy of data is another potential breach point. If you’ve exported sensitive analytics data to multiple systems, you’ve multiplied your attack surface and compliance obligations.

Data becomes stale and undocumented: Exported data from three years ago may lack documentation about what the fields meant, how data collection was configured, or what changed between then and now. It becomes increasingly difficult to use over time without proper context.

Best practices

Start with the use case, not the capability. Don’t export data just because you can. Define specifically what analysis or requirement necessitates export, then export only what serves that purpose.

Document everything. When you export data, document what was exported, when, for what purpose, and what the fields mean. Future analysts (or regulators) will need this context.

Implement access controls. Exported data should have at least as strict access controls as the source analytics platform, if not stricter given that raw data exports often contain more detailed information.

Consider automated versus manual export. Automated exports keep data synchronized but create ongoing storage costs and maintenance obligations. Manual exports for specific projects limit costs but require more planning.