Warehouse native analytics

What is warehouse-native analytics?

Warehouse native analytics is an approach to data analysis where queries and insights are drawn directly from a data warehouse itself, eliminating the need for data extraction and transference into separate systems. 

This method enables faster, more accurate insights by allowing analytics to happen where the data resides, making the process seamless and efficient.

For organizations seeking to reduce latency, increase data accuracy, and improve overall efficiency, this approach offers significant advantages over traditional analytics workflows. 

Top 5 use cases for data teams include: 

1. Business outcomes = ROI: You can test and experiment on metrics/results that are actual business outcomes and also have them live in your warehouse.
2. Save ad hoc data analyst time spent on custom queries: You can explore specific cohorts more deeply and obtain statistical results at a more granular level. For example, if test results are significant for a specific cohort, maybehigh lifetime value customers or visitors from a specific geography. 
3. Run cross-channel experimentation with ease: You want to test against events, exposure data, and metrics from other digital channels that may not be delivered via your existing tool, but the data sits in your warehouse. For example, you could have email exposure data and associated metrics in your warehouse and want to use a Stats Engine like Optimizely to analyze the experiment. 
4. No angry calls from the compliance department: You could be a financial institution that doesn't want any of itsdata to leave the warehouse. With warehouse native analytics you can experiment without the data leaving your control. 
5. The same source of truth for all the results: You don't want any discrepancy between your experimentation product and the digital analytics data. 

How warehouse-native analytics works

Typically, analytics workflows require data to be moved across systems, often through an extract, transform, load (ETL) process before analysis can occur. Warehouse native analytics simplifies this by enabling direct analysis within the data warehouse where data is stored in a single, centralized environment.

Leveraging modern cloud data warehouses such as Snowflake, Databricks, BigQuery, and Redshift, teams can perform sophisticated analyses and data processing without needing complex architectures or additional platforms.

This modern data stack approach: 

• Eliminates complex ETL processes 
• Reduces data latency 
• Maintains data consistency in datasets 
• Leverages existing warehouse capabilities 
• Simplifies the analytics stack in your data platform 

The evolution of warehouse native analytics 

Driven by an increased demand for real-time, reliable insights, warehouse native analytics allows companies to rely on their data warehouse as an active tool for analytics and business intelligence. It helps in maintaining a unified data source and make data-driven decisions more quickly and with higher accuracy. 

Before starting your warehouse-native analytics journey, assess your current state by considering: 

• How does your analytics architecture affect business agility? 
• What data silos exist in your organization? 
• How many product analytics tools does your team juggle? 
• What's your time-to-insight for data-driven decisions? 
• What's the total cost of maintaining multiple analytics platforms? 

Here’s what your implementation journey could look like:  

Step 1: Audit your current analytics landscape including tools, data sources, and workflows. Document pain points and bottlenecks to identify where warehouse-native analytics can provide the most value. 

Step 2: Select your cloud data warehouse platform and design a unified data model that supports your analytics needs. Define clear success metrics and create a phased migration strategy. 

Step 3: Configure your warehouse infrastructure and begin migrating data sources in order of priority. Set up analytics tools, user access, and governance frameworks to ensure proper data usage. 

Step 4: Monitor system performance and costs, continuously refine data models, and scale resources based on usage patterns. Regular reviews ensure optimal operation as your needs evolve. 

Benefits of warehouse native analytics 

1. Usable insights: Warehouse native analytics enables teams to access data instantly for optimization, making it ideal for environments that require rapid decision-making and experimentation. This real-time capability allows forimmediate feedback and adjustments, especially valuable for organizations managing complex campaigns or customer experiences. 
2. Improved data governance and accuracy: Analytics within the data warehouse minimizes the risk of inconsistencies and data silos, fostering a single source of truth across the organization. This streamlined approach aligns departments on the same data, reducing discrepancies and ensuring consistency in reporting. 
3. Scalability and flexibility: As businesses grow, their data engineering needs become more complex. Warehouse native analytics can scale alongside growing data volumes without the need for additional tools or architecture. This flexibility enables organizations to remain agile and meet evolving data demands, especially important for leaders driving data-informed strategy and execution across various departments. 
4. Cost and operational efficiency: By limiting the need for data duplication, transformation, or storage in multiple systems, warehouse native analytics can reduce infrastructure and maintenance costs on overall pricing. This approach simplifies the technical overhead and works great if you manage extensive data or require frequent insights. 

The future of warehouse native product analytics 

As companies increasingly turn to data-driven approaches, warehouse native analytics is becoming essential. By eliminating data silos and enabling direct analysis within the data warehouse, companies can make faster, more informed decisions while maintaining data governance and reducing operational complexity. 

When combined with experimentation capabilities, a warehouse-native analytics solution becomes even more powerful.You can go quickly from insights to action, run experiments, and measure results within your data warehouse. With warehouse-native experimentation you can: 

• Run A/B tests using your consolidated customer data 
• Make better decisions based on experiment results 
• Scale experimentation across products and features 
• Measure the true impact of changes through controlled tests 

As the ecosystem continues to mature, organizations that adopt warehouse-native analytics will be well-positioned to compete in an increasingly data-driven business environment.