What is treasure data?

Treasure Data is a cloud-based customer data platform (CDP) that allows companies to collect, unify, and analyze customer data across multiple channels. The platform integrates data from websites, mobile apps, IoT devices, and more to create a 360-degree customer profile.

What problems does Treasure Data solve?

Treasure Data helps solve several key data challenges:

• Data silos – Customer data often lives in disparate systems and channels like CRM, website, social media, etc. Treasure Data brings all that data together.
• Understanding customers – With data unified in one place, companies can better understand customer behaviors, preferences, and journeys across channels.
• Real-time analytics – Treasure Data enables real-time queries and analysis on incoming customer data streams.
• Activation – Insights from Treasure Data can be activated across the organization, from marketing to product teams.

Key capabilities and features

Here are some of the key features and capabilities provided by Treasure Data:

Data collection and ingestion

• Over 300 pre-built connectors and integrations to pull data from various sources like websites, CRM, mobile apps, cloud storage, etc.
• Flexible ingestion mechanisms like batch upload, streaming inserts, and more.
• Ability to handle large data volumes in the millions of records per day.
• Real-time data ingestion capabilities to capture customer behaviors as they happen.

Data processing and transformation

• Tools to clean, filter, reshape, enrich, and transform incoming raw data into analysis-ready forms.
• Ability to merge data from disparate sources into unified customer profiles.
• Flexible data processing options through SQL, JavaScript, Python, R, and more.
• Query functions to derive insights from processed data.

Analysis and reporting

• Drag-and-drop workflow designer to build reusable analysis recipes.
• Library of over 300 pre-built analytics recipes.
• SQL, JavaScript, and charting capabilities to analyze data.
• Customizable dashboards to share insights across the organization.
• Scheduled reports and alerting capabilities.

Activation and integration

• APIs to connect insights with downstream apps like CRM, marketing automation, support tools, etc.
• Pre-built integrations with systems like Salesforce, Marketo, and Slack.
• Web SDKs to activate data in digital properties.
• Options to export data to cloud storage or BI tools.

Data management

• Cloud-based storage optimized for analytics use cases.
• Data governance capabilities like encryption, access controls, and data retention policies.
• Compliance with regulations like GDPR, CCPA, etc.

Administration and security

• User and team management with configurable roles and permissions.
• Audit logging to track data and system access.
• SOC 2 Type 2 compliance for data security.
• High availability architecture across multiple AWS regions.

Treasure Data architecture

Treasure Data follows a cloud-based architecture hosted on Amazon Web Services (AWS). Here are the key components:

Ingestion layer

The ingestion layer brings data into Treasure Data from various sources via batch uploads, streaming inserts, integrations, etc. It can handle millions of records per day.

Storage layer

Incoming data lands in cloud-based object storage buckets optimized for analytics. Data is stored durably and encrypted.

Processing layer

This distributed processing framework transforms, enriches, and processes raw data into analysis-ready forms. It leverages technologies like Spark and Amazon EMR.

Serving layer

Processed datasets are loaded into a high-performance query engine to enable interactive SQL, charting, and analysis through the Treasure Data web UI.

Activation layer

APIs, integrations, and SDKs activate insights across other systems. Webhooks enable data to flow bidirectionally.

Management layer

Admins manage user access, data governance policies, connector configuration, and more through web UI and APIs.

Treasure Data use cases

Here are some common use cases and examples of how companies leverage Treasure Data:

360-degree customer view

Unify data from CRM, web, mobile, social, and offline sources to build comprehensive customer profiles. Get a complete view of behaviors and journeys across touchpoints.

Personalization

Use customer insights to deliver personalized experiences across channels like web, app, email, call center, and more. Respond to behaviors in real time.

Campaign analytics

Analyze the impact of marketing campaigns across channels. Track engagement, attrition, conversions, and ROI. Optimize campaigns dynamically.

Customer segmentation

Leverage data to group customers into cohorts for targeted campaigns. Define segments based on behaviors, attributes, value, predicted lifetime value, and more.

Product analytics

Understand product usage patterns. Identify popular features, workflows, and adoption funnels. Feed insights to product teams.

Predictive analytics

Build machine learning models on Treasure Data to predict behaviors like churn risk, lifetime value, next purchase, content recommendations, and more.

IoT and streaming data

Ingest and process high-velocity data streams from IoT devices, web/mobile apps, sensors, etc. Analyze streaming data flows in real time.

Benefits of Treasure Data

Adopting Treasure Data can provide the following key benefits:

• Accelerated time to insight – Go from data to insights within hours instead of weeks.
• Increased customer intelligence – Unprecedented visibility into customer behaviors and characteristics.
• Improved experience – Data-driven experiences engage customers and boost satisfaction.
• Informed decision making – Analyze data when and where needed to drive smart decisions.
• Enhanced innovation – Rapidly test ideas and iterate based on data-led feedback.
• Reduced costs – Consolidating customer data yields operational efficiencies.

Limitations of Treasure Data

Some potential limitations to consider:

• Can require significant data integration work for setup.
• Analysis capabilities do not match full enterprise BI platforms.
• Need dedicated personnel to manage over time.
• Relies heavily on cloud infrastructure dependencies.
• Not ideal for managing sensitive private data at high volumes.

Alternatives to Treasure Data

Some other customer data platform options beyond Treasure Data include:

• Segment – A CDP with code-free implementation and broad data pipeline capabilities.
• mParticle – Focused on collecting and routing customer data from apps and websites.
• ActionIQ – An enterprise-scale CDP offering machine learning capabilities.
• Adobe Real-Time CDP – Integrates with other Adobe marketing and analytics clouds.
• Tealium – Specializes in tag management, data integration, and analytics.

Choosing among CDPs depends on factors like use cases, data sources, implementation needs, analytics requirements, and budget.

Conclusion

Treasure Data provides a powerful cloud-based customer data platform to help companies unlock insights from fragmented customer data. By ingesting, unifying, and analyzing cross-channel data, Treasure Data helps brands improve customer experiences, marketing performance, and product development. With capabilities for batch, streaming, and real-time processing on customer event data, Treasure Data enables a wide range of analytics use cases. While it requires significant implementation investment, integrating Treasure Data can yield accelerated insights, enhanced personalization, and informed decision-making powered by customer intelligence. Leading global brands rely on Treasure Data as a core piece of their modern data stack.