June 2018 Summaries

Heap aims to revolutionize data analytics by addressing common problems in current analytics tools, such as complex datasets, lack of trust in data, and difficulty in navigating datasets. By introducing automatic data capture and data virtualization, Heap seeks to provide a complete and easily interpretable dataset that allows for flexible and retroactive analysis through Virtual Events, which are defined within the analytics tool rather than being hardcoded in the product's codebase. This approach enables organizations to iterate on their event schemas quickly, enhancing the speed and depth of analysis. Additionally, Heap's model of user identity allows for more accurate and comprehensive user tracking across different stages and devices, overcoming limitations of traditional analytics tools. By integrating data from various third-party sources and offering a unified view of user behavior, Heap's analytics platform aims to make data-driven decision-making more accessible and effective. The company has invested years into building a scalable, cost-effective data virtualization system using advanced distributed systems technology, positioning itself at the forefront of the evolving landscape of analytics.

Heap's CTO, Dan Robinson, was interviewed on the Data Engineering Podcast about the company's data infrastructure and the role of data virtualization in improving data organization and cleaning processes. The podcast, titled “User Analytics In Depth At Heap with Dan Robinson,” explores how Heap automatically captures user behavior data and discusses the evolution of its architecture. Additionally, Robinson and Heap's COO, Ravi Parikh, have written about the advantages of data virtualization, with Parikh's whitepaper, "Why Data Virtualization is an Analytics Game Changer," providing insights into how data virtualization is transforming analytics and addressing key challenges faced by data teams.

Google Analytics, a widely used web analytics tool for over two decades, requires extensive manual event tracking, which can be burdensome due to its need for constant updates. Google Tag Manager (GTM) was introduced to alleviate this by allowing non-developers to manage event tags, although it still demands upfront planning and organization to prevent data loss. In contrast, Heap offers a more dynamic approach by automatically recording all user interactions and enabling event definitions on top of its collected raw data, allowing for retroactive analysis and immediate insights without waiting for new data accumulation. Heap also supports data from iOS, Android, and numerous third-party integrations, making it versatile compared to GTM. The choice between Google Tag Manager and Heap depends on an organization's specific needs, resources, and objectives, with a comparison available on G2 Crowd for customer insights on both tools.