September 2020 Summaries

The hub-and-spoke method is superior to point-to-point data synchronization because it scales more easily, synchronizes data more accurately, is easier to maintain, more secure, and more affordable. The hub-and-spoke system allows for only one connection per app, reducing the number of connections required as the business grows, making it less prone to errors and inconsistencies. It also provides a central point of truth, ensuring accurate and consistent data across all apps. Additionally, the hub-and-spoke model is easier to maintain and clean up, reduces exposure to security breaches, and can be more cost-effective in the long run despite potentially higher upfront costs.

At Census, their goal is to help users make their product and customer data available across their company by turning it into a platform for unified customer data. Their Hubspot Connector is now live on Hubspot's Marketplace, allowing teams to build better operations with synced internal data. Users can leverage the integration in various ways, such as displaying product usage metrics, creating hyper-targeted lists, triggering workflows, and sending personalized messages. Census syncs data to Contact and Company Objects, with support for other objects coming soon, while respecting API rate limits and minimizing API usage. To get started, users can simply add Hubspot as a new service connection, create a new sync, and use the visual data mapping UI to pick what data they want to make available in Hubspot.

The business intelligence landscape has undergone significant changes over the past decade, driven by advances in technology and big data. The roles of Business Analysts and Business Intelligence Developers have evolved to accommodate this shift, with Data Scientists emerging as a new role that leverages Ph.D.-level statistical analysis to drive business decisions. The modern data stack has given rise to Analytics Engineers, who bridge the gap between Data Analysts and Data Scientists by maintaining flexible but clean data lakes and creating easy-to-process data pipelines. However, the lack of proper care for data lakes can lead to "data swamps," and without an intimate understanding of the data, incorrect metrics can be created, leading to a re-emergence of siloed IT departments. To overcome these challenges, organizations are adopting modern data stacks and leveraging tools such as dbt, Fivetran, and BetterException, which empower Analytics Engineers to do their job correctly.