June 2021 Summaries

We've released an Airflow Provider that helps deliver fresh data directly to your apps through trigger syncing, reducing manual sync schedules and triggers. The provider also includes a status endpoint and an Airflow Sensor to further orchestrate data pipelines. This move aims to support data orchestration workflows in Census, regardless of the framework used, such as Airflow, Dagster, or Prefect.

Census has achieved SOC 2 Type 2 compliance, a high standard for customer data security and confidentiality, ensuring that its operational analytics platform delivers the same level of security as business application endpoints. This achievement demonstrates Census' commitment to designing and operating secure data infrastructure from the ground up. With SOC 2 compliance, customers can trust that their data is securely managed throughout the entire data stack, from the data warehouse to third-party applications like Salesforce or HubSpot. The company's security-by-design approach and regular testing measures further ensure the protection of customer data, providing a single source of truth for managing customer interactions and operations across various business applications.

To run a successful business today, having good analytics is not only beneficial but almost instrumental. SQL fluency is crucial for unlocking advanced analytics and deeper insights. Window functions are essential for this purpose, as they allow for calculations that don't group the result set, unlike aggregate functions. The basic anatomy of a window function includes an aggregate function, PARTITION BY, and ORDER BY. There are five essential windows functions for business operations: ROW_NUMBER(), SUM(), AVG(), LEAD()/LAG(), and DENSE_RANK(). These functions can be used to get first or last instance via ROW_NUMBER(), calculate cumulative sums via SUM(), compute moving averages via AVG(), compare values via LEAD() and LAG(), and rank data via DENSE_RANK(). By mastering these window functions, you can unlock advanced analytics and gain deeper insights into your business operations. `` Note: I've summarized the key points of the text in a single paragraph, focusing on the importance of SQL fluency, window functions, and their applications in business operations.

Building a modern data stack doesn't require a lot of up-front effort, but starting small is crucial to achieving modularity and avoiding stressful workflow disruptions. The modern data stack's strength lies in its modularity, where each tool serves a distinct purpose, mutually exclusive to the other tools that support it. This leads to simplicity, better ROI, and easier implementation. Starting with one small optimization allows you to build a data infrastructure that's efficient and customized to your business's exact needs. The journey to a modern data stack starts with a single step toward the modern data stack, focusing on simplifying specific processes first, such as getting CS reps the data they need faster or tracking how your data team uses data. By doing so, you can build a series of quick wins that help prove to executives that your vision for how your organization uses data has merit, ultimately leading to a more efficient and effective modern data stack.

The modern data stack is likened to the human nervous system, with the brain acting as the central hub for decision-making, receiving signals from receptor cells (data), processing and transforming them into a single source of truth, and sending them out to the limbs (teams) for action. The data warehouse and modeling tools act as the brain, while reverse ETL tools represent the spinal cord, enabling the company to sync clean, unified data back into the body via pre-built API integrations. Behavioral data collection and ETL tools serve as the peripheral nervous system, gathering sensory data from teams and tools at the edge and relaying it to the brain for further analysis. The sales, marketing, and support teams represent the limbs of the company, interacting with the world and moving the business forward. The combination of these layers enables the company to process and learn from large amounts of data, making it possible to make better decisions in real-time.

You're currently using Pipedrive as a sales CRM, but you're missing valuable sales data that's not being utilized effectively due to its non-standard format. To address this, you can leverage the Pipedrive API to create custom fields, allowing you to better organize and utilize your sales data. By connecting to the Pipedrive API using Python and utilizing libraries like requests, you can make HTTP requests to retrieve existing fields, create new custom fields, and write data to these fields. This enables you to improve your sales process functionality and integrate with other tools more efficiently. However, implementing this process in production requires careful consideration of rate limits, logging, and retry mechanisms to ensure seamless integration.

The author, Plushcap, reflects on the lessons learned from the first Operational Analytics Conference hosted by Census, which brought together data pioneers and leading practitioners to share their experiences and insights. The conference was held on Clubhouse, a new event platform that posed challenges for community engagement and audience measurement. Despite these challenges, the conference was a success, with attendees sharing valuable lessons on topics such as category creation, hiring for data teams, and the weaponization of data. The author highlights the importance of experimentation and finding creative ways to extend the reach of the conference content beyond the platform's limitations.