May 2021 Summaries

The modern data landscape presents a complex challenge, with increasing amounts of data requiring better governance, synchronization, and processing. To overcome this, companies need to break down silos between their data sources and storage, leveraging software tools to automate and streamline their data operations. Data orchestration is a key solution, enabling organizations to operationalize their data and improve business decisions through real-time insights. By automating data collection, preparation, transformation, unification, and delivery, data orchestration helps companies reduce manual workloads, improve data quality, and ensure compliance with regulations like GDPR and CCPA. As the modern data stack continues to evolve, tools like Census are emerging to support data orchestration, providing scalable solutions for companies looking to establish a strong foundation for their data operations.

At Census we believe that you shouldn't worry about scheduling your syncs. With today's technology, data and workflows should sync as close to realtime or "just in time" as possible. This is one of the reason we quickly gave you the ability to run a sync "continuously" and help you always have the latest version of your data available in all of your applications. We are happy to announce that you can now trigger a sync programmatically via an API call, allowing you to add Census to your data orchestration workflow with frameworks like Airflow, Dagster, Luigi, Argo, Astronomer, GCP Cloud composer, Prefect and dbt Cloud. This new feature enables automatic syncing of the latest version of your model in dbt Cloud, making it easier for teams to manage their data without worrying about sync scheduling. New integrations with Airflow and other popular frameworks are coming soon, and our team is happy to help you set up the trigger sync API with the frameworks you love.

The key takeaway from this text is that, in order to land and excel in a data career, one must develop strong people-focused skills, such as communication, management, and business acumen, alongside technical data skills. Data professionals need to demonstrate an understanding of how their work impacts the business and stakeholders, and be proactive in building tools and systems that meet those needs. To do so, they should actively participate in data communities, produce content, and showcase a broad perspective on data and its impact on the organization. Ultimately, future data leaders must stay up-to-date with industry trends and be able to think critically about patterns of problems and the systems needed to solve them.

If investing in a new data tool is an emotional rollercoaster, investing in an entirely new approach to data is a monumental task that requires careful consideration of emotions, vision, and discipline. Creating a category is messy and difficult, but it can be achieved by having a unique perspective that aligns with existing customer behavior, defining the vocabulary of your category with marketing, being disciplined about what you fix and when, just starting, and differentiating your product through data management. By following these principles, founders can increase their chances of success in creating a new category and making a lasting impact on the industry.

The company aims to enable more data integration, allowing users to operationalize their data across multiple platforms. They have partnered with Braze to announce a new connector that enables the direct import of unified product and customer data from data warehouses into Braze. This connection allows for various marketing campaign optimizations, such as hyper-targeted segments, personalized messaging, and automated email triggers, while respecting API rate limits.

Survival analysis is a statistical technique that helps analyze the expected duration of time until an event occurs, providing valuable insights into customer and product lifecycle, employee churn, machine failure, and campaign effectiveness. It can be applied to various industries, including real estate, mortgage, manufacturing, and more, to predict key metrics such as active user survival rate, product time to purchase, campaign effectiveness evaluation, employee churn estimation, and machine lifecycle measurement. The Kaplan-Meier method is a commonly used approach in survival analysis, which involves computing survival probabilities from observed events while making assumptions about participants who dropped out and the event's occurrence at specified times. Python provides the lifelines library for implementing survival analysis, allowing users to harness its value in improving customer insights and operationalizing data.