April 2022 Summaries

The company has made several exciting announcements in April, including using Looker Looks as models and new integrations with Google Analytics and Postgres. They also launched a product update that enables syncing business logic from Census to downstream tools like Looker and Google Analytics. Additionally, they have improved their connectors with enhancements for Zendesk, Mailchimp, Shopify, S3 authentication alternatives, and Braze. The company is also hiring and invites users to join them on Slack in the OA Club or reach out through email to provide feedback.

dbt is a data transformation tool that leverages SQL and Jinja to write modular data models within a data warehouse, allowing users to create reusable code and easily maintain their data environment. It is not a new language to learn, but rather builds upon existing languages like SQL and Jinja. dbt is also free and open-source, making it a cost-effective solution for data transformation. The tool provides features such as modular models, clear documentation, and the ability to visualize lineage using directed acyclic graphs (DAGs). By building a dbt model, users can improve their data processes by allowing them to write reusable code and document their data models thoroughly. With its extensive documentation and community support, dbt is an excellent tool for data analysts and analytics engineers looking to streamline their data workflows.

As you move your data from Snowflake to Pipedrive, you can take advantage of the hub-and-spoke philosophy to improve scalability, maintainability, and business outcomes. By operationalizing your analytics, you can enhance sales pipeline management with rich customer data, personalize email campaigns, create continuity across connectors and teams, and gain observability into your organization's data investment. To achieve this, you can export data from Snowflake using various methods, including unloading into a stage and downloading locally, unloading to an external location, or downloading from a Snowflake Worksheet. Once you have your data ready, you can import it into Pipedrive using CSV files and map the fields accordingly. Additionally, Census offers a reverse ETL solution to automate the data sync process, providing a maintenance-free and secure way to integrate Snowflake with other tools and platforms.

There's a pervasive sort of magical thinking that data analysts are "true neutral" scientists, presenting universal truths with no particular agenda. However, everyone has an agenda, and analysts pursue theirs through numbers. The effectiveness of the argument is judged by whether or not people are convinced, rather than just looking for precision in measurements and algorithms. Finding the right answer is not always right, as business outcomes suffer when analysis becomes an end in itself. A good analyst helps make decisions confidently and convinces executives to take action. Embracing being wrong and presenting a clear recommendation can be beneficial, even if it's not the "right" choice, as it shows leadership a cheap option with low downside. Ultimately, data analysis is about driving action, not just finding truth.

Traditional data teams are often criticized for being mere "curators" who pull out metrics without using their full potential. Former CTO Erik Bernhardsson of Better and Spotify's music recommendations feature thinks that data teams should be used to investigate and explore which metrics would make the biggest impact on a company's goals, rather than just following instructions from executives. To build a truly data-driven company, leaders need to identify quality metrics that can measurably impact their business, and give data analysts more freedom to model questions before trying to answer them. The right reporting structure is key, with some companies adopting an embedded model where data teams report to a centralized head of data but perform day-to-day tasks in assigned business units. Successful data teams are built on creative people who care about the business outcome, rather than just tool-oriented engineers. By loosening the reins and giving data analysts more autonomy, businesses can tap into their full potential and drive real business success.

Snowflake is a fully-managed software as a service (SaaS) that combines data warehousing, data lakes, and other data sources for the secure sharing and consumption of real-time/collaborative data in a single platform. It allows users to export data from Snowflake into various locations such as internal stages, external locations like AWS S3, or even download it directly from a Snowflake Worksheet. On the other hand, NetSuite is an all-in-one cloud business software suite that automates core business processes and provides real-time visibility into the efficiency of these processes. To import data into NetSuite, users can use the Import Assistant, which allows them to scan and upload CSV files, confirm their import options, conduct file and field mapping, and save their mapping before starting their import. However, due to lack of native support for Snowflake in NetSuite, manual methods are required to sync data between the two platforms, such as using Census reverse ETL or other non-native methods.

Integrating metrics across marketing tools often feels like a duct-taped solution, with each platform managing a different stage of the customer journey resulting in disjointed experiences and pain internally. However, Census's integration with Google Analytics enables companies to unify and sync metrics across all their marketing tools using data warehouse as the source of truth. This allows for evaluation of marketing channel effectiveness, attribution of user properties to revenue, and iteration faster and running big experiments by enriching website visitors with data context in the warehouse. The integration also benefits from Census's warehouse-centric approach, enabling consistent experiment analysis and evaluation across all tools, and improves ROI using all the data in the warehouse.

Snowsight is a quick and intuitive second-generation web interface for Snowflake data that can be used by all accounts running on AWS, Azure, or Google Cloud. It allows users to create, modify, and manage users, databases, and database objects, share data with other Snowflake accounts, and navigate the Snowflake Data Marketplace in one streamlined interface. Unlike traditional business intelligence tools, Snowsight uses fast querying, SQL auto-completion, automatic stats, and various charts for data visualization. It enables users to complete ad-hoc data analysis on massive datasets in a jiffy. The tutorial demonstrates how to get started with Snowsight by creating a worksheet, running a query, analyzing the results, and summarizing the results with automatically generated contextual statistics. Snowsight is designed to help users become data-driven by providing an intuitive interface for exploring and visualizing their data.

Census has announced that it now supports syncing data to Postgres, a popular database for developers and data professionals. This new integration enables companies to use Census as a destination for their data warehouse, allowing them to drive more relevant product interactions by syncing modeled data back into application databases. With Census + Postgres, operations and development teams can automate internal and external workflows, such as enriching customer profiles, influencing products, and sharing data with clients. This integration opens up new use cases for companies, including the ability to push data warehouse insights directly into their applications, reducing barriers of entry for analytics and automating workflows.

The future is now for businesses as machine learning and predictive modeling take center stage in operations. Businesses should conduct time series forecasting to prepare for future events, allocating resources efficiently, staying agile and adaptive, and optimizing their team's time. Time series forecasting describes predictions made with historical data, providing valuable insights into trends and patterns. The most fundamental application is simple moving averages (SMAs), which can provide long-term trend forecasts using exponential smoothing. SMAs are useful for getting a clear picture of a metric's general trend but not for predicting exact future values. By applying time series forecasting techniques, businesses can make informed decisions, reduce errors, and increase profits.

The data field is often lonely, with professionals on the business-side lacking understanding of what the data team does and faces challenges. However, community has played a crucial role in connecting people to build better data systems. Junior data professionals need to connect with more senior colleagues to develop essential skills, which can only be gained by interacting with others. Community gives data professionals a place to commiserate, collaborate, and advance technical skills. Relationships are crucial to career advancement in the data industry, and building connections outside of work is key to success. A sense of belonging and frame of reference regarding career progression are essential for data practitioners to grow in their profession.

Looker, a data analytics platform by Google Cloud, enables companies to collaborate on BI dashboards and explore their organizational data through its modeling language LookML. By integrating with Census, users can take business logic out of dashboards and sync directly into downstream apps, driving action. This integration allows operations teams to create and edit reports called Looks, which can be reused across various business tools without requiring extensive data team assistance. With Census + Looker, business teams can self-serve with data in the warehouse, powering operational workflows such as hyper-personalizing sales outreach and marketing email campaigns, segmenting marketing audiences, and inspiring action rather than reaction. The integration also streamlines workflow efficiency by sending data directly to ops tools, reducing manual work and increasing productivity.

The software development landscape has undergone significant changes over the years, with software now constantly evolving and updating in a live environment. This shift has carried everything else along, including data teams, which are now working in a live environment, collecting and storing vast amounts of data that require curation and analysis to make informed decisions. The ability to collect and store data has increased exponentially, but the complexity of managing this information has also grown, leading to challenges such as identifying what data is important, when it's used, and how it's used. As a result, companies need to integrate data-driven decision-making, being selective about which data is analyzed and how that analysis impacts business intelligence and ultimately, business decisions. The integration of data observability and visualization will play a crucial role in addressing these challenges.

Sync Logs' from Census is a feature that writes detailed logs of each data point synced, enabling audit, troubleshooting, and alert creation with granular information. This centralizes all data and business logic in the user's data warehouse, providing native integration with their data warehouse for seamless visibility into data pipelines. With Sync Logs, users can achieve full visibility, real-time monitoring, and granular alerting to proactively troubleshoot and solve problems faster. The feature is available on Platform plans only and offers a range of benefits, including troubleshooting API rejection reasons, rolling back data to a previous state, and notifying the right teams of data issues through the Census Slack integration.