Operational analytics is a type of analytics that informs day-to-day decisions with the goal of improving efficiency and effectiveness in an organization's operations. It drives action by automatically delivering real-time data to where it'll be most useful, no matter the location within the organization. Operational analytics powers important daily decisions, both big and small, by providing insights that are not just about understanding business operations but also about driving business operations. At its core, operational analytics is about putting an organization's data to work so everyone can make smart decisions about their business. It introduces a set of fundamentals for leveraging data across the organization, making it a key tool for companies looking to achieve data-driven decision-making at scale. Operational analytics differs from traditional analytics in that it uses data to drive business operations rather than just providing an understanding of what's going on in the business to inform strategic decisions over time. It answers questions like "Which support ticket should I tackle first?" and is used by teams such as customer success, sales, and marketing to make specific activities more efficient. The modern data stack that supports operational analytics consists of four sections: data integration, data storage, data modeling, and data activation, which work together to provide a hub-and-spoke model for data flow. This data stack can be set up with tools such as Fivetran, Snowflake, dbt, and Census, making it accessible to businesses of all sizes. Operational analytics is necessary for companies with more than a handful of customers, as it allows them to overcome limitations in individual tools and enables their data teams to take a more proactive role in how the business uses data. With operational analytics, companies can automate daily brain-draining tasks, enable marketing ops people to create hyper-specific drip campaigns, and empower CS reps to prioritize support tickets without relying on spreadsheets or traditional analytics. The "X factor" making all this possible is the data team, which can diagnose workflow problems and create solutions using operational analytics. To start implementing operational analytics, businesses should build a data stack blueprint starting with an ETL tool, a data warehouse, dbt, and Census, and begin small with one use case before rolling out to other teams.