Modern data-driven organizations are synergizing operations observability, business intelligence, and data science with digital business observability programs to break down data silos, increase productivity, and drive innovation. Digital business observability combines IT and business data with cutting-edge data science techniques to enable deeper analysis and unlock valuable insights that propel innovation across use cases from sales and marketing to product design and financial operations. It is a data initiative blending three existing programs: operations observability, business intelligence, and data science, integrating telemetry data from IT systems with relational data from BI systems to break down silos and enable new analytics use cases. Digital business observability is distinct from monitoring, which involves tracking predefined system health metrics or KPIs in real time, whereas digital business observability involves collecting a wider range of telemetry data about an IT system's internal state and behavior, aggregating that data alongside business data, and enabling advanced analytics software to explore the data. Digital business observability can create alignment between IT and the business by breaking down data silos with analytics platforms, creating a significant business impact. It powers business observability programs through multimodal analytics - the ability to seamlessly apply all three analytical modes to IT and business data: search, query, and model. This capability unlocks powerful new use cases and massive value potential, enabling both business and IT users to leverage data in new ways to make smarter decisions and drive value. Successful digital business observability initiatives depend on people, processes, and technology, including assembling a cross-functional team, defining business use cases, researching new architectural approaches, and meeting essential requirements such as scalability, processing efficiency, and multimodal analytics.