Unify Your Analytics Ecosystem¶
After establishing a data warehouse connection to execute SQLs, Numbers Station integrates the various analytical resources, communication channels, and operational tools into a single environment. This eliminates data silos and consolidates the entire analytics landscape—dashboards, data transformations, semantic layers, documentation, presentation slides, email archives, messaging channels, and even relevant external data sources—into one coherent platform.
Aggregate Existing Dashboards and Metrics¶
Numbers Station indexes metrics, descriptions, and historical analyses directly from your existing dashboards. This indexing process includes extracting dashboard schemas, metric definitions, and related attributes. By mapping these details back to the Knowledge Layer, Numbers Station maintains a referenceable inventory of organizational metrics. When users pose questions, the platform can leverage this repository to surface relevant data points, insights, or visualizations from past analyses. This minimizes the need to recreate analyses and improves the discoverability of institutional knowledge.
Incorporate Data Transformation Scripts and Semantic Layers¶
Beyond dashboards, the system ingests data transformation scripts (e.g., SQL, Python), existing semantic layers (e.g., LookML, dbt models), and related documentation. These artifacts provide insight into how raw data is processed, joined, or enriched. By understanding transformations, Numbers Station can reference intermediate computations, clarify how metrics are derived, and maintain lineage information. Incorporating semantic layers aligns the platform's knowledge representation with established business logic, ensuring consistent and traceable definitions of key metrics and dimensions.
Index Documentation, Slides, and External References¶
Numbers Station also ingests relevant documentation—technical specs, data dictionaries, FAQs—as well as presentation slides that show how insights have been communicated historically. In addition, it can integrate web-based sources of industry benchmarks or best practices through API connections. This allows users to augment their queries with context that exists outside traditional data warehouses. The system uses natural language processing to map key terms and concepts from these documents back to the Knowledge Layer, ensuring that all forms of organizational knowledge are searchable and contextually linked.
Connect Communication Channels (Slack, Teams, Email)¶
By integrating directly with messaging platforms (Slack, Teams) and email systems, Numbers Station extends its reach into everyday workflows. When a user asks a data-related question in Slack, for example, the platform can respond in-channel by retrieving insights from previously indexed data, dashboards, or documentation. Historical conversations become part of the knowledge graph, allowing the system to reference past Q&A sessions, decisions, or analyses to provide richer responses. This integration reduces context switching, ensuring that the discussion, exploration, and consumption of analytics results occur where teams naturally collaborate.
Enable Direct Actions and Extensions through Agent Integrations¶
Numbers Station supports agent extensions—custom workflows that trigger downstream actions directly within the platform. For instance, a user can request automatically generated presentation slides based on the results of a query. The system can pull in charts, metrics, and narratives and assemble them into a slide deck, reducing manual effort and ensuring data accuracy. Additionally, by integrating with APIs defined via OpenAPI/Swagger specifications, Numbers Station can fetch external benchmarks, pull reference tables from web services, or trigger downstream processes. This action capability means users can progress from initial query to final deliverable or operational task in one location.
Summary¶
By centralizing dashboards, data transformations, semantic layers, documentation, collaboration channels, and action endpoints, Numbers Station constructs a tightly integrated knowledge network. This unified ecosystem streamlines data discovery, makes reuse of prior analyses straightforward, and allows teams to generate insights, respond to questions, and produce deliverables without navigating multiple, disjointed tools. It converts previously isolated artifacts into a connected system that supports continuous, context-rich, and action-oriented analytics workflows.