Advanced Analytics Dashboard

Analytics Metric Register organizes metric definitions and their sources for a business decision. The skill supplies a metric register and planning workflow. It starts with the decision the dashboard should support, then selects one metric per relevant part before considering layout.

The intake distinguishes a requested build, an existing process to convert, a review, or an advice question. It asks one unresolved question at a time and stops when further answers would not change the output. Decision context includes who decides and how often. Data context includes sources, approximate row count, and required freshness. Measures include the desired metrics, count, and comparison basis. The current dashboard's manual or tool-based process and whether it is trusted shape the recommendation.

The recommended sequence defines the metric, identifies source data, records its calculation, establishes refresh, and brings it into decision review. This targets metric sprawl before visual design. A recommendation is the default response; artifacts are built when requested and after essential context is resolved.

Use it for an analytics register, KPI-dashboard template, business metrics, or a dashboard whose measures need focus. A request can begin with:

We have a dashboard with 30 tiles that nobody uses.

The template alone produces no predictions or verified insights. Forecasts require supplied data, a specified model, evaluation evidence, and uncertainty. The skill does not connect to source systems, clean data, guarantee metric accuracy, or calculate KPIs. Automation, reminders, and synchronization belong to an integration layer.

The input is confirmed business context and the requested deliverable: a metric set, a layout, or both. Register fields include Metric, Category, Source Module, Formula or Method, Period, Value, Target, Trend, Owner, Last Updated, and Metric ID. Values and targets are numeric, update dates use a date type, and descriptive context remains text. Category options distinguish General, Operations, Finance, People, and Compliance. The SQL representation adds creation and update timestamps.

Requested CSV, SQL DDL, JSON Schema, Notion mapping, or workbook outputs derive from the same field reference so labels and types remain aligned. Templates are empty by default. Illustrative rows are clearly synthetic and do not contain real employee or customer data. Excel-compatible CSV uses UTF-8 with a byte order mark; a workbook is produced only when explicitly requested. Numeric and date formatting requirements are identified after CSV delivery.

Notion rendering goes through notion-manual-import for the CSV, property mapping, import steps, and verification. Manual mappings need no account connection. A live workspace write requires verified tool access and an authorized target. Imported metric IDs remain text, with number, date, and select properties configured after import. Delivered mappings are distinguished from a completed live workspace change.