docs: add Power BI result presentation section

.gitattributes

@@ -1,2 +1,5 @@
 # Auto detect text files and perform LF normalization
 * text=auto
+
+# Power BI LFS
+*.pbix filter=lfs diff=lfs merge=lfs -text

README.md

@@ -8,7 +8,40 @@
 ## Overview
 Small to mid-sized organizations are trapped in a cycle where they outgrow the capabilities of spreadsheets but lack the technical infrastructure to migrate to databases. This creates a "structural debt" where the very tools that allow a business to be agile (spreadsheets) are the same tools that make their data fundamentally untrustworthy for scaling or reporting.
 
-### The Solution
+## The Result: Operational Decision Systems
+The ultimate purpose of this defensive architecture is to serve the **Presentation Layer** with absolute reliability. By the time data reaches Power BI, it has been stripped of anomalies, structurally validated, and semantically flattened.
+
+Because the pipeline utilizes **Atomic View Swaps** in BigQuery, the semantic models are updated instantaneously. This guarantees that Power BI scheduled refreshes (Import Mode) never encounter locked tables or partial data, resulting in zero downtime for business consumers.
+
+> **Dynamic Sensitivity Calibration**
+>
+>The reporting suite features interactive "Smoke Detectors." Built with dynamic What-If parameters, these dashboards allow operators to manually adjust alert sensitivity thresholds to match changing business realities (e.g., loosening thresholds during peak holiday seasons).
+>
+> Explore the  **[Power BI Directory](/power_bi)** to read detailed [operational guides](/power_bi/docs) or download the `.pbix` [releases](/power_bi/releases/).
+
+### Customer Experience & Revenue Exposure
+A decision-support system designed to monitor financial risk driven by fulfillment failures. It correlates delivery delays with buyer drop-off rates, allowing leadership to quantify the "cost of friction."
+
+* **Calibration Mechanism:** Utilizes a **Delay Threshold Parameter** to dynamically define what constitutes a critical "Danger Zone" for segment and regional prioritization.
+
+![Customer Experince Image](/assets/screenshots/customer-experience-monitor.png)
+
+### Fulfillment Decision Monitor
+An operational early-warning system that identifies logistics partners and sellers requiring immediate intervention by focusing on statistical deviations in network speed rather than absolute failure.
+
+* **Calibration Mechanism:** Controlled via **Standard Deviation & Slippage Thresholds** to filter out normal variance and flag true systemic slowdowns.
+
+![Fulfillment Decision Monitor](/assets/screenshots/seller-fulfillment-monitor.png)
+
+### Product Friction Monitor
+
+Designed to identify physical and structural fulfillment bottlenecks driven by product specifications (e.g., weight-driven outliers).
+
+* **Calibration Mechanism:** Leverages **Standard Deviation & Slippage Thresholds** applied to *lead-time volatility*, alerting operations teams to route oversize items to specialized freight before delays impact the customer.
+
+![Product Friction Monitor](/assets/screenshots/product-friction-monitor.png)
+
+## The Solution
 This project solves that challenge by delivering a highly resilient, event-driven data pipeline on Google Cloud Platform for reliable operational analytics. It guarantees data integrity through a strict Medallion architecture (Bronze, Silver, Gold) that relies on rigid data contracts and validation gates to catch and isolate bad data early in the lifecycle.
 
 ### Defensive Pipeline Architecture
@@ -182,16 +215,20 @@ operations-analytics-pipeline/
 │   ├── shared/             # Extractor logic and core I/O utilities
 │   └── run_extract.py      # The Drive extractor orchestrator
 ├── data_pipeline/
+│   ├── .shared/            # Storage adapters, IO wrappers, and registry configurations
 │   ├── assembly/           # Delta merging and event mapping logic (Gold Pre-processing)
 │   ├── contract/           # Subtractive filtering logic (Silver Layer)
 │   ├── publish/            # Manages the atomic publish lifecycle of semantic datasets
 │   ├── semantic/           # Fact/Dimension table builders (Gold Layer)
-│   ├── shared/             # Storage adapters, IO wrappers, and registry configurations
 │   ├── validation/         # Dual-pass structural data validation gates
 │   └── run_pipeline.py     # The pipeline orchestrator and state manager
 ├── docs/                   # Detailed architectural and stage-level system contracts
 ├── runtime/                # Git-ignored ephemeral workspace used by the local pipeline executor
-└── tests/                  # Pytest suite for pipeline logic and validation rules
+├── tests/                  # Pytest suite for pipeline logic and validation rules
+└── power_bi/
+    ├── .shared/            # Global Dashboards BI assets (e.g. Themes, .json files, etc.)
+    ├── dashboards/         # Source Control (PBIP) 
+    └──releases             # Deliverables (PBIX)
 ```
 
 ## CI/CD & Security

power_bi/.shared/.m_queries/calendar_date.pq

@@ -0,0 +1,23 @@
+let
+    SourceFact = // fact table,
+    MaxDateInFact = Date.From(List.Max(SourceFact[week_start_date])),
+
+    // List of dates
+    StartDate = #date(2022, 12, 26),
+    EndDate = MaxDateInFact,
+    CalendarList = List.Dates(StartDate, Duration.Days(EndDate - StartDate) + 1, #duration(1, 0, 0, 0)),
+
+    // Convert to table
+    #"Converted to Table" = Table.FromList(CalendarList, Splitter.SplitByNothing(), {"Date"}, null, ExtraValues.Error),
+    #"Changed Type" = Table.TransformColumnTypes(#"Converted to Table",{{"Date", type date}}),
+
+    // Date format columns
+    #"Added Year" = Table.AddColumn(#"Changed Type", "Year", each Date.Year([Date]), Int64.Type),
+    #"Added Month" = Table.AddColumn(#"Added Year", "Month", each Date.Month([Date]), Int64.Type),
+    #"Added Month Name" = Table.AddColumn(#"Added Month", "Month Name", each Date.MonthName([Date]), type text),
+    #"Added Year Month" = Table.AddColumn(#"Added Month Name", "Year Month", each Text.From([Year]) & "-" & Text.PadStart(Text.From([Month]), 2, "0"), type text),
+    #"Added Week Number" = Table.AddColumn(#"Added Year Month", "Week Number", each " W" & Text.PadStart(Text.From(Date.WeekOfYear([Date])), 2, "0"), type text),
+    // #"Added Year Week" = Table.AddColumn(#"Added Year Month", "Year Week", each Text.From([Year]) & " W" & Text.PadStart(Text.From(Date.WeekOfYear([Date])), 2, "0"), type text),
+    #"Added Week Start Date" = Table.AddColumn(#"Added Week Number", "Week Start Date", each Date.StartOfWeek([Date], Day.Monday), type date)
+in
+    #"Added Week Start Date"