# Create a Report based on multiple Data Store

### **Purpose**

This workflow explains how to create a **Self-Service Report** on the **BDB Platform** using **Retail Store Purchase and Sales Data**.\
It demonstrates how to use the **Data Sandbox**, build **Datastores**, and visualize insights in the **Report Module** using standard KPIs, charts, and Machine Learning (ML) capabilities such as **Customer Segmentation**, **Anomaly Detection**, and **Time Series Forecasting**.

### **Business Context**

Retail organizations often deal with large amounts of purchase and sales data that require real-time insights to optimize procurement, pricing, and customer engagement strategies.\
This workflow empowers business analysts to:

* Visualize **Procurement Trends** through KPI-driven dashboards.
* Identify **Top Performing Brands and Products**.
* Monitor **Sales Patterns** and **Category Performance**.
* Leverage **Machine Learning (ML)** tasks for forecasting, segmentation, and anomaly detection — all within a single self-service reporting environment.

### **Key Highlights**

<table data-header-hidden><thead><tr><th width="143.77783203125"></th><th></th></tr></thead><tbody><tr><td><strong>Category</strong></td><td><strong>Description</strong></td></tr><tr><td><strong>Goal</strong></td><td>Build a comprehensive Self-Service Report for retail purchase, sales, and insights.</td></tr><tr><td><strong>Data Source</strong></td><td>Retail Store Purchase and Sales Data uploaded to the Data Sandbox.</td></tr><tr><td><strong>Modules Used</strong></td><td>Data Center and Report modules.</td></tr><tr><td><strong>Key Features</strong></td><td>KPIs, interactive charts, ML View (Segmentation, Time Series, Anomaly Detection).</td></tr><tr><td><strong>Outcome</strong></td><td>A fully interactive, analytics-driven reporting workspace with intelligent forecasting.</td></tr></tbody></table>

### **Workflow Overview**

<table data-header-hidden><thead><tr><th width="65.888916015625"></th><th width="165.88885498046875"></th><th></th></tr></thead><tbody><tr><td><strong>Step</strong></td><td><strong>Module</strong></td><td><strong>Description</strong></td></tr><tr><td>1</td><td>Data Center</td><td>Upload Retail Purchase and Sales data to the Data Sandbox.</td></tr><tr><td>2</td><td>Data Center</td><td>Create Datastores for Purchase and Sales data.</td></tr><tr><td>3</td><td>Report Module</td><td>Create a Self-Service Report for Purchase data.</td></tr><tr><td>4</td><td>Report Module</td><td>Create a Self-Service Report for Sales data.</td></tr><tr><td>5</td><td>Report Module</td><td>Explore Machine Learning (ML) features for deeper insights.</td></tr></tbody></table>

### **Step 1 – Upload Data to the Data Sandbox**

1. Navigate to the **App Menu → Data Center**.
2. Click **Create (+)** → select **Data Sandbox**.
3. Provide the following details:
   * **Sandbox Name:** *Retail\_Store\_Data*
   * **Description:** *Contains retail store purchase and sales records*
4. Upload your Excel or CSV files:
   * *Retail\_Store\_Purchase\_Data.xlsx*
   * *Retail\_Store\_Sales\_Data.xlsx*
5. Click **Upload** to complete.
6. Once uploaded, both datasets will appear in the Sandbox list.

### **Step 2 – Create Datastores for Purchase and Sales Data**

1. In the **Data Center**, click the **three dots (⋮)** beside the Retail Sandbox.
2. Select **Create Data Store**.
3. Create two separate Datastores:
   * **Retail\_Purchase\_Store**
   * **Retail\_Sales\_Store**
4. Verify that both Datastores are active and available under the Data Store list.

* [x] **Result:** Two Datastores are now available for report creation — one for purchases, one for sales.

### **Step 3 – Create a Self-Service Report for Purchase Data**

1. Navigate to the **App Menu → Report Module**.
2. Click **Create Report**.
3. Provide:
   * **Report Name:** *Product Procurement Overview*
   * **Description:** *(Optional) A report visualizing procurement trends and top-purchased products.*
4. Select **Existing Data Store** as the data source.
5. Choose the **Retail\_Purchase\_Store** and click **Create Report**.

#### **3.1 Create KPI Tiles**

1. Search for **Purchase Amount** and plot a KPI tile showing the **Total Purchase Amount**.
2. Search for **Quantities** and plot a KPI tile showing the **Total Quantity Purchased**.
3. Use **Chart Properties** to adjust:
   * Color Theme (e.g., gradient green or blue)
   * Font Size and KPI Format (e.g., Currency, Numeric).
4. Save the KPI tiles.

#### **3.2 Create Brand and Product Insights Charts**

1. Search for **Brand Name** and **Purchase Amount**.
   * Create a **Bar Chart** showing the **Most Preferred Brand** by purchase amount.
   * Customize chart colors and label rotation under **Chart Properties**.
2. Search for **Product Name** and **Purchase Amount**.
   * Create a **Column Chart** showing **Top Purchased Products**.
   * Apply data sorting to show products in descending order of purchase value.
3. Add more KPIs as needed, such as:
   * Average Purchase Value
   * Total Unique Products Purchased.
4. Once complete, save the report as **“Product Procurement Overview.”**

### **Step 4 – Create a Self-Service Report for Sales Data**

1. In the **Report Module**, click **Create Report** again.
2. Provide:
   * **Report Name:** *Product Sale Overview*
   * **Description:** *(Optional) Visualizing sales trends and category performance.*
3. Select **Existing Data Store → Retail\_Sales\_Store**.
4. Click **Create Report.**

#### **4.1 Create Sales KPIs**

1. Search for **Billing Date** and **Net Sale Value**.
   * Plot a KPI tile showing the **Net Value of Goods Sold**.
2. Search for **Billing Date** and **Quantities**.
   * Plot a KPI tile showing the **Total Quantities Sold**

#### **4.2 Create Category Analysis Charts**

1. Search for **Product Categories** and **Net Sale Value**.
   * Plot a **Bar Chart** titled **“Most Valued Categories.”**
   * Use **Charting Properties → Sort → Descending** to show top performers.
2. Create another chart using the same fields.
   * Change **Sort → Ascending** and title it **“Least Valued Categories.”**
3. Apply consistent visual themes and axis formatting across all charts.
4. Save the completed report as **“Product Sale Overview.”**

### **Step 5 – Create the ML View for Insights**

The **Machine Learning (ML) View** enables users to apply predictive analytics directly within the report — without writing code.\
It supports four ML tasks:

* **Customer Segmentation**
* **Anomaly Detection**
* **Time Series Analysis**
* **Sentiment Analysis**

#### **5.1 Customer Segmentation**

1. Search for the following fields:
   * **City**, **Net Sale Value**, **Basic Cost**, **Tax Amount**, **Discount**.
2. Click the **ML Tab → Customer Segmentation** task.
3. Configure parameters:
   * **Feature Columns:** Select all the fields listed above.
   * **Cluster Count:** *3* (or as per requirement).
   * **Operator:** *K-Means Clustering*.
4. Click **Predict** to visualize customer segments.

* [x] **Result:** The chart groups customers into clusters based on purchase patterns.

#### **5.2 Time Series Forecasting**

1. Search for **Billing Date** and **Net Sale Value**.
2. Create a chart visualizing sales trends over time.
3. Click **ML Tab → Time Series Analysis**.
4. Configure:
   * **Feature Column:** *Net Sale Value*
   * **Prediction Count:** *10 (Next 10 Days)*
5. Click **Predict.**

* [x] **Result:** The model forecasts sales for the next 10 days based on historical data.

#### **5.3 Anomaly Detection**

1. Search for **Store List** and **Quantities**.
2. Create a chart displaying quantity distribution per store.
3. Click **ML Tab → Anomaly Detection.**
4. Configure:
   * **Feature Column:** *Quantities*
   * **Store Filter:** *Wonders*
5. Click **Predict.**

* [x] **Result:** The system highlights stores with abnormally high quantities as anomalies.

#### **5.4 Sentiment Analysis (Optional)**

1. Upload a **Customer Review Dataset** (if available).
2. Select **Review Text** as the feature column.
3. Run **Sentiment Analysis** under the ML tab.
4. Review polarity outputs (*Positive*, *Neutral*, *Negative*) as insights.

### **Step 6 – Finalize and Save the Insights Report**

1. Review all KPIs, charts, and ML results.
2. Click **Theme Settings** and apply a visual theme for branding consistency (e.g., Retail Blue or Dark Mode).
3. Provide:
   * **Final Report Name:** *Retail Insights Dashboard*
   * **Description:** *(Optional) Integrated overview of Purchase, Sales, and ML insights.*
4. Click **Save Report.**

* [x] **Final Output:** Three separate but linked reports:
  * Product Procurement Overview
  * Product Sale Overview
  * Retail Insights (ML View)

### **Outcome**

* [x] Two self-service reports created using **Retail Purchase and Sales Datastores**.
* [x] Insights visualized through **KPI Tiles, Bar, and Column Charts**.
* [x] Advanced analytics achieved using **ML View (Segmentation, Forecasting, Anomaly Detection)**.
* [x] Unified **Retail Insights Dashboard** enabling data-driven decision-making.

### **Best Practices**

* Use **consistent column names** across purchase and sales data.
* Regularly update **Sandbox data** to reflect recent transactions.
* For forecasting, ensure at least **30 days of historical data**.
* Save **custom chart views** for reuse across reports.
* Apply **color-coded themes** for better visual distinction (e.g., blue for sales, green for purchase).
* Validate all KPI metrics before publishing reports.

### **Business Value**

This workflow helps retail teams:

* Monitor **procurement and sales KPIs** in one consolidated view.
* Predict **future sales** and detect operational anomalies.
* Segment customers for **targeted marketing and loyalty programs**.
* Enable **self-service analytics** without technical dependencies.
* Enhance operational agility with real-time data insights.&#x20;


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