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Redo this but this time create hypothetical data bases for the sales, ordering, provisioning, inventory, and sales example and include SQL code for the examples

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Certainly. I’ll create hypothetical databases for the sales, ordering, provisioning, inventory, and billing systems, and then provide SQL code examples for each scenario. This will give a more concrete illustration of how the RAG and ANN technologies could interact with the actual data structures.

SQL Examples: RAG-Based Analysis of Legacy Telco IT Systems

Click to open code

I’ve created hypothetical database schemas for the sales, ordering, provisioning, inventory, and billing systems, along with SQL code examples for each of the scenarios we discussed earlier. Here’s a summary of the examples:

1. Data Modeling Decay – Customer Address Inconsistency: The SQL query compares customer addresses across the sales, billing, and provisioning systems, highlighting inconsistencies.
2. Process Logic Decay – Outdated Credit Check Process: This query analyzes customer spending and payment behavior in relation to their credit scores, providing data for the ANN to identify patterns that the current credit check process might miss.
3. System Integration Issues – Order-to-Activation Delay: The SQL code examines the time between order placement and service activation, categorizing activations as “On Time,” “Delayed,” or “Not Activated.”
4. Revenue Leakage – Unbilled Services: This query identifies active services in the provisioning system that don’t have corresponding entries in the billing system.
5. Predictive Maintenance – Network Equipment Failure Prediction: The SQL code prepares a dataset for ANN analysis by combining equipment data with performance measurements and environmental factors, and calculating rolling averages and standard deviations.

These SQL examples demonstrate how the RAG and ANN technologies could interact with the actual data structures in the legacy systems. They provide a starting point for more in-depth analysis and can be further refined based on the specific needs and complexities of the telco’s systems.

Would you like me to explain any of these SQL queries in more detail or discuss how they might be integrated into the larger RAG and ANN analysis process?

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Edit

Certainly. Let’s explore how these SQL queries can be integrated into the larger RAG and ANN analysis process, and how we can use the results to drive improvements in the telco’s systems.

Integration of SQL Queries with RAG and ANN Analysis

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I’ve expanded on how we can integrate the SQL queries with RAG and ANN technologies to create a comprehensive analysis and optimization system for the telco. Here’s a summary of the key points:

1. Data Modeling Decay – Customer Address Inconsistency:
   • We use RAG to generate recommendations for address standardization based on industry best practices.
   • Implementation includes creating a unified customer profile and setting up automated data quality checks.
2. Process Logic Decay – Outdated Credit Check Process:
   • An ANN is trained to predict customer value based on various factors beyond just credit score.
   • We implement A/B testing to compare the new model with the existing one before full rollout.
3. System Integration Issues – Order-to-Activation Delay:
   • We combine RAG and ANN to identify delay patterns and suggest process improvements.
   • Implementation includes real-time order status tracking and a performance monitoring dashboard.
4. Revenue Leakage – Unbilled Services:
   • An ANN is used to predict which services are at risk of being unbilled.
   • We implement automated daily reconciliation between systems and develop a workflow for resolving issues.
5. Predictive Maintenance – Network Equipment Failure Prediction:
   • An ANN is trained to recognize patterns that precede equipment failures.
   • We implement real-time monitoring and a predictive maintenance scheduling system.

For each area, I’ve provided example code snippets (SQL or Python) to illustrate how these concepts could be implemented in practice.

This integrated approach allows the telco to leverage both the pattern recognition capabilities of ANNs and the contextual knowledge provided by the RAG system. It creates a powerful framework for continuous improvement across all aspects of the business.

Would you like me to dive deeper into any specific aspect of this integration, or discuss how we might measure the impact of these improvements?

Creating the data bases.

I’ve created a detailed schema for our hypothetical telco databases, including sample data for each table. Here’s a summary of the databases and their tables:

1. Sales Database:
   • customers: Contains customer information including credit scores.
   • orders: Stores order information.
2. Ordering Database:
   • orders: Mirrors the sales orders table for order processing.
   • order_items: Contains details of individual items in each order.
3. Provisioning Database:
   • services: Tracks service activation and status for each customer.
4. Inventory Database:
   • products: Lists available products and services.
   • equipment: Tracks network equipment status and maintenance.
5. Billing Database:
   • customers: Contains customer billing information.
   • invoices: Stores invoice data for each customer.
   • invoice_items: Details individual items on each invoice.
6. Monitoring Database:
   • equipment_measurements: Stores performance and environmental data for predictive maintenance.

Each table includes sample data to illustrate the type of information stored and how it relates to other tables.

Key points to note:

1. The customer information is slightly different across databases, reflecting the data inconsistency issues we discussed.
2. The ordering and provisioning processes are represented, showing how an order moves through the system.
3. The billing system is separate, allowing us to identify potential unbilled services.
4. The equipment and measurement tables provide data for predictive maintenance analysis.

This structure allows us to demonstrate all the scenarios we’ve discussed:

• Address inconsistencies across systems
• Credit check process based on customer data
• Order-to-activation delays
• Unbilled services
• Equipment performance for predictive maintenance

Would you like me to explain any specific relationships between these tables or demonstrate how we might use this data structure to address one of our analysis scenarios in more detail?

— Sales Database

CREATE DATABASE sales;

USE sales;

CREATE TABLE customers (

    customer_id INT PRIMARY KEY,

    name VARCHAR(100),

    address VARCHAR(200),

    phone VARCHAR(20),

    email VARCHAR(100),

    credit_score INT

);

CREATE TABLE orders (

    order_id INT PRIMARY KEY,

    customer_id INT,

    product_id INT,

    order_date DATE,

    status VARCHAR(20),

    FOREIGN KEY (customer_id) REFERENCES customers(customer_id)

);

— Sample data

INSERT INTO customers VALUES 

(1, ‘John Doe’, ‘123 Main St, Anytown, AN 12345’, ‘555-1234’, ‘john@example.com’, 720),

(2, ‘Jane Smith’, ‘456 Oak Rd, Somewhere, SW 67890’, ‘555-5678’, ‘jane@example.com’, 680),

(3, ‘Bob Johnson’, ‘789 Pine Ave, Elsewhere, EL 13579’, ‘555-9012’, ‘bob@example.com’, 590);

INSERT INTO orders VALUES

(101, 1, 1001, ‘2023-01-15’, ‘COMPLETED’),

(102, 2, 1002, ‘2023-02-01’, ‘PROCESSING’),

(103, 3, 1001, ‘2023-02-10’, ‘PENDING’);

— Ordering Database

CREATE DATABASE ordering;

USE ordering;

CREATE TABLE orders (

    order_id INT PRIMARY KEY,

    customer_id INT,

    product_id INT,

    order_date DATE,

    status VARCHAR(20)

);

CREATE TABLE order_items (

    item_id INT PRIMARY KEY,

    order_id INT,

    product_id INT,

    quantity INT,

    FOREIGN KEY (order_id) REFERENCES orders(order_id)

);

— Sample data

INSERT INTO orders VALUES

(101, 1, 1001, ‘2023-01-15’, ‘COMPLETED’),

(102, 2, 1002, ‘2023-02-01’, ‘PROCESSING’),

(103, 3, 1001, ‘2023-02-10’, ‘PENDING’);

INSERT INTO order_items VALUES

(1001, 101, 1001, 1),

(1002, 102, 1002, 2),

(1003, 103, 1001, 1);

— Provisioning Database

CREATE DATABASE provisioning;

USE provisioning;

CREATE TABLE services (

    service_id INT PRIMARY KEY,

    customer_id INT,

    product_id INT,

    activation_date DATE,

    status VARCHAR(20),

    geocode VARCHAR(20)

);

— Sample data

INSERT INTO services VALUES

(10001, 1, 1001, ‘2023-01-20’, ‘ACTIVE’, ‘40.7128,-74.0060’),

(10002, 2, 1002, NULL, ‘PENDING’, ‘34.0522,-118.2437’),

(10003, 3, 1001, ‘2023-02-15’, ‘ACTIVE’, ‘41.8781,-87.6298’);

— Inventory Database

CREATE DATABASE inventory;

USE inventory;

CREATE TABLE products (

    product_id INT PRIMARY KEY,

    name VARCHAR(100),

    type VARCHAR(50),

    stock_quantity INT

);

CREATE TABLE equipment (

    equipment_id INT PRIMARY KEY,

    type VARCHAR(50),

    status VARCHAR(20),

    last_maintenance_date DATE

);

— Sample data

INSERT INTO products VALUES

(1001, ‘Basic Internet Package’, ‘SERVICE’, NULL),

(1002, ‘Premium TV Package’, ‘SERVICE’, NULL),

(2001, ‘Modem X1’, ‘HARDWARE’, 500),

(2002, ‘Router Y2’, ‘HARDWARE’, 350);

INSERT INTO equipment VALUES

(3001, ‘Cell Tower’, ‘ACTIVE’, ‘2023-01-01’),

(3002, ‘Fiber Optic Node’, ‘ACTIVE’, ‘2023-02-15’),

(3003, ‘Network Switch’, ‘MAINTENANCE’, ‘2022-12-01’);

— Billing Database

CREATE DATABASE billing;

USE billing;

CREATE TABLE customers (

    customer_id INT PRIMARY KEY,

    name VARCHAR(100),

    street VARCHAR(100),

    city VARCHAR(50),

    state VARCHAR(2),

    zip VARCHAR(10)

);

CREATE TABLE invoices (

    invoice_id INT PRIMARY KEY,

    customer_id INT,

    invoice_date DATE,

    total_amount DECIMAL(10, 2),

    status VARCHAR(20),

    FOREIGN KEY (customer_id) REFERENCES customers(customer_id)

);

CREATE TABLE invoice_items (

    item_id INT PRIMARY KEY,

    invoice_id INT,

    service_id INT,

    amount DECIMAL(10, 2),

    FOREIGN KEY (invoice_id) REFERENCES invoices(invoice_id)

);

— Sample data

INSERT INTO customers VALUES

(1, ‘John Doe’, ‘123 Main St’, ‘Anytown’, ‘AN’, ‘12345’),

(2, ‘Jane Smith’, ‘456 Oak Rd’, ‘Somewhere’, ‘SW’, ‘67890’),

(3, ‘Bob Johnson’, ‘789 Pine Ave’, ‘Elsewhere’, ‘EL’, ‘13579’);

INSERT INTO invoices VALUES

(5001, 1, ‘2023-02-01’, 79.99, ‘PAID’),

(5002, 2, ‘2023-02-01’, 129.99, ‘PENDING’),

(5003, 3, ‘2023-03-01’, 79.99, ‘PAID’);

INSERT INTO invoice_items VALUES

(7001, 5001, 10001, 79.99),

(7002, 5002, 10002, 129.99),

(7003, 5003, 10003, 79.99);

— Additional table for equipment measurements (for predictive maintenance)

CREATE DATABASE monitoring;

USE monitoring;

CREATE TABLE equipment_measurements (

    measurement_id INT PRIMARY KEY,

    equipment_id INT,

    measurement_date DATETIME,

    performance_metric DECIMAL(5,2),

    temperature DECIMAL(5,2),

    humidity DECIMAL(5,2)

);

— Sample data

INSERT INTO equipment_measurements VALUES

(1, 3001, ‘2023-03-01 00:00:00’, 95.5, 72.3, 45.0),

(2, 3001, ‘2023-03-01 01:00:00’, 94.8, 71.9, 46.2),

(3, 3001, ‘2023-03-01 02:00:00’, 95.2, 71.5, 46.5),

(4, 3002, ‘2023-03-01 00:00:00’, 98.1, 68.7, 40.3),

(5, 3002, ‘2023-03-01 01:00:00’, 97.9, 69.1, 41.0),

(6, 3002, ‘2023-03-01 02:00:00’, 98.0, 69.3, 40.8);

Integration of SQL Queries with RAG and ANN Analysis

1. Data Modeling Decay – Customer Address Inconsistency

Integration with RAG:

1. The RAG system uses the results of the SQL query to identify inconsistencies in address data across systems.
2. It then retrieves relevant information from its knowledge base about data standardization practices and telco-specific address management.
3. Based on this, the RAG system generates recommendations for address standardization.

Implementation Steps:

1. Create a unified customer profile with standardized address fields.
2. Implement an address validation and normalization service that integrates with all systems.
3. Set up automated data quality checks to flag inconsistencies.

sql

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— Example of a data quality check query

SELECT customer_id, COUNT(DISTINCT address_format) as address_format_count

FROM (

    SELECT customer_id, ‘sales’ as system, address as address_format FROM sales.customers

    UNION ALL

    SELECT customer_id, ‘billing’, street || ‘, ‘ || city || ‘, ‘ || state || ‘ ‘ || zip FROM billing.customers

    UNION ALL

    SELECT customer_id, ‘provisioning’, geocode FROM provisioning.services

) combined_addresses

GROUP BY customer_id

HAVING COUNT(DISTINCT address_format) > 1;

2. Process Logic Decay – Outdated Credit Check Process

Integration with ANN:

1. Use the SQL query results as training data for an ANN.
2. The ANN learns to predict customer value based on various factors beyond just credit score.

Implementation Steps:

1. Develop a new credit scoring model using the ANN.
2. Implement A/B testing to compare the new model with the existing one.
3. Gradually roll out the new model, monitoring its performance.

python

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# Pseudo-code for ANN model

import tensorflow as tf

model = tf.keras.Sequential([

    tf.keras.layers.Dense(64, activation=’relu’, input_shape=(5,)),

    tf.keras.layers.Dense(32, activation=’relu’),

    tf.keras.layers.Dense(1, activation=’sigmoid’)

])

model.compile(optimizer=’adam’, loss=’binary_crossentropy’, metrics=[‘accuracy’])

# Train the model using the data from the SQL query

model.fit(X_train, y_train, epochs=100, validation_split=0.2)

3. System Integration Issues – Order-to-Activation Delay

Integration with RAG and ANN:

1. Use the SQL query to identify patterns in activation delays.
2. The ANN predicts likely delay causes based on order characteristics.
3. The RAG system suggests process improvements based on these insights and best practices.

Implementation Steps:

1. Implement real-time order status tracking across all systems.
2. Develop automated alerts for orders exceeding expected activation times.
3. Create a dashboard for monitoring order-to-activation performance.

sql

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— Query to populate the dashboard

SELECT 

    DATE_TRUNC(‘day’, o.order_date) as date,

    COUNT(*) as total_orders,

    AVG(DATEDIFF(day, o.order_date, p.activation_date)) as avg_activation_time,

    SUM(CASE WHEN DATEDIFF(day, o.order_date, p.activation_date) > 3 THEN 1 ELSE 0 END) as delayed_orders

FROM 

    ordering.orders o

LEFT JOIN 

    provisioning.services p ON o.customer_id = p.customer_id AND o.product_id = p.product_id

GROUP BY 

    DATE_TRUNC(‘day’, o.order_date)

ORDER BY 

    date DESC;

4. Revenue Leakage – Unbilled Services

Integration with ANN:

1. Use the SQL query results to train an ANN to predict which services are at risk of being unbilled.
2. The ANN can consider factors like service type, customer history, and provisioning details.

Implementation Steps:

1. Implement automated daily reconciliation between provisioning and billing systems.
2. Develop a workflow for investigating and resolving unbilled services.
3. Create alerts for high-value services that remain unbilled after a certain period.

python

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# Pseudo-code for automated reconciliation

def reconcile_services():

    unbilled_services = run_sql_query(“SELECT * FROM unbilled_services_view”)

    for service in unbilled_services:

        if service.days_since_activation > 7:

            create_alert(service)

        elif predict_billing_risk(service) > 0.8:

            flag_for_review(service)

# Run this function daily

schedule.daily(reconcile_services)

5. Predictive Maintenance – Network Equipment Failure Prediction

Integration with ANN:

1. Use the SQL query results to train an ANN for predicting equipment failures.
2. The ANN learns to recognize patterns in performance metrics and environmental data that precede failures.

Implementation Steps:

1. Implement real-time monitoring of equipment performance and environmental factors.
2. Develop a predictive maintenance scheduling system based on ANN predictions.
3. Create alerts for equipment with high failure probability.

python

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# Pseudo-code for predictive maintenance system

def predict_failures():

    equipment_data = run_sql_query(“SELECT * FROM equipment_performance_view”)

    for equipment in equipment_data:

        failure_prob = failure_prediction_model.predict(equipment)

        if failure_prob > 0.7:

            schedule_maintenance(equipment)

        elif failure_prob > 0.5:

            increase_monitoring_frequency(equipment)

# Run this function hourly

schedule.hourly(predict_failures)

By integrating these SQL queries with RAG and ANN technologies, we create a powerful system for analyzing and optimizing the telco’s operations. The RAG system provides context and best practices, while the ANN models learn from historical data to make predictions and identify patterns. Together, they can drive continuous improvement across all aspects of the business.