Business Intelligence (BI) Explained

Business Intelligence (BI)

• BI involves technologies, processes, and tools for data analysis to inform business decisions.
• It transforms raw data into actionable insights.
• BI improves decision-making, identifies trends, and enhances efficiency.

3 Phases of BI

• Data Collection: Gathering data from various sources.
• Data Analysis: Processing and analyzing collected data.
• Data Visualization: Presenting data in understandable formats like dashboards and reports.

KPIs (Key Performance Indicators)

• KPIs are metrics used to measure business performance.
• KPIs vary based on industry and business goals.
• Examples of KPIs include revenue and customer satisfaction.

Data Silo

• A data silo is isolated data stored by a specific department, difficult for others to access.
• Data silos are caused by cultural competition between departments, structural hierarchical organizations, and incompatible technology.
• Silos limit a global view of data, threaten data integrity, waste resources, and discourage collaboration.
• The solution is to unify data for better analysis and decision-making.

Data Warehouse

• A data warehouse is a large database collecting data from multiple sources for analysis.
• It is secure, reliable, easily recoverable, and focused on analysis.
• Data warehouses help businesses improve performance through analysis.
• Examples of data warehouses are Supply Chain Management, ERP, and OLAP.

ETL (Extract, Transform, Load)

• ETL extracts data from sources, transforms (cleans, filters) it, and loads it into a data warehouse.
• It is resource-intensive and complex to design and maintain.
• Tools include Oracle Warehouse Builder, IBM WebSphere DataStage, and Microsoft SSIS.

RDBMS (Relational Database Management System)

• A system that manages relational databases, where data is stored in tables and related.
• Examples of RDBMS are Microsoft SQL Server, Oracle Database, MySQL, IBM DB2, PostgreSQL, and Microsoft Access.
• It enables interaction with databases via SQL.

SQL (Structured Query Language)

• A programming language used to communicate with and manage relational databases.
• SQL allows programmers to extract, manipulate, and analyze data.

Defining KPI (Key Performance Indicator)

• Metrics measuring specific business goals and performance.
• KPIs are relevant, clear, easy to understand, comparable, cost-effective, timely, and verifiable.
• Examples of Revenue KPIs are profits, total revenue, and new customers.
• Examples of Employment KPIs are employee turnover, performance, and vacancies.
• Examples of Customer Service KPIs are call time, efficiency, and satisfaction.
• Examples of Marketing KPIs are sales generation and campaign effectiveness.
• Examples of Efficiency KPIs include overall and departmental processes.
• High-Level KPIs measure overall business performance like profits.
• Low-Level KPIs measure specific outputs or performance, like product performance.

Dashboard

• A BI tool visualizing key performance indicators (KPIs) on a single screen.
• It provides real-time insights for executives to monitor performance and make decisions.
• BI dashboards display data like revenue, customer satisfaction, and operational efficiency.

Importance of Avoiding Data Silos

• A global view is prevented, limiting opportunities for cost savings and efficiency.
• Storing the same data in multiple places can lead to inconsistencies and inaccuracies.
• Consolidating data saves storage space and reduces costs.
• Breaking down silos encourages teamwork and shared insights.

Data Warehousing

• Collecting and managing data from various sources to provide business insights.
• Centralizes data for analysis and reporting, serving as the core of BI systems.

Data Types

• Metadata: Data that describes other data.
• Summary Data: Statistical records derived from raw data.
• Raw Data: Unprocessed data as collected from sources.

KPIs vs. Metrics

• Metric: A standard measurement.
• KPI: A metric with context, used for decision-making.

Setting Goals Using KPIs

• KPIs should align with business objectives and be regularly monitored.
• Digital marketing tracks metrics like website traffic, conversion rates, and customer acquisition costs.
• KPIs measure campaign effectiveness and ROI.

Dashboards in BI

• Visualize KPIs and business metrics for real-time decision-making.
• BI dashboards displays revenue, customer satisfaction, and operational efficiency on one screen.

Big Data

• Extremely large and complex datasets unmanageable by traditional tools.
• Structured Data: Organized data like databases and spreadsheets.
• Unstructured Data: Data without a predefined format like emails and videos.
• Semi-structured Data: Data with some organization.
• Big Data helps businesses gain insights, improve decision-making, and optimize operations.

Big Data Generation

• Approximately 328.77 million terabytes of data are created daily
• In 2023 around 120 zettabytes of data was generated.
• Expected to reach 181 zettabytes by 2025.
• Videos account for over half of internet data traffic.
• The US has over 2,700 data centers.

The Three V's of Big Data

• Volume: Sheer amount of data from various sources.
• Velocity: Speed at which data is generated and processed.
• Variety: Different formats of data, structured, unstructured, and semi-structured.

Sectors Leveraging Big Data

• Big data analytics finds use in retail, healthcare, finance, and transportation.
• Big Data Analytics: Examining large datasets to uncover patterns and trends.
• Benefits: More effective marketing, new revenue, improved personalization, efficiency, and competitive advantages.

How Big Data Analytics Works

• Data Collection: From web servers, cloud apps, mobile apps, social media, IoT sensors, and others.
• Data Processing: Organizing, configuring, and partitioning data for analysis.
• Data Cleansing: Removing errors, inconsistencies, and duplicates.
• Data Analysis: Using data mining, predictive analytics, machine learning, deep learning, text mining and data visualization.

Big Data Analytics Technologies

• Examples include artificial intelligence, business intelligence software, data visualization tools, and statistical analysis software.

Uses and Examples of Big Data Analytics

• Customer Acquisition and Retention: Personalization engines improve experience and loyalty.
• Targeted Ads: Personalized ads based on past purchases and browsing history.
• Product Development: Insights into product viability and development decisions.
• Price Optimization: Retailers use data to set optimal pricing.
• Supply Chain and Channel Analytics: Predictive models optimize inventory and delivery schedules.
• Risk Management: Identifying risks from data patterns to develop strategies.
• Improved Decision-Making: Faster decisions based on data insights.

Benefits of Big Data Analytics

• Quick Analysis: Rapidly analyze data from diverse sources and formats.
• Better Decision-Making: Improved strategic decisions in supply chain and operations.
• Cost Savings: Efficiency improvements lead to reduced costs.
• Customer Insights: Better understanding of customer needs and behavior.
• Risk Management: Improved strategies based on large data samples.

Challenges of Big Data Analytics

• Data Accessibility: Storing and processing large datasets is complex.
• Data Quality Maintenance: Ensuring data accuracy and consistency requires effort.
• Data Security: Security challenges.
• Choosing the Right Tools: Selecting appropriate tools can be difficult.
• Skill Shortage: Demand for data scientists and engineers.

IoT and Big Data

• Develop IoT devices (e.g., sensors for energy management and attendance tracking) for a university.
• Use IoT data to improve efficiency, reduce costs, and enhance student experiences.
• Collect data on energy usage, attendance, or facility utilization.
• Analyze data to identify trends, optimize resources, and make informed decisions.

CRM (Customer Relationship Management)

• A system to manage customer interactions and data throughout the customer lifecycle.
• To improve relationships, increase retention, and drive sales growth.
• Key is understand customers, retention, and customer lifetime value (CLTV).

Why CRM is Important

• CRM helps companies differentiate themselves by understanding and meeting customer needs better than competitors.
• Customer Relationships: CRM systems consolidate customer information to provide a unified view of the customer.
• Acquire new customers.
• Provide better service and support to existing customers.
• Customize offerings to meet customer needs.
• Retain profitable customers by delivering ongoing value.

Types of CRM Systems

• Operational CRM: Focuses on customer-facing processes (sales, marketing, service).
• Sales Force Automation (SFA): Helps sales teams by providing tools for contact management and sales forecasting.
• Customer Service: Improves call center efficiency by routing calls and maintaining customer data.
• Marketing Automation: Supports direct marketing campaigns by managing customer data and tracking success.
• Analytical CRM: Focuses on analyzing customer data to identify trends and predict behaviors.
• Tools like data mining, OLAP, and predictive analytics are used.
• Identifies profitable customers, understand patterns, and improve strategies.

Business Value of CRM Systems

• Increased Customer Satisfaction: Better service and personalized interactions.
• Cost Reduction: Lower costs for customer acquisition, retention, and marketing campaigns.
• Increased Revenue: Effective cross-selling and up-selling strategies.
• Customer Retention: Reduced churn rate.
• Improved Decision-Making: Data-driven insights for better strategic decisions.

ICICI Bank (Case Study)

• India’s largest private sector bank, known for its successful CRM strategies.
• Reduce service costs, increase retention, improve cross-selling, and enhance efficiency.
• Multiple Channels: Customers can use internet banking, ATMs, call centers, mobile apps, and more.
• Increased interaction with customers to improve service and build loyalty.
• One-to-One Marketing: Tracking customer lifecycle to offer personalized services.
• Campaign Management: Automating and analyzing marketing campaigns.

Components of the CRM Cycle

• Understand and Differentiate: Profiling & Valuation.
• profiling: Understand demographics, purchase patterns, and preferences.
• Customer Valuation: Analyze profitability and lifetime value.
• Develop and Customize: Products and services based on customer needs.
• Customize offerings based on customer value.
• Interact and Deliver: Access to customer information and tailored interactions.
• Acquire and Retain: Retain high-value customers & Improve retention strategies.

Implementing CRM

• Business Focus: Align CRM goals with business objectives.
• Organizational Structure: Ensure the organization supports CRM initiatives.
• Business Metrics: Set measurable targets (e.g., customer growth, profitability).
• Marketing Focus: Use CRM to improve marketing effectiveness.
• Technology: Choose the right CRM software and tools.
• Identify CRM goals.
• Set measurable targets.
• Evaluate and choose the right CRM package.
• Implement and monitor the CRM system.

Benefits of Effective CRM

• Cost Reduction: Efficient operations and targeted marketing.
• Increased Customer Satisfaction: Customers get what they want.
• External Focus: The organization focuses on customer needs.
• Customer Growth: Increased number of customers.
• Maximized Opportunities: Cross-selling, up-selling, and referrals.
• Long-Term Profitability: Sustainable growth through better customer relationships.

ICICI Bank's CRM Initiatives

• Mobile ATMs for convenient banking and ATMs for blind clients.
• Prepaid mobile recharge, internet packs, and mutual fund transactions via ATMs.
• Mobile Wallet for financial transactions and Informed customers about new services and offers.
• Acting on customer feedback to improve services.

Conclusion

• Attracting, maintaining, and enhancing customer relationships.
• Customers are the core of business activities, and maintaining strong relationships yields better results.
• Proactive service enables companies to anticipate customer needs.