Business Intelligence Tools: Advantages

• Data of quality is needed for important decisions in the global market due to mass customization and competition

Advantages of Business Intelligence Tools

• Data Consolidation
• Visualization
• Insights
• Advanced Analysis
• Decision Support
• Monitoring Impact

Data Consolidation

• Import sales data from multiple sources
• Customer Relationship Management (CRM) Systems
• Enterprise Resource Planning (ERP) Systems
• Relational Database Systems
• Cloud Storage and Services
• Marketing Platforms
• Finance and Accounting Systems
• Project Management Tools
• Social Media Platforms
• Excel, CSV, JSON, XML

Visualization

• Create dashboards showing key metrics
• Show Power BI Example Retail Analysis Sample PBIX, tracking:
  • Total Stores (104)
  • New Stores (10)
  • Sales by Chain

Insights

• Use visuals to find stores with declining sales trends
• Line charts might show a consistent increase in sales for all new stores or a specific store over the last year

Advanced Analysis

• Apply filters to focus on district managers and their stores
• Use AI features like Q&A to determine "Which managers are underperformers?"

Decision Support

• Decisions are based on insights from the data.
• Targeted actions can be decided upon from these insights.

Monitoring Impact

• Using Power BI's real-time data refresh capability helps to monitor the effectiveness of promotion strategies.
• Adjustments can be made as needed:
  • Live Connection to SSAS Tabular
  • Direct Query to a Database
  • Import Data from Excel to a Database

Business Intelligence (BI)

• BI consists of technologies and tools to gather and store data systematically
• BI acts as a decision support system
• BI transforms enterprise data into knowledge through visuals
• BI helps an enterprise to make better business decisions at the right time

Decision Support System (DSS)

• Inputs:
  • Monitoring
  • Reports, Questionnaires, Text
  • Databases
  • Sales Data, Inventory Data, Market Data, Financial Data, Environmental Data, Survey Data, RFID Data
• DSS
• Outputs:
  • Strategic: Expansion Plans, E-commerce Technology Investments
  • Tactical: Pricing Strategies, Supplier Negotiation, Inventory Management
  • Operational: Restocking Shelves, Shaft Schedules, Daily Sales

How Business Intelligence Works

• Legacy systems, CRM(Customer Relationship Management), ERP(Enterprise Resource Planning), and Clickstream provide Operational Data
• The data is integrated and goes through storage in a Data Warehouse
• Dashboards and reports provide visualization.

Factors for Business Intelligence

• Group Communication & Collaboration enables collaboration across geography
• Improved Data Management handles complex data types like text, sound, and video
• Big Data warehouses integrate multiple sources, like those used by Walmart
• Anytime, Anywhere Support requires wireless technologies to access information

Evolution of Decision Support Tools

• 1970s: Decision Support Systems
• 1980s: Enterprise Information Systems
• 1990s: Business Intelligence
• 2020s: Analytics and Big Data

What is Business Intelligence?

• Umbrella term with varied meanings depending on the user
• Marketing Director: BI tools track customer behavior
• Financial Analyst: BI tools generate reports on revenue
• CEO: BI provides a high-level overview of the company

Definition of Business Intelligence

• Enable interactive access (sometimes in real time) to data
• Enable manipulation of data
• Provide business stakeholders the ability to make better decisions

The Future of Business Intelligence

• Data quality is one of the biggest challenges faced by Business Intelligence
• Innovations are making Business Intelligence tools more accessible and collaborative
• AI for Business Intelligence
• Better tools are generating more opportunities for businesses

Key Takeaways

• Business Intelligence has the potential to find information on where you least expect it
• Business Intelligence can be a gold mine for organizations
• Business Intelligence is a trend among organizations that seek to stand out
• The decision-making process becomes better once quality data is acquired

What is a Data Warehouse?

• A centralized repository for large volumes of structured and historical data from multiple sources
• It's designed to support decision-making processes in organizations
• Unlike operational databases, a data warehouse is optimized for analytical tasks and provides a Single Version of Truth (SVOT) for business intelligence (BI) purposes

Single Version of Truth (SVOT)

• Centralized Data Repository
• Data Standardization
• Data Accuracy
• Unified Metrics and KPIs
• Version Control: Single, up-to-date data accessible

OLTP vs OLAP

• OLTP (Online Transaction Processing) systems are core transactional systems focused on day-to-day business transactions
• OLAP (Online Analytical Processing) systems are designed for multidimensional data analysis, supporting complex queries and decision-making

OLTP (Online Transaction Processing)

• Handles day-to-day operational data (ERP, SCM, CRM)
• Uses real-time transactions (inventory updates, orders)
• Data updates are completed in an all-or-nothing fashion
• Inefficient for ad hoc reports and in-depth analysis

OLAP (Online Analytical Processing)

• Stores historical and current data
• Creates snapshots of business conditions for quick queries
• Provides reporting and interactive analysis, essential for Data Warehousing and BI

Evolution of Business Intelligence

• Descriptive
• Predictive
• Prescriptive

Descriptive Analytics

• Addresses what happened before and what's happening now
• Involves data warehouses, reports, queries, alerts, trends, visualization, and strategic/tacit knowledge

Predictive Analytics

• Addresses what will happen
• Forecasting
• Data mining
• Projections

Prescriptive Analytics

• Addresses what should be done and why
• Involves Machine Learning, explainable artificial intelligence and simulations
• Helps in making better, more informed decisions.

BI Application Areas

• BI can be applied in all “businesses” (industries, areas, domains)
  • Private: Retail, manufacture, real-estate, financial, sports, media, Entertainment, publications, etc.
  • Public (non-profit): Education, government, healthcare, associations, etc.

Power BI Desktop

• Connections: Connect to various data sources (Excel, SQL, Azure, etc.)
• Transformation: Clean and transform data using Power Query
• Modeling: Create relationships and calculated columns/measures using DAX
• Visualizations: Build reports with charts, graphs, and custom visuals
• Use Case: Business analysts and data professionals for in-depth reports

Power BI Desktop Interface

• Report View: Location where users create the visualizations
• Data View: Location where users can view and edit the raw data
• Model View: Location where users can manage relationships between datasets

Power BI Service

• Publishing Reports: Publish reports created in Power BI Desktop to the cloud
• Dashboards: Combine visuals from different reports into a single dashboard
• Sharing & Collaboration: Share reports and dashboards with team members
• Data Refresh: Automatic refresh of data connections in real-time
• Use Case: Used by organizations for sharing insights and collaborating across teams

Power BI Mobile App

• A mobile app (iOS, Android, and Windows) for accessing and interacting with Power BI reports and dashboards
• Key Features:
  • Mobile Reports: Access reports and dashboards from anywhere
  • Real-Time Data: View live, up-to-date insights in real-time
  • Alerts: Set up notifications for data changes or thresholds
• Use Case: For team members while away from their desks

Key Power BI Takeaways

• Power BI Desktop: Powerhouse for report creation and data modeling
• Power BI Service: For publishing, sharing, and collaborating with others
• Power BI Mobile: Offers flexibility and access to insights anytime, anywhere