Title:
ICT701
Assignment 2
Muhammad Farhan Zafar
Student ID: 5604
Table of Contents
Introduction .................................................................................................................................................. 2
What is Business Intelligence? ...................................................................................................................... 4
Business Intelligence (BI) tools classically suggestion:.................................................................................. 5
The primary purposes of BI are:.................................................................................................................... 5
Characteristics of a Data Warehouse ............................................................................................................ 7
A data warehouse is typically structured into layers: ................................................................................... 7
Types of Data Warehouses............................................................................................................................ 8
Different types of data storage systems exist for various purposes: ............................................................ 8
Retail Business Intelligence System and Data Warehouse Implementation ................................................. 8
1. Data Sources: ........................................................................................................................................ 8
2. ETL Process ............................................................................................................................................ 9
3.In Retail Business Intelligence, a data warehouse organizes data into key areas: ................................. 9
4. Business Intelligence (BI) Tools and Dashboards .................................................................................. 9
Blue Bar: ...................................................................................................................................................... 10
Line Graph (Purple): .................................................................................................................................... 10
Funnel chart: ............................................................................................................................................... 11
Advantages of Business Intelligence (BI) Systems and Data Warehouses for Retail Industry: ................... 13
BI and Data Warehousing Challenges ........................................................................................................ 14
Best Practices for BI Systems and Data Warehouses Operation: ................................................................ 15
Conclusion ................................................................................................................................................... 15
Introduction
In today's business world, decision-making relies heavily on data and insights. Business
intelligence (BI) systems and data warehouses play a crucial role in providing this information.
These systems collect and store massive amounts of data, allowing companies to analyze it and
extract actionable insights. In the face of fierce competition, businesses need access to accurate,
up-to-date, and relevant information. BI systems help them gain a competitive edge by
optimizing processes and enabling them to adapt quickly to market shifts. This report delves into
the design, functionality, and implementation of BI systems and data warehouses. It explores
their significance, challenges, and benefits, focusing on retail applications. However, the
principles discussed are relevant to businesses of all industries.
What is Business Intelligence?
Business intelligence (BI) is a process driven by technology that analyzes business data in order
to provide information that can be actioned so that executives and managers can make better-
informed business decisions.
Business intelligence is a broad term that encompasses data mining, process analysis,
performance benchmarking, and descriptive analytics. BI parses all the data generated by a
business and presents easy-to-digest reports, performance measures, and trends that inform
management decisions
Business intelligence (BI) encompasses the tools, techniques, and technology that organizations
leverage to assess and interpret business information. This process involves collecting data,
storing it securely, and generating practical insights. Ultimately, BI aims to improve
understanding of business dynamics and market conditions, enabling effective decision-making
for key stakeholders.
Business Intelligence (BI) tools classically suggestion:
Data Visualization: Dashboards and reports to show data in a accessible setup.
Data Analysis: Data mining methods to reveal expressive shapes and propensities.
Advanced Analytics: Statistical forecasting and predictive modeling to make forecasts
and forestall future results.
The primary purposes of BI are:
Empowered Decision-Making: Deliver key stakeholders with appropriate and
appreciated data for informed decision-making.
Process Optimization: Categorize blocks and improvement areas to boost operative
efficiency.
Customer Understanding: Analyze customer behavior, preferences, and market
subtleties to increase customer involvements.
Competitive Positioning: Generate insights to benefit businesses upsurge an lead over
their rivals.
In the retail sector, Business Intelligence (BI) systems benefit businesses advance their
procedures by:
Optimizing Inventory: BI allows companies to estimate there claim extra precisely,
leading to upgraded inventory administration.
Increasing Sales: BI helps businesses classify leanings and customer preferences,
permitting them to tailor marketing operations and enhancement sales.
Enhancing Customer Relationships: BI tools offer perceptions into customer behavior,
permitting businesses to initial their connections and recover customer approval.
Data Warehouse: The Core of BI A data warehouse acts as a dynamic center for storing
and shaping data from various sources, both organized and unstructured. It flattens data
analysis and examination, discrete from transactional processing. The data stored in a
data warehouse naturally contains past records from different business systems, such as
sales, customer support, and finance.
Characteristics of a Data Warehouse
Subject-Centric: Data is organized around specific themes ( customers, transactions,
products) to support focused business analysis.
Data Consolidation: Information from diverse sources is standardized and merged into a
single, consistent dataset.
Historical Data Retention: Past data is preserved, enabling analysis of trends and changes
over time.
Immutable: Data stored in the data warehouse cannot be altered or removed, preserving
its accuracy and completeness.
A data warehouse is typically structured into layers:
Data Gathering: Information is collected from different sources, such as databases, files,
APIs, and external providers.
Data Processing: The gathered data is cleaned and converted into a common format. This
involves removing duplicates, filling in missing information, and ensuring consistency.
The processed data is then loaded into the data warehouse.
Data Storage: The core of the data warehouse, where the processed data is organized and
stored in a structured way. The data can be arranged in different formats, like the star
schema or snowflake schema.
Metadata Management: Information about data itself (source, structure, connections) is
stored in metadata. This enhances data management and speeds up queries.
Data Marts: Smaller subsets of a data warehouse, tailored to specific business areas.
Enable focused analysis without retrieving the entire warehouse.
Business Intelligence (BI) Tools: Data is retrieved from the warehouse for analysis and
reporting.
BI tools include dashboards, reports, and visualization platforms.
Users: Business analysts and decision makers: Access data through BI tools to analyze
and make informed decisions.
Types of Data Warehouses
Different types of data storage systems exist for various purposes:
Enterprise Data Warehouse (EDW): Massive, central storage for data used by entire
organizations to make important decisions.
Operational Data Store (ODS): Provides data for daily operations and reports in near
real-time, helping with day-to-day tasks.
Data Marts: Smaller, more specific data storage systems designed to meet the needs of
specific divisions or teams within an organization.
Retail Business Intelligence System and Data Warehouse Implementation
1. Data Sources:
In retail, data is collected from various sources:
Point of Sale (POS): tracks transactions Customer Relationship Management (CRM):
stores customer information
Inventory Management:
records stock levels and replenishment plans Marketing and Campaign:
handles promotions and customer engagement External Sources: provides information on
market trends, competitors, and economic indicator.
2. ETL Process
Retailers manage high volumes of data from multiple sources. The ETL process is crucial in this
context:
Extraction: Data is gathered from different systems
Cleansing: Data is corrected and standardized
Transformation: Data is formatted to align with the data warehouse
3.In Retail Business Intelligence, a data warehouse organizes data into key areas:
Sales Data Mart: Houses daily, weekly, and monthly sales figures for tracking performance
trends. Customer Data Mart: Stores customer profiles, purchase records, and engagement data
for segmentation and personalized marketing.
Inventory Data Mart: Contains information on stock levels, order history, and supplier
performance to optimize inventory management and prevent stock shortages or excess.
4. Business Intelligence (BI) Tools and Dashboards
Retailers leverage BI tools to: Display data visually for easy analysis
Monitor key business metrics (KPIs)
Identify trends and patterns Dashboards provide instant access to critical information, including:
Sales Trends: Track sales performance over various timeframes (daily, weekly, monthly)
Inventory Levels: Monitor real-time stock levels to optimize inventory management
Customer Insights: Understand customer behavior, such as preferences, purchase patterns, and
responses to promotions Moreover, retailers utilize predictive analytics provided by BI tools to:
Forecast future sales
Optimize pricing strategies : Adjust inventory to anticipate future demand
Blue Bar:
This bar represents the total sales generated by each product category. While all categories show
similar sales patterns, the health and beauty category appears to have slightly lower overall sales
compared to the others.
Line Graph (Purple):
Shows the Sum of Gross Margin Percentage for each product line. The gross margin
percentage varies across product lines:
Food and Beverages: Has a relatively lower gross margin percentage.
Sports and Travel: Shows a further decline in gross margin percentage.
Electronic Accessories: The margin percentage starts increasing.
Fashion Accessories: Exhibits the highest gross margin percentage among the
product lines.
Home and Lifestyle: A noticeable drop in gross margin percentage.
Health and Beauty: The lowest gross margin percentage.
Funnel chart:
The chart displays the quantity of items sold each month, comparing the distribution of sales
across the year.
The top three months (January, February, March) show the highest quantity sold, with 1K
units for each month.
From April to December, sales are significantly lower, labeled as "OK," indicating
moderate or reduced sales levels.
The bar width decreases progressively from January to December, reflecting a drop in
sales after the first quarter.
This funnel-like shape suggests that the highest sales occurred early in the year, with a gradual
decline in sales activity toward the end of the year.
The chart illustrates the quantity of purchases made by gender across different payment methods:
Cash, Credit Card, and Ewallet.
For females, cash purchases have the highest quantity, followed by credit card and
Ewallet purchases, with a slight drop after cash transactions.
For males, Ewallet purchases are highest, while credit card transactions show the lowest
quantity. Cash purchases are in the middle.
The general trend indicates that females prefer cash payments, while males favor Ewallets. Both
genders make fewer purchases using credit cards compared to the other payment methods.
This table displays the overall sales for Ballarat, Geelong, and Melbourne in Australia. Sales are
categorized by payment method (cash, credit card, and e-wallet) and customer gender (male and
female). Ballarat has significant cash and e-wallet sales. Notably, female customers contribute
the most to cash payments, with a total of 23,142.52, while males spend 20,291.33 in cash.
Geelong's sales are primarily driven by cash transactions from male customers, with a total of
19,204.18. However, e-wallet payments are lower in Geelong. Melbourne exhibits a more
diversified payment pattern. Both e-wallet and credit card sales are relatively high. Credit card
payments from female customers account for a significant portion, reaching 19,849.22. The total
sales across all cities amount to 325,721.75. Melbourne stands out with its substantial e-wallet
and credit card sales. The data reveals varying payment preferences across the different cities.
Advantages of Business Intelligence (BI) Systems and Data Warehouses for Retail Industry:
Enhanced Decision-Making: Real-time data access allows retailers to make
timely and well-informed decisions, from inventory optimization to pricing
adjustments.
Customer-Focused Insights: BI systems empower retailers to understand
customer behavior effectively, leading to tailored marketing campaigns and
loyalty initiatives.
Operational Efficiency: Data warehousing streamlines processes, such as
inventory management, reducing expenses and boosting profitability.
Trend Analysis and Forecasting: Historical data provides a basis for identifying
trends, forecasting performance, and anticipating shifts in demand and market
conditions.
Risk Management: BI systems detect potential threats, such as weak product
performance, enabling retailers to take proactive measures to mitigate risks.
BI and Data Warehousing Challenges
Data Integration: Addition data from different systems and setups can be difficult,
mainly with old-fashioned systems or data sources that lack uniformity.
Data Quality: Upholding the accuracy, completeness, and uniformity of data is
essential. Low-quality data can lead to misleading insights and inaccurate
decision-making.
Scalability: As retail businesses enlarge, the quantity of data shaped also
increases.
A data warehouse must measure to quarter this data development without
reducing down.
Cost and Complexity: Launching and supervision a data warehouse requires
important resources and funds. Companies must capitalize in structure, tools, and
knowhow to safeguard effective operation.
Security and Privacy: Sellers have a duty to safeguard data from unofficial
entree. They must implement strong security events and fulfil with guidelines to
shield customer information.
Best Practices for BI Systems and Data Warehouses Operation:
Start Modestly, Grow Gradually: Start with limited projects (sales and customer data
marts) and rise based on considerate with the system.
Ensure Data Integrity: Launch plain data management protocols, with data verification,
purification, and stable specialist care.
Prioritize Training: Whole training to staffs on using BI tools fruitfully.
Attention on Serious Metrics: Classify key business poles and generate dashboards and
reports to path their demonstration.
Embrace Advanced Analytics: Use extrapolative analytics and machine learning to
expose patterns, forecast future results, and make data-driven decisions.
Conclusion
Progressive retail observes rely acutely on business intelligence (BI) systems and data
warehouses. These tools form the provision for assemblage, storing, and examining huge
datasets. With these insights, decision-makers can make knowledgeable selections. In spite of
these tasks in data mixing, scalability, and cost, the continuing rewards of BI systems and data
warehouses are positive. As BI technology advances, businesses that completely grip its helps
will thrive in the increasingly data-centric marketplace. This technology will authorize them to
make data-driven results and stay in advance in the modest retail landscaped. A data warehouse
safeguards that this data is structured, clean, and naturally available for analysis. Data is
professionally held and stored using ETL/ELT procedures. Advanced analytics offer instant
visions. Dashboards and reporting help investors track crucial metrics and make well-versed
conclusions. Data governance and security measures confirm agreement and safeguard sensitive
material.
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