Business Intelligence and Analytics -
Midterm Review
1. Overview of Business Intelligence (BI)
Business Intelligence (BI) is the use of various technologies, tools, and processes to analyze
and present business data to help organizations make informed decisions. The purpose of
BI is to transform data into actionable insights, improving decision-making, operational
efficiency, and business strategies. BI involves multiple components: Data, Technology,
People, and Processes.
Over time, BI has evolved with advancements in hardware, software, and networking.
Initially, BI was primarily confined to IT departments, but now it is widely used across
various departments like marketing, operations, finance, and even by individual employees.
2. Data Management
Data is a crucial element in Business Intelligence and Analytics. Data can be categorized into
two main types: Structured and Unstructured. Structured data is organized and stored in a
tabular format (e.g., databases), making it easy to process and analyze. Unstructured data,
on the other hand, lacks a predefined structure (e.g., social media posts, images, videos),
requiring more complex techniques for analysis.
The ETL (Extract, Transform, Load) process is essential in managing and integrating data. It
involves extracting data from various sources, transforming it into a usable format
(cleaning, structuring), and loading it into a central data repository like a data warehouse.
Data quality—ensuring the accuracy, reliability, and integration of data—can often be a
challenge, but it is vital to ensure meaningful insights.
3. Data Warehouses and Data Marts
A data warehouse is a centralized repository that stores integrated data from multiple
sources. It serves as the backbone of an organization’s data analysis activities. Data marts,
on the other hand, are smaller, department-specific versions of data warehouses that focus
on specific needs. While data warehouses support enterprise-wide analytics, data marts are
built for specialized purposes.
4. Data Integration Challenges
Integrating data from disparate sources (e.g., internal databases, external providers, social
media) is complex and time-consuming. The ETL process is often complicated by data
quality issues such as redundancy, inconsistency, and missing values. Overcoming these
challenges ensures that BI tools provide accurate and reliable insights.
5. BI Tools and Middleware
BI tools are software applications used to analyze, visualize, and report data. Middleware
acts as a bridge between BI applications and the data warehouse, enabling seamless data
exchange. Without middleware, BI tools would struggle to retrieve and process data
effectively.
6. Data Visualization and Dashboards
Dashboards are a critical tool in BI. They provide a visual representation of key metrics and
data, making it easier for decision-makers to track performance. Data visualization
techniques like charts, graphs, and heat maps help simplify complex data and make it more
digestible. However, designing effective dashboards that communicate the right information
clearly is a challenge.
7. Descriptive and Inferential Statistics
Statistics play a crucial role in Business Intelligence. Descriptive statistics (e.g., Mean,
Median, Mode) help summarize large datasets, while inferential statistics help make
predictions or draw conclusions based on sample data. Together, they provide valuable
insights that aid in decision-making and forecasting.
8. Decision Making in BI
BI supports decision-making in three main contexts: Certainty, Uncertainty, and Risk.
Decision-makers use BI tools to model and analyze scenarios under these conditions. For
example, decision trees and simulation models help visualize different outcomes and guide
decisions based on available data.
9. Data Mining and Predictive Analytics
Data mining involves analyzing large datasets to identify patterns, trends, and relationships.
Predictive analytics uses these patterns to forecast future events or behaviors. Together,
they help businesses anticipate changes and make proactive decisions.
10. Text Mining and Natural Language Processing (NLP)
Text mining extracts valuable information from unstructured text, such as customer
reviews or social media posts. NLP, a subset of text mining, deals with the complexities of
human language, like ambiguity and context, to extract insights. Text mining and NLP help
businesses understand customer sentiment and improve customer experiences.
11. Optimization and Sensitivity Analysis
Optimization techniques in BI help decision-makers find the best possible solution, given
certain constraints. Sensitivity analysis is used to understand how sensitive a decision is to
changes in certain variables. These techniques are used to simulate different scenarios and
make more informed decisions.
12. Web and Social Media Analytics
Web mining and web analytics analyze online data like website content, structure, and
usage patterns to improve online business performance. SEO (Search Engine Optimization)
is a key aspect of web analytics, helping businesses increase their online visibility. Social
media analytics tracks sentiments and customer behavior on platforms like Twitter and
Facebook.
