This document discusses business intelligence, analytics, data mining, and their relationships. It defines business intelligence as tools and techniques to analyze data and provide meaningful information for decision making. Data mining examines large datasets to discover patterns and hidden features. The document provides examples of market basket analysis, which finds purchasing patterns, and cluster analysis, which groups similar data together.
DATA MINING AND DATA WAREHOUSE
W.H. Inmon
OLAP, (On-line analytical processing)
OLTP, ( On-line transaction processing )
Data Cleaning
Data Integration
Data Selection
Data Transformation
Data warehouse vs Data Mining
Use in Urban Planning
DATA MINING AND DATA WAREHOUSE
W.H. Inmon
OLAP, (On-line analytical processing)
OLTP, ( On-line transaction processing )
Data Cleaning
Data Integration
Data Selection
Data Transformation
Data warehouse vs Data Mining
Use in Urban Planning
In today’s competitive world, every business has to fight huge competition to achieve success. So it is necessary for every business organization to collect large amount of information like employee’s data, Sales data, customer’s information, market analysis reports, etc.
Data Mining: What is Data Mining?
History
How data mining works?
Data Mining Techniques.
Data Mining Process.
(The Cross-Industry Standard Process)
Data Mining: Applications.
Advantages and Disadvantages of Data Mining.
Conclusion.
meaning of data warehousing
needs of data warehousing
applications of data warehousing
architecture of data warehousing
advantages of data warehousing
disadvantages of data warehousing.
meaning of data mining
needs of data mining
applications of data mining
architecture of data mining
advantages of data mining
disadvantages of data mining
This presentation covers data mining within artificial intelligence. Topics covered are as follows: motivation, synonym, process of data mining, operation of data mining, data mining techniques, business application, application selection, and current issues.
Data Mining is a set of method that applies to large and complex databases. This is to eliminate the randomness and discover the hidden pattern. As these data mining methods are almost always computationally intensive. We use data mining tools, methodologies, and theories for revealing patterns in data. There are too many driving forces present. And, this is the reason why data mining has become such an important area of study.
Big Data Analytics and a Chartered AccountantBharath Rao
Big Data Analytics is a growing field and currently being capitalized by many businesses. Businesses leverage on Big Data to gain a keen understanding of the Consumer Behavior and Market Understanding. Additionally Big Data can be used different fields such as Financial Audit, Control Assurance and Forensics.
This presentation is made to provide an insight regarding what opportunities reside for a Chartered Accountant in order to provide suitable value creation with regards to Big Data Analytics.
This presentation was made during my GMCS 2 Course at Mangalore branch of SIRC of ICAI and hence has limited number of slides.
In today’s competitive world, every business has to fight huge competition to achieve success. So it is necessary for every business organization to collect large amount of information like employee’s data, Sales data, customer’s information, market analysis reports, etc.
Data Mining: What is Data Mining?
History
How data mining works?
Data Mining Techniques.
Data Mining Process.
(The Cross-Industry Standard Process)
Data Mining: Applications.
Advantages and Disadvantages of Data Mining.
Conclusion.
meaning of data warehousing
needs of data warehousing
applications of data warehousing
architecture of data warehousing
advantages of data warehousing
disadvantages of data warehousing.
meaning of data mining
needs of data mining
applications of data mining
architecture of data mining
advantages of data mining
disadvantages of data mining
This presentation covers data mining within artificial intelligence. Topics covered are as follows: motivation, synonym, process of data mining, operation of data mining, data mining techniques, business application, application selection, and current issues.
Data Mining is a set of method that applies to large and complex databases. This is to eliminate the randomness and discover the hidden pattern. As these data mining methods are almost always computationally intensive. We use data mining tools, methodologies, and theories for revealing patterns in data. There are too many driving forces present. And, this is the reason why data mining has become such an important area of study.
Big Data Analytics and a Chartered AccountantBharath Rao
Big Data Analytics is a growing field and currently being capitalized by many businesses. Businesses leverage on Big Data to gain a keen understanding of the Consumer Behavior and Market Understanding. Additionally Big Data can be used different fields such as Financial Audit, Control Assurance and Forensics.
This presentation is made to provide an insight regarding what opportunities reside for a Chartered Accountant in order to provide suitable value creation with regards to Big Data Analytics.
This presentation was made during my GMCS 2 Course at Mangalore branch of SIRC of ICAI and hence has limited number of slides.
Business intelligence (BI) is a set of theories, methodologies, architectures, and technologies that transform raw data into meaningful and useful information for business purposes.
What is business intelligence and where it is applicable is described in this presentation. The subject is offered as elective to BE IT students of Pune University.
Warren Buffet would often think of companies as castles with a competitive moat protecting the business. Products or companies that figure out how to build and leverage differentiated data assets will be best positioned to win their respective markets. This talk describes the properties of a good data moat, why it matters, and how to go about building them within your organization.
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Talk presented at Kubernetes Community Day, New York, May 2024.
Technical summary of Multi-Cluster Kubernetes Networking architectures with focus on 4 key topics.
1) Key patterns for Multi-cluster architectures
2) Architectural comparison of several OSS/ CNCF projects to address these patterns
3) Evolution trends for the APIs of these projects
4) Some design recommendations & guidelines for adopting/ deploying these solutions.
ER(Entity Relationship) Diagram for online shopping - TAEHimani415946
https://bit.ly/3KACoyV
The ER diagram for the project is the foundation for the building of the database of the project. The properties, datatypes, and attributes are defined by the ER diagram.
1.Wireless Communication System_Wireless communication is a broad term that i...JeyaPerumal1
Wireless communication involves the transmission of information over a distance without the help of wires, cables or any other forms of electrical conductors.
Wireless communication is a broad term that incorporates all procedures and forms of connecting and communicating between two or more devices using a wireless signal through wireless communication technologies and devices.
Features of Wireless Communication
The evolution of wireless technology has brought many advancements with its effective features.
The transmitted distance can be anywhere between a few meters (for example, a television's remote control) and thousands of kilometers (for example, radio communication).
Wireless communication can be used for cellular telephony, wireless access to the internet, wireless home networking, and so on.
This 7-second Brain Wave Ritual Attracts Money To You.!nirahealhty
Discover the power of a simple 7-second brain wave ritual that can attract wealth and abundance into your life. By tapping into specific brain frequencies, this technique helps you manifest financial success effortlessly. Ready to transform your financial future? Try this powerful ritual and start attracting money today!
2. Learning Objectives
Upon successful completion of this chapter, you
will be able to:
• Explain the difference between BI, Analytics,
Data Marts and Big Data.
• Define the characteristics of data for good
decision making.
• Describe what Data Mining is.
• Explain market basket and
cluster analysis.
3. Business Analytics, BI, Big Data, Data
Mining - What’s the difference?
• Business Analytics – Tools to explore past data
to gain insight into future business decisions.
• BI – Tools and techniques to turn data into
meaningful information.
• Big Data –data sets that are so large or
complex that traditional data processing
applications are inadequate.
• Data Mining - Tools for discovering
patterns in large data sets.
4. Textbook
• Making the Most of Big Data,
Kandasamy & Benson, 2013
• Free download from Bookboon.com
• Bookboon’s business model:
– Free download
– Books have advertisements
– Pay monthly fee to remove ads
5. Businesses Need Support for
Decision Making
• Uncertain economics
• Rapidly changing environments
• Global competition
• Demanding customers
• Taking advantage of information acquired by
companies is a Critical Success Factor.
7. The Information Gap
• The shortfall between gathering information
and using it for decision making.
– Firms have inadequate data warehouses.
– Business Analysts spend 2 days a week gathering
and formatting data, instead of performing
analysis. (Data Warehousing Institute).
– Business Intelligence (BI) seeks to bridge the
information gap.
8. Data Mining
• “Data mining is an interdisciplinary subfield of
computer science. It is the computational process of
discovering patterns in large data sets involving
methods at the intersection of artificial intelligence,
machine learning, statistics, and database systems.” -
Wikipedia
• Examining large databases to produce new
information.
– Uses statistical methods and artificial intelligence to
analyze data.
– Finds hidden features of the data that were not yet known.
9. BI
• Tools and techniques to turn data into
meaningful information.
– Process: Methods used by the organization to turn
data into knowledge.
– Product: Information that allows businesses to
make decisions.
10. BI Applications
• Customer Analytics
• Human Capital Productivity Analysis
• Business Productivity Analytics
• Sales Channel Analytics
• Supply Chain Analytics
• Behavior Analytics
11. What is Business Intelligence?
• Collecting and refining information from many
sources (internal and external)
• Analyzing and presenting the information in
useful ways (dashboards, visualizations)
• So that people can make better decisions
• That help build and retain competitive
advantage.
14. BI Applications
• Customer Analytics
• Human Capital Productivity Analysis
• Business Productivity Analytics
• Sales Channel Analytics
• Supply Chain Analytics
• Behavior Analytics
15. BI Initiatives
• 70% of senior executives report that analytics will
be important for competitive advantage. Only 2%
feel that they’ve achieved competitive advantage.
(zassociates report)
• 70-80% of BI projects fail because of poor
communication and not understanding what to
ask. (Goodwin, 2010)
• 60-70% of BI projects fail because of technology,
culture and lack of infrastructure (Lapu, 2007)
18. Data Warehouse
• Collection of data
from multiple
sources (internal
and external)
• Summary, historical and raw data from
operations.
• Data “cleaning” before use.
• Stored independently from
operational data.
• Broken down into DataMarts for
use.
Chapter 4 of ISBB Text
19. 5 Tasks of Data Mining in Business
• Classification – Categorizing data into
actionable groups. (ex. loan applicants)
• Estimation – Response rates, probabilities of
responses.
• Prediction – Predicting customer behavior.
• Affinity Grouping – What items or services are
customers likely to purchase together?
• Description – Finding interesting patterns.
20. Data Mining Techniques
• Market Basket Analysis
• Cluster Analysis
• Decision Trees and Rule Induction
• Neural Networks
21. Market Basket Analysis
• Finding patterns or sequences in the way that
people purchase products and services.
• Walmart Analytics
– Obvious: People who buy Gin also buy tonic.
– Non-obvious: Men who bought diapers would also
purchase beer.
22. Cluster Analysis
• Grouping data into like clusters based on
specific attributes.
• Examples
– Crime map clusters to better deploy police.
– Where to build a cellular tower.
– Outbreaks of Zika virus.
23. Summary
• Explained BI, Analytics, Data Marts and Big
Data.
• Defined the characteristics of data for good
decision making.
• Described data mining in detail.
• Explained and gave examples of
market basket and cluster analysis.
