Chapter 11: Business Intelligence and Knowledge Management
Data Mining and Online Analysis
* Data warehouses are useless without software tools
* Process data into information
* Business intelligence (BI): information gleaned with information tools Data Mining
* Data mining: selecting, exploring, and modeling data
* Supports decision making
* Finds relationships and ratios within data
* Finds unknown relationships
* Queries are more complex than traditional
* Combination of data-warehouse and data-mining facilitates predictions * Data mining has four objectives
* Sequence or path analysis
* Techniques applied to various fields
* Fraud detection
* Marketing to individual
* Data mining can predict customer behavior
* Find profitable customers
* Find patterns of fraud
* Mobile phones
* Customers tend to switch companies often
* Customer loyalty programs ensure steady flow of customer data Potential Applications of Data Mining
Data Mining Application| Description|
Consumer clustering | Identify the common characteristics of consumers who tend to buy the same products and services from your company.| Costumer churn | Identify the reason customers switch to competitors; predict which customers are likely to do so.| Fraud detection | Identify characteristics of transactions that are most likely to be fraudulent.| Direct marketing | Identify which prospective clients should be included in a mailing or e – mail list to obtain the highest response rate.| Interactive marketing| Predict what each individual accessing a web site is most likely to be interested in seeing.| Market basket analysis| Understand what products or services are commonly purchased together, and on what days of the week.| Trend analysis | Reveal the difference between a typical customer this month and a typical customer last month.| * Utilizing loyalty programs
* Frequent flier
* Consumer clubs
* Amass huge amount of data about customer
* Harrah’s Entertainment Inc.
* Uses data mining to discern big spenders
* Allows sales agents to charge big spenders less money *
* Inferring demographics
* Predict what customers likely to purchase in future
* Age ranges estimated from purchase history
* Advertises for appropriate age group
* Anticipates holidays
Online Analytical Processing
* Online analytical processing (OLAP): application to exploit data warehouses *
* Extremely fast response
* View combinations of two dimensions
* Drilling down: start with broad info and get more specific * Can receive info in numbers or percentages
* Uses specifically tailored data or relational database *
* OLAP application composes tables immediately
* Dimensional database: data organized into tables
* Tables show information in summaries
* Companies sell multidimensional database packages
* OLAP applications are powerful tools for executives
* Ruby Tuesday restaurant chain case
* One location was performing below average
* Customers were waiting longer than normal
* Appropriate changes were made
* OLAP applications installed on special server
* OLAP faster than relational applications
* OLAP increasingly used by corporations
* Office Depot used OLAP on data warehouse
* CVS let 2,000 employees run analyses
* Ben & Jerry’s track ice cream popularity
* BI software becoming easier to use
* Intelligent interfaces
More Customer Intelligence
...Foundation of BusinessIntelligence: Databases and
Course: Computer and Information Management (CIS-501)
Program : MBA
Aziza Md. Aziz
Md. Azizul Hasan
Mr. Mohammad Raisul Alam
Chairman and Assistant Professor
Faculty of Engineering and Applied Science
Table of Content
Problems of Managing Data resources in a traditional file environment and how they are solved by data management system
Major capabilities of database management systems (DBMS) and the reason of Relational database becoming so powerful
Important principles of database designing
Principal tools for accessing information from database to improve business performance and decision making
Reasons of Information policy, data administration and data quality assurance essential for managing a firm’s data resources
Database systems are the information heart of modern enterprises, where they are used for processing business transactions and for understanding and managing the enterprise. Businessintelligence is the analysis of data to...
...In today’s business environment, every organization has developed a unique ways of gaining a competitive advantage over its rival and these unique ways can be furthermore described as a business process. An effective business process can be adequately administered by the use of certain software tools and systems known as BusinessIntelligence (BI).
BusinessIntelligence represents the tools and systems that play a key role in strategic planning of an Organization. These tools are used to gather, store, access and analyze corporate data for better decision making, cut cost, more profits and stability through improved efficiency.
A company may illustrate businessintelligence in the areas of customer profiling, customer support, market research, market segmentation, product profitability, statistical analysis, inventory and distribution analysis and so more. In this academic paper, I will explicitly elaborate on these areas of businessintelligence used by today’s companies, its implementation, benefits and possible or potential problems.
BusinessIntelligence tools analyze raw data into meaningful information by the help of the management for future decision making. The management devises special approaches such as data/text mining, data warehousing, online...
...Data mining and OLAP are the most common BusinessIntelligence technologies. The term BusinessIntelligence refers to computer based methods to identify and extract useful information from business data. Online Analytical Processing commonly known as OLAP provides summary data and generates rich calculations. OLAP is a class of systems that provide answers to multidimensional queries. OLAP is typically used inbusiness reporting for sales, marketing and various such domains. OLAP enables the users to view the data interactively from multiple perspectives.
On the other hand, Data mining helps discover hidden pattern or trend in data to support a conclusion. As the name suggests, unlike OLAP that operates at a summary view, Data mining operates at detail level. For instance, if walmart would like to identify the trend of products sold during a holiday season, data mining would help them answer that question based on historic data.
Although, OLAP and data mining operate on data to gain intelligence, the main difference lies on how they operate on the data. OLAP tools provide multidimensional data analysis and summaries of the data. On the contrary data mining focuses on ratios, patterns and influences in the set of data. OLAP and data mining can complement each other. OLAP might point out problems with sales of a specific product for walmart for this month in particular...
...1) Identify and define the Big 5 personality dimensions. Which of these are related to job performance? Describe how you would rate yourself on each of these dimensions, and explain why.
The Big 5 personality factors are:
Extroversion: how outgoing, positive, talkative, assertive, sociable a person is.
Agreeableness: A tendency to be compassionate and cooperative rather than suspicious and antagonistic towards others.
Conscientiousness: A tendency to be organized and dependable, self-discipline, achievement-oriented, and prefer planned rather than spontaneous behavior.
Emotional stability: how relaxed, secure and unworried one is.
Openness to experience: reflects the degree of intellectual curiosity, creativity and a preference for novelty and variety a person has.
Among these Extroversion and Conscientiousness are related to job performance because Extroversion has been associated with success for managers and salespeople, and Conscientiousness has the strongest positive correlation with job performance and training performance. I am working in the fast-paced environment so I think I have tendency of conscientiousness because I am always follow a schedule, organized, accurate, pay attention to details, dependable and responsible.
2) Define the four types of distortion in perception. Provide an example of each
Stereotyping is a set of beliefs about the characteristics of a group of people.
Example: Politicians are considered as manipulative and corrupt. Women...
Cloud computing is the technology which gives efficiency to an organization by efficient use of the computing resources. Tamer, C., Kiley, M., Ashrafi, N., & Kuilboer, J. (2013) said cloud computing gives its services as Infrastructure as a service (IaaS), Platform as a service (PaaS) and Software as a service (SaaS). Businessintelligence (BI) makes organization intelligent and stand firmly in the competitive market by delivering the right information at the right time. Mixing of the cloud computing with business intelligent makes BI affordable and easily available. Cloud computing allows to access the businessintelligence applications easily and the other advantages are deployment speed, scalability, elasticity and accessibility. Many vendors are looking forward to develop strategies and solutions. There are many challenges to overcome when migrating from traditional BI to cloud BI. The cloud businessintelligence influences the operational and financial factors of the organization and it plays an important role in times of economic crisis in the market. The cloud BI is adopting slowly around the world and technology is going to be a trend in future.
Key Words: Cloud computing, Businessintelligence, agility, cloud strategy,...
for Social CRM Systems
Adam Czyszczoń and Aleksander Zgrzywa
Politechnika Wrocławska, Faculty of Computer Science and Management,
Institute of Informatics,
Wybrzeże Wyspiańskiego 27, 50370 Wrocław, Poland
Abstract. This paper attempts to address the problem of the automatic customer segmentation by processing data collected in Social Customer RelationshipManagement (Social CRM) systems using Kohonen
networks. Presented segmentation approach comprises classic loyaltyproﬁtability link model that is explicit for CRM, and new social media
components direct to Social CRM. The result of presented approach is an
analysis tool with data visualization for managers which signiﬁcantly improves the process of customer segmentation. Presented research is supported by implementation of proposed approach by which experiments
were conducted. Additionally, the experimental results showed that proposed method performed very close to k-means algorithm which indicate
the correctness of the proposed approach.
Keywords: customer segmentation, CRM, Social CRM, clusterization,
SOM, unsupervised learning, ANN, data mining.
To acquire competitive advantage many companies use the strategy of Customer
Relationship Management (CRM) what can be observed in growing interest
in this domain. However, in recent years new...
...Current business and technology conditions that complicate effective application of business analytics to businessintelligence and knowledgemanagement data, and the prospects for improvement
Businesses have collected data for many years. Most of the data they collect has been for historical purposes, such as how much of an item has sold and what are the profits gained from those sales.Businessintelligence allows one to take that data, manipulate as you see fit and generate reports. The data then has to be extracted and trends analyzed so that businesses can find more opportunities and new customer segments. This is known as business analytics. However that may sound simple, there are current business and technology conditions that complicate the effective application of business analytics to businessintelligence and knowledgemanagement data. I will discuss some of these conditions as well as some of the prospects for improvement.
Businesses have come to realize that the years of data that they have been collecting is of value and have launched major businessintelligence initiatives in their organizations. However, many businesses are making a huge mistake in how they are handling the data and fail to realize that data does...
• Data mining (knowledge discovery in databases):
– Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from data in large databases
• Data mining helps end users extract useful business information from large databases
• Data mining is the exploration and analysis of large quantities of data in order to discover meaningful patterns and rules. • The goal of data mining may be to allow a corporation to improve its marketing, sales, and customer support operations through a better understanding of its customers.
Intro to Data Mining
• The non-trivial extraction of novel, implicit, and actionable knowledge from large datasets.
– – – – Extremely large datasets Discovery of the non-obvious Useful knowledge that can improve processes Can not be done manually
• Technology to enable data exploration, data analysis, and data visualisation of very large databases at a high level of abstraction, without a specific hypothesis in mind.
Lecture 8 3 Intro to Data Mining
What is Data Mining and its purpose?
• Search for relationships and global patterns that exist in large databases but are hidden in the vast amounts of data. • Analyst combines knowledge of data and machine learning technologies to discover nuggets of...