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Chapter 11: Business Intelligence and Knowledge Management. Oz (5th edition). Data Mining. Data warehouses are useless without software tools that process data into information Currently Decision Support Systems are called Business intelligence (BI) software

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data mining
Data Mining
  • Data warehouses are useless without software tools that process data into information
  • Currently Decision Support Systems are called Business intelligence (BI) software
  • BI software takes data and produces information useful for managerial decision-making
  • Data mining refers to the use of tools to extract information from a data warehouse; business intelligence is one result of data mining
data mining and data mining tools
Data-Mining and Data-Mining Tools
  • Data-mining is the process of selecting, exploring, and modeling large amounts of data to discover previously unknown relationships that support decision making.
  • Traditional data mining tools answer questions about variables that we think are related
    • Query languages (QBE or SQL)
    • Report generators
    • Multidimensional analysis tools (OLAP or pivot tables)
    • Standard statistical procedures (regression, ANOVA)
  • Knowledge discovery tools are data-mining tools for finding relationships that are not discernable to the human eye (see next slide);
typical data mining tasks related to knowledge discovery
Typical Data Mining Tasks Related to Knowledge Discovery
  • Clustering- this activity is designed to take a population of objects (e.g., customers) and develop characteristics that can be used to classify them. You start with no pre-defined classes. Often clustering is the first step in market segmentation.
  • Classification – examines the features of new objects and assigns them to one of a predetermined set of classes. Often preceded by clustering. Clustering could be used to determine characteristics of customers who respond to selected types of promotions. Customers in the same cluster get the same type of promotion material.
typical data mining tasks cont
Typical Data Mining Tasks (cont.)
  • Affinity grouping (market basket analysis)- This task is used to determine which things go together. Typically used to help in cross-selling (e.g., diapers and beer).
  • Prediction – used to determine patterns that can lead to predictable results. For example, customer churn or who will default on a loan. Amazon uses items purchased as gifts to predict the age range of recipients. Amazon uses your past purchases to determine what to offer you when you return to the Amazon site.
collecting data for the warehouse
Collecting Data for the Warehouse
  • Customer loyalty cards have multiple uses, but one use is to collect data for the data warehouse
  • Examples (see textbook for more details)
    • Grocery stores
    • Web sites
    • Harrah’s
    • Store related credit cards
  • Assurance of a steady flow of data
multidimensionality or olap
Multidimensionality or OLAP
  • Multidimensional data analysis (or OLAP) enables users to view data using various dimensions, measures and time frames (i. e., OLAP)
    • dimensions: products, business units, country, industry (e.g., categories)
    • measures: money, unit sales, head count, variances
    • time: daily, weekly, monthly, quarterly, yearly)
  • This type of analysis also provides the ability to view data in different ways (tables, charts, 3-D, geographically)
  • OLAP tools provide for this
  • Pivot tables in Excel or Access
characteristics of olap tools
Characteristics of OLAP Tools
  • Primarily used to exploit data warehouses
  • Provide extremely fast response
  • View combinations of two dimensions
  • Enable drilling down (start with broad info and get more specific)
  • Produces results as counts or percentages
  • Conversion of tables to charts/graphs
  • Usually requires a tailored-made relational database
  • OLAP applications are widely used by mid-level and upper level managers
  • A form of business intelligence software
other firms that use olap
Other Firms that Use OLAP
  • Office Depot
  • CVS
  • Ben & Jerry’s
customer relationship management crm systems
Customer Relationship Management (CRM) Systems
  • CRM systems are programs to learn more about customer’s needs and behaviors in order to develop stronger relationships with them.
  • Some sources of data for CRM systems
    • Data from Web user’s click stream (see example about in the textbook)
    • Data from the firm’s data warehouse
    • Data from the firm’s customer call centers
    • Data from the firm’s help line
    • Service and support records
    • Customer responses to ad campaigns
goals of crm systems
Goals of CRM Systems
  • CRM systems try to use technology to gain insight into the behavior of customers and the value of those customers. If CRM works as hoped, a business can:
    • provide better customer service
    • make call centers more efficient
    • cross sell products more effectively
    • help sales staff close deals faster
    • simplify marketing and sales processes
    • discover new customers
    • increase customer revenues
  • OLAP and other data mining tools are often available in CRM software
summary thoughts
Summary Thoughts
  • CRM software is concerned with data/information flows between firm and customers
  • Datamining is concerned with internal data/information flows from the data warehouse to managers (although data originates from external sources)
  • BI software is a more common term for software once called DSS
  • Current BI software focuses on Simon’s intelligence stage of decision making
  • Traditional DSS software focuses more on the design and choice stages of the Simon model
summary thoughts on bi
Summary Thoughts on BI
  • Much of BI concerns finding information about customers
  • Datamining and OLAP are often integrated into CRM systems
  • The Web is a popular way to gather BI
  • BI on customers promotes targeted marketing rather than mass marketing
  • Third parties often provide BI (e.g., Acxiom and DoubleClick)
  • Overzealous BI efforts are sources of moral and ethical issues
executive dashboards
Executive Dashboards
  • A dashboard is a common form of interface between BI tools and users
    • Resembles a car dashboard with clock like indicators and scales
    • Designed so users can quickly grasp business situations
knowledge management
Knowledge Management
  • What is knowledge?
    • Answer: Knowledge in an organization is the primarily the collective expertise/experiences of the organizations employees
  • Tacit versus explicit knowledge
    • Tacit knowledge is embedded in the human brain and cannot be expressed easily
    • Explicit knowledge is knowledge that exist outside the brain often in a text format
examples of explicit knowledge
Examples of Explicit Knowledge
  • Written descriptions of best practices for a business process
  • Written knowledge about products, markets, or customers
  • Lessons learned on projects or product development
  • Written records of experiences with new approaches
  • Examples of successful and failed projects (e.g., contracts, proposals, bids, etc)
knowledge management18
Knowledge Management
  • Capture employee knowledge
  • Transfer captured knowledge into a database
  • Filter and separate the most relevant knowledge
  • Organize the knowledge so that it is accessible to employees or “push” specific knowledge to employees based on pre-specified needs
examples of knowledge management systems
Examples of Knowledge Management Systems
  • Xerox built a Web-based maintenance knowledge base for field engineers who repair copiers
  • AT&T developed a “people finder” database that provides an on-line directory of “who knows what” (a knowledge directory)
  • HP has a Web-based site that provides knowledge about competitors, research, products, and customer satisfaction
  • Dow Chemical devised a system to manage its patents. To keep a patent enforced can cost up to $250,000. Dow needed to determine which patents had value. Saved over 40 million in 18 months
employee knowledge networks
Employee Knowledge Networks
  • Some tools direct employees to other employees
  • Expert can provide non-recorded expertise
  • No need to waste money hiring experts in every department
  • Learning from past mistakes saves money
  • Employee knowledge network facilitate knowledge sharing through intranets