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Master data management
- companies like R.R. Donnelley to eliminate outdated, incomplete or incorrectly
- formatted data.
- •Demonstrates IT’s role in
- successful data management.
- •Illustrates digital
- technology’s role in storing and organizing data.
- •Collection of related files containing
- records on people, places, or things.
- •Prior to digital databases, business
- used file cabinets with paper files.
- •Generalized category representing
- person, place, thing on which we store and maintain information
•E.g., SUPPLIER, PART
- •Specific characteristics of each
•SUPPLIER name, address
- description, unit price, supplier
- •Organize data into two-dimensional
- tables (relations) with columns and rows.
(columns) store data representing an attribute.
store data for separate records, or tuples
uniquely identifies each record
•One field in each table
•Cannot be duplicated
- •Provides unique identifier for all
- information in any row
Entity relationship diagram
- •Used to clarify table relationships in a relational
•Process of streamlining complex groups of data to:
•Minimize redundant data elements.
•Minimize awkward many-to-many relationships.
•Increase stability and flexibility.
- •Used by relational databases to ensure
- that relationships between coupled tables remain consistent.
- •E.g., when one table has a foreign key that points to another
- table, you may not add a record to the table with foreign key unless there is a
- corresponding record in the linked table.
Data definition capabilities
Specify structure of content of database
- •Automated or manual file storing
- definitions of data elements and their characteristics.
Querying and reporting
- •Data manipulation
•Structured query language (SQL)
•Microsoft Access query-building tools
- •Report generation,
- e.g., Crystal Reports
- •Database that stores current and
- historical data that may be of interest to decision makers
- •Consolidates and standardizes data from
- many systems, operational and transactional databases
•Data can be accessed but not altered
- •Subset of data warehouses that is
- highly focused and isolated for a specific population of users
- for consolidating, analyzing, and providing access to large amounts of data to
- improve decision making
Online Analytical Process (OLAP)
- •Supports multidimensional data
- analysis, enabling users to view the same data in different ways using multiple
- •Enables users to obtain online answers
- to ad hoc questions such as these in a fairly rapid amount of time
- •Finds hidden patterns and relationships in large databases
- and infers rules from them to predict future behavior
- linked to single event
Events linked over time
patterns describing a group an item belongs to
discovering as yet unclassified groupings
- series of values to forecast future values
One popular use of data mining?
- patterns in customer data for one-to-one marketing campaigns or for identifying
- profitable customers
- •Uses data mining techniques, historical data, and
- assumptions about future conditions to predict outcomes of events, such as the
- probability a customer will respond to an offer or purchase a specific product
- •Unstructured data (mostly text files) accounts for 80
- percent of an organization’s useful information.
- •Text mining allows businesses to extract key elements
- from, discover patterns in, and summarize large unstructured data sets.
- •Discovery and analysis of useful patterns and
- information from the Web
•Content mining, structure mining, usage mining
- •States organization’s rules for
- organizing, managing, storing, sharing information
- Responsible for specific policies and
- procedures through which data can be managed as a resource
Poor data quality
major obstacle to successful customer relationship management
Data quality problems
- Redudant and inconsistent data produced by multiple systems
- Data input errors
Data quality audit
structured survey of the accuracy and completeness of data
detects and corrects incorrect imcomplete, improperely formatted and redundant data