Refers to the extent of detail within the information
Fig 6.2 Four primary traits of the value of information
Fig 6.3 Transactional vs Analytical information
Means immediate, up-to-date information
Provide real-time information in response to requests
Information inconsistency
Occurs when the same data element has different values
Information integrity issues
Occur when a system produces incorrect, inconsistent, or duplicate data
Fig 6.4 Five common characteristics of high quality information
Occurs when a company examines its data to determine if it can meet business expectations, when or identifying possible data gaps or where missing data might exist
The management and oversight of an organization’s data assets to help provide business users with high quality data that is easily accessible in a consistent manner
Responsible for ensuring the policies and procedures are implemented across the organization and act as a liaison between the MIS department and the business
Refers to the overall management of the availability m, usability, integrity, and security of company data
Master data management (MDM)
Practice of gathering data and ensuring that it is uniform, accurate, consistent, and complete, including such entities as customers, suppliers, products, sales, employees, and other critical entities that are commonly integrated across organizational systems
Includes the tests and evaluations used to determines compliance with data governance policies to endure correctness of data
Maintains information about various types of objects(inventory), events (transactions), people (employees) and places (warehouses)
Database management system (DBMS)
Creates, reads, updates, and deletes data in a database while controlling access and security
Query-by-example (QBE) tool
Helps users graphically design the answer to a question against a database
Structured query language (SQL)
Asks users to write lines of code to answer questions against a database
Fig 6.6 relationship of database, DBMS and user
Data element or data field
Smallest or basic unit of information
Logical data structures that detail the relationships among data elements using graphics or pictures
Provides detail about data
Complies all of the metadata about the data elements in the data model
Relational database model
Stores information in the form of logically related two-dimensional tables
Relational database management system
Allow users to create, read, update, and delete data in a relational database
Stores information about a person, place, thing, transaction or event
Attributes (columns or fields)
Are the data elements associated with entity
Collection of related data elements
A field that uniquely identifies a given record in a table
Primary key of one table that appears as an attribute in another table and axes to provide a logical relationship between the two tables
Fig 6.9 Business advantages of a relational database
Physical view of information
Deals with the physical storage of information on a storage device
Logical view of information
Focuses on how individual users logically access information to meet their own particular business needs
Time it takes for data to be stored or retrieved
Duplication of data, or the storage of the same data in multiple places
Measure of the quality of information
Rules that help ensure the quality of information
Relational integrity constraints
Rules that before basic and fundamental information-based constraints
Defines how a company performs certain aspects of its business and typically results in either a yes/no or true/false answer
Business-critical integrity constraints
Enforce business rules vital to an organization’s success and often require more insight and knowledge then relational integrity constraints
Broad administrative area that deals with identifying individuals in a system and controlling their access to resources within that system by associating user rights and restrictions with the established identity
Person responsible for creating the original website content
Person responsible for updating and maintaining website content
Includes fixed data incapable of change in the event of a user action
Includes data that change based on user actions
An area of a website that Stira information about products in a database
Interactive website kept constantly updated and relevant to the needs of its customers using a database
Fig 6.12 reasons business analysis is difficult from operational databases
Central location in which data is stored and managed
Logical collection of information—gathered from many different operational databases — that supports business analysis activities and decision-making tasks
Fig 6.13 Data Warehousing components
Collection of data from various sources for the purpose of data processing
Extraction m, transformation, and loading (ETL)
Process that extracts information from internal to external databases, transforms the information using a common set of enterprise definitions
Contains a subset of data warehouse information
Fig 6.14 Model of a typical data warehouse
Fig 6.15 Dirty Data Problems
Information cleansing or scrubbing
Process that weeds out and fixes or discards inconsistent, incorrect, or incomplete information
Fig 6.16 Contact information in operational systems
Fig 6.19 Accurate and Complete information
Individual item on a graph or chart
Business that collects personal information about consumers and sells that information to other organizations
Storage that holds a vast amount of raw data in its original format until the business needs it
Identifies the primary location where data is collected
Organized collection of data
Compares two or more data sets to identify patterns and trends
Where a company keeps tabs of its competitor’s activities on the web using software that automatically tracks all competitor website
Technique for establishing a match, or balance, between the source data and the target data warehouse
Data-driven decision management
Approach to business governance that values decisions that can be backed up with verifiable data
Fig 6.21 four common characteristics of big data
Processes and manages algorithms across many machines in a computing environment
Fig 6.26 Business focus area of big data
Process of analyzing data to extract information not offered by the raw data alone
Fig 6.27 data mining process model overview
Process of collecting statistics and information about data in an existing source
Process of sharing information to ensure consistency between multiple data sources
Data-mining algorithm that analyzes a customer’s purchases and actions on a website and then uses the data to recommend complementary products
Fig 6.29 data mining techniques
Determines values for an unknown continuous variable behavior or estimated future value
Affinity grouping analysis
Reveals the relationship between variables along with the nature and frequency of the relationships ; determines which things go together
Evaluates such items as websites and checkout scanner information to detect customers’ buying behavior and predict future behavior by identifying affinities among customers’ choices of products and services
Technique used to divide information set into mutually exclusive groups such that the members of each group are as close together as possible to one another and the different groups are as far as possible
Process of organizing data into categories or groups for its most effective and efficient use : assigns records to one of a predefined set of classes
Use a variety of techniques to find patterns and relationships in large volumes of information that predict future behavior and guide decision making
Statement about what will happen or might happen in the future
Fig 6.33 data mining modeling techniques for predictions
Common term for the representation of multidimensional information
Science of fact-based decision making
Note: algorithms in last chapter
Process of identifying rare or unexpected items or events in data set that do not conform to other items in the data set
Data value that is numerically distant from most of the other data points In a set of data
Application of big data analytics to smaller data sets in a near-real or real-time in order to solve a problem or create business value
Extracts knowledge from data by performing statistical analysis, data mining and advanced analytics on big data to identify trends, market changes, and other relevant information
Business analytics specialist who uses visual tools to help understand complex data
Occurs when the user goes into an emotional state of over-analysis (overthinking) a situation so that a decision or action is never takin in effect paralyzing the outcome
Describe technologies that allow users to see or visualize data to transform information into a business perspective
Move beyond excel graphs and charts into sophisticated analysis techniques such as controls, instruments, maps, Time-series graphs, and more
Business intelligence dashboards
Track corporate metrics such as critical success factors and key performance indicators and include advanced capabilities such as interactive controls, allowing users to manipulate data for analysis