Chapter 6 Notes

Data, Information, and Business Intelligence

Section 6.1: Data, Information, and Databases

• Data: Everywhere in an organization and comes in different levels, formats, and granularities.

  • Employees must be able to analyze organizational data to make decisions.

  • Collecting, compiling, sorting, and analyzing data provides insight into organizational performance.

• Data Type: Transactional and Analytical

  • Transactional Data: Data within a single business process or unit of work that supports daily operational tasks.

  • Analytical Data: Encompasses all organizational data, supporting managerial analysis tasks.

• Data Timeliness

  • Real-time data: Immediate, up-to-date data.

  • Real-time system: Provides real-time data in response to requests.

• Data Quality

  • Business decisions are only as good as the quality of the data used to make them.

  • Data inconsistency: Occurs when the same data element has different values.

  • Data integrity issues: Occur when a system produces incorrect, inconsistent, or duplicate data.

• Costs of low-quality data:

  • Inability to accurately track customers, identify valuable customers, identify selling opportunities, marketing to nonexistent customers.

  • Difficulty tracking revenue and building strong customer relationships.

• Benefits of Good Data: High quality data can significantly improve the chances of making a good decision, which can directly impact an organization's bottom line.

  • Data steward: Responsible for ensuring data policies and procedures are implemented across an organization.

• Data Governance

  • Data governance: Overall management of the availability, usability, integrity, and security of company data.

  • Master data management (MDM): Gathering data and ensuring it is uniform, accurate, consistent, and complete.

    • Includes entities such as customers, suppliers, products, sales, employees, and other critical entities.

  • Data validation: Tests and evaluations used to determine compliance with data governance policies.

Storing Data in a Relational Database

• Database: Maintains data about various types of objects (inventory), events (transactions), people (employees), and places (warehouses).

• Database management systems (DBMS): Allows users to create, read, update, and delete data in a relational database.

• Data element: The smallest or basic unit of data.

• Data model: Logical data structures that detail the relationships among data elements using graphics or pictures.

• Metadata: Details about data.

• Data dictionary: Compiles all of the metadata about the data elements in the data model.

Storing Data Elements in Entities and Attributes

• Entity: A person, place, thing, transaction, or event about which data is stored.

  • Rows in a table contain entities.

• Attribute (field, column): The data elements associated with an entity.

  • Columns in each table contain the attributes.

• Record: A collection of related data elements.

Creating Relationships Through Keys

• Primary keys and foreign keys identify the various entities (tables) in the database.

• Primary key: A field (or group of fields) that uniquely identifies a given entity in a table.

• Foreign key: A primary key of one table that appears as an attribute in another table and acts to provide a logical relationship among the two tables.

Using a Relational Database for Business Advantages

• Increased Flexibility

  • Handle changes quickly and easily.

  • Provide users with different views.

  • Physical view: Deals with the physical storage of data on a storage device.

  • Logical views: Focuses on how individual users logically access data to meet their own particular business needs.

• Increased Scalability and Performance

  • Scalability: Refers to how well a system can adapt to increased demands

  • Performance: Measures how quickly a system performs a certain process or transaction.

• Reduced Data Redundancy

  • Data redundancy: The duplication of data or storing the same data in multiple places.

    • Inconsistency is one of the primary problems with redundant data.

• Increased Data Integrity (Quality)

  • Data integrity: Measures the quality of data.

  • Integrity constraint: Rules that help ensure the quality of data.

    • Relational integrity constraint.

    • Business-critical integrity constraint.

• Increased Data Security

  • Password: Provides authentication of the user.

  • Access level: Determines who has access to the different types of data.

  • Access control: Determines types of user access, such as read-only access.

Section 6.2: Business Intelligence

• Organizational data is difficult to access and contains structured data in databases, and unstructured data such as voice mail, phone calls, text messages, and video clips.

• Data Rich, Information Poor: Many organizations find themselves in the position of being data rich and information poor.

The Solution: Data Aggregation

• BI enables business users to receive data for analysis that is: reliable, consistent, understandable, easily manipulated.

Data Warehouse

• Data warehouses extend the transformation of data into information.

• Data warehouse provided the ability to support decision making without disrupting the day-to-day operations.

• Data warehouse: A logical collection of data – gathered from many different operational databases – that supports business analysis activities and decision-making tasks.

  • The primary purpose of a data warehouse is to aggregate data throughout an organization into a single repository for decision-making purposes.

Reasons business analysis is difficult from operational systems:

• Inconsistent Data Definitions, Lack of Data Standards, Poor Data Quality, Inadequate Data Usefulness, Ineffective Direct Data Access.

  • Data Aggregation: Collection of data from various sources for the purpose of data processing.

  • Extraction, transformation, and loading (ETL): A process that extracts data from internal and external databases, transforms the data using a common set of enterprise definitions, and loads the data into a data warehouse.

  • Data mart: Contains a subset of data warehouse data.

• Data lake: A storage repository that holds a vast amount of raw data in its original format until the business needs it.

Data Cleansing or Scrubbing

• An organization must maintain high-quality data in the data warehouse.

• Dirty data: Erroneous or flawed data.

• Data cleansing or scrubbing: A process that weeds out and fixes or discards inconsistent, incorrect, or incomplete data.

• Data Visualization: Describes technologies that allow users to “see” or visualize data to transform data into a business perspective.

  • Data visualization tools: Move beyond Excel graphs and charts into sophisticated analysis techniques such as pie charts, controls, instruments, maps, time-series graphs, and more.