Managing Knowledge and Data
Managing Data
Difficulties in managing data:
• Amount of data increasing exponentially
• Data are scattered throughout organizations and collected by many individuals
using various methods and devices.
• Data come from many sources.
• Data security, quality, and integrity are critical.
• Data degrade over time;
• Data subject to data rot;
• Data security, quality, and integrity are critical, yet easily jeopardized;
• Information systems that do not communicate with each other can result in inconsistent data;
• Federal regulations.
Data Governance
Data governance is an approach to managing information across an entire organization.
Master data management is a process that spans all of an organization’s business processes
and applications.
Master data are a set of core data that span all of an enterprise’s information systems.
The Database Approach
Database management system (DBMS) minimize the following problems:
Data redundancy: The same data are stored in many places.
Data isolation: Applications cannot access data associated with other applications.
Data inconsistency: Various copies of the data do not agree.
DBMSs maximize the following issues:
Data security: Keeping the organization’s data safe from theft, modification,
and/or destruction.
Data integrity: Data must meet constraints (e.g., student grade point averages
cannot be negative).
Data independence: Applications and data are independent of one another.
applications and data are not linked to each other, meaning that
applications are able to access the same data.
Data Hierarchy
A bit is a binary digit, or a “0” or a “1”.
A byte is eight bits and represents a single character (e.g., a letter, number or symbol).
A field is a group of logically related characters (e.g., a word, small group of words,
or identification number).
A record is a group of logically related fields (e.g., student in a university database).
A file is a group of logically related records.
A database is a group of logically related files.
Designing the Database
The data model is a diagram that represents the entities in the database and their relationships.
An entity is a person, place, thing, or event about which information is maintained.
A record generally describes an entity.
An attribute is a particular characteristic or quality of a particular entity.
The primary key is a field that uniquely identifies a record.
Secondary keys are other field that have some identifying information but typically do not
identify the file with complete accuracy.
Entity-Relationship Modeling
Database designers plan the database design in a process called entity-relationship (ER) modeling.
ER diagrams consists of entities, attributes and relationships.
Entity classes are groups of entities of a certain type.
An instance of an entity class is the representation of a particular entity.
Entity instances have identifiers, which are attributes that are unique to that entity instance.
Database Management Systems
A database management system is a set of programs that provide users with tools to add,
delete, access, and analyze data stored in one location.
The relational database model is based on the concept of two-dimensional tables.
Structured query language allows users to perform complicated searches by using
relatively simple statements or keywords.
Query by example allows users to fill out a grid or template to construct a sample or
description of the data he or she wants.
Normalization
Normalization is a method for analyzing and reducing a relational database to its most
streamlined form for minimum redundancy, maximum data integrity, and best processing
performance.
Data Warehousing and Data Marts
A data warehouse is a repository of historical data organized by subject to support
decision makers in the organization.
Historical data in data warehouses can be used for identifying trends, forecasting, and making
comparisons over time.
Online analytical processing (OLAP) involves the analysis of accumulated data by end users
(usually in a data warehouse).
In contrast to OLAP, online transaction processing (OLTP) typically involves a database, where
data from business transactions are processed online as soon as they occur.
Benefits of Data Warehousing
End users can access data quickly and easily via Web browsers because they are located in one place.
End users can conduct extensive analysis with data in ways that may not have been possible before.
End users have a consolidated view of organizational data.
Knowledge Management
Knowledge management is a process that helps organizations manipulate important
knowledge that is part of the organization’s memory, usually in an unstructured format.
Knowledge that is contextual, relevant, and actionable.
Intellectual capital is another term often used for knowledge.
Explicit knowledge: objective, rational, technical knowledge that has been documented.
Examples: policies, procedural guides, reports, products, strategies, goals, core competencies
Tacit knowledge: cumulative store of subjective or experiential learning.
Examples: experiences, insights, expertise, know-how, trade secrets, understanding,
skill sets, and learning
Knowledge management systems refer to the use of information technologies to systematize,
enhance, and expedite intrafirm and interfirm knowledge management.
Best practices are the most effective and efficient ways of doing things.
Knowledge Management System Cycle