ISYS1000 Lecture Notes - Lecture 3: Intellectual Capital, Breakcore, Tacit Knowledge
29 May 2018
School
Department
Course
Professor
Module 3 – Data ad Kowledge
Maageet
LEARNING OUTCOMES
3.1 Managing Data
3.2 The database approach
3.3 Database management systems
3.4 Data warehouses and data marts
3.5 Knowledge management
3.1 MANAGING DATA
WHAT IS DATA/INFORMATION/KNOWLEDGE?
Data is raw (unprocessed) facts that have some relevancy (albeit unknown) to an individual
or organisation. Information is data that has been processed or given some structure that
brings meaning to an individual or organisation.
In the context of Information Systems we are trying to use IT/ICT to manage data,
information and knowledge.
DIFFICULTIES OF MANAGING DATA: DATA QUALITY
DQ/IQ Complex
Relates to data that is reliable, accurate, current, relevant etc.
Data quality
The amount of data available is increasing exponentially. Data comes from many sources,
can be scattered throughout organisations, collected by many individuals using multiple
methods and devices. Data can also be subject to data rot and degrade over time, in that it
can become outdated, or destroyed in storage media. Issues around data security, quality,
and integrity are critical, yet easily compromised. Due to non-integrated information
systems, it is thought that many organisations could be drowning in unstructured data.
Data governance
An approach to managing information across an entire organisation, involving a formal set
of business processes and policies designed to ensure that data is handled in a certain, well
defined fashion. The organisation follows unambiguous rules for creating, collecting,
handling and protecting its information. One strategy for implementing data governance is
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master data management. Master data is a set of core data, such as customer, product,
employee, vendor, geographic location and so on, that spans the enterprise information
systems.
3.2 THE DATABASE APPROACH
MINIMISING PROBLEMS
Databases minimise the following problems
Data redundancy
The same data is stored in many places
Data isolation
Applications cannot access data associated with other applications
Data inconsistency
Various copies of that data that do not agree
Data security
Keepig the orgaisatio’s data safe fro theft, odificatio, ad/or destructio
Data integrity
Data must meet constraints and be reliable
Data independence
Applications and data are independent of one another
DATABASE MANAGING SYSTEM (DBMS)
A specific type of software for creating, storing, organising and accessing data from a
database
e.g. In a university, different departments have access to different aspects of data, whatever
is relevant to them, distributed to them via a DBMS
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BUILDING DATABASES
Data Models
First step in building a database. A data model is a relatively simple representation of
complex real-world data structures. They are often graphical, and merely an abstraction of a
real-world object or event. They are useful in understanding complexities of the real-world
environment. It is also iterative and progressive.
Entity-Relationship (ER) Modelling
An entity is an object or thing (a person, a place, a thing, an event).
An attribute is a characteristic of an entity: An EMPLOYEE entity would be described by
attributes such as employee number, last name, salary and so on (fields).
Each entity is a table in the database and attributes are columns in each table.
A relationship describes an association among entities – how they relate to one another
(e.g. 1 agent serves many customers.)
Data models use three types of relationships:
- One-to-many (1:M or 1..*) relationship. A painter paints many paintings. Each
painting is painted by only one painter.
- Many-to-many (M:M or *..*) relationship. An employee may learn many job skills. A
job skill is learned by many employees.
- One-to-one (1:1 or 1..1) relationship. A manager manages one retail store. Each store
is managed by one manager.
A constraint is a restrictio placed o the data: e.g. a eployee’s salary ust hae alues
that are between 6,000 and 350,000 dollars.
Designing Databases with ER modelling
Conceptual Process (entities, primary attributes & relationships)
1. Identify entities
2. Identify relationships
3. Identify and associate primary/unique attributes of entities to make a primary key
Logical Process
4. Resolve Many-to-Many relationships (if required)
5. Design relational Constraints (determine, PKs, FKs)
Producing the Physical Database
6. Determine/add remaining attributes of each entity
7. Develop DDL statement of tables, columns and keys
8. Build physical database
9. Check for redundancy (i.e., normalise to 3NF)
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Document Summary
Data is raw (unprocessed) facts that have some relevancy (albeit unknown) to an individual or organisation. Information is data that has been processed or given some structure that brings meaning to an individual or organisation. In the context of information systems we are trying to use it/ict to manage data, information and knowledge. Relates to data that is reliable, accurate, current, relevant etc. The amount of data available is increasing exponentially. Data comes from many sources, can be scattered throughout organisations, collected by many individuals using multiple methods and devices. Data can also be subject to data rot and degrade over time, in that it can become outdated, or destroyed in storage media. Issues around data security, quality, and integrity are critical, yet easily compromised. Due to non-integrated information systems, it is thought that many organisations could be drowning in unstructured data.
