1. Data and Knowledge Management5
Data and Knowledge Management

2. Discuss ways that common challenges in managing data can be addressed using data governance.
Define Big Data, and discuss its basic characteristics.
Explain how to interpret the relationships depicted in an entity-relationship diagram.
Discuss the advantages and disadvantages of relational databases.
Explain the elements necessary to successfully implement and maintain data warehouses.
Describe the benefi ts and challenges of implementing knowledge management systems in organizations.

3. Managing Data
Big Data
The Database Approach
Database Management Systems
Data Warehouses and Data Marts
Knowledge Management

4. [ Opening Case Tapping the Power of Big Data ]
What We Learned from This Case

5. 5.1
Rollins Automotive

6. 5.1
Managing Data
The Difficulties of Managing Data
Data Governance

7. Difficulties in Managing Data
Data increases exponentially with time
Multiple sources of data
Data rot, or data degradation
Data security, quality, and integrity
Government Regulation

8. Multiple Sources of Data
Internal Sources
Corporate databases, company documents
Personal Sources
Personal thoughts, opinions, experiences
External Sources
Commercial databases, government reports, and corporate Web sites.

9. New York City Opens Its Data to All
5.2
New York City Opens Its Data to All

10. Data Governance
An approach to managing information across an entire organization.
Master Data
Master Data Management

11. 5.2 Big Data Defining Big Data Characteristics of Big Data
Managing Big Data
Leveraging Big Data

12. Defining Big Data Big data is difficult to define
Two Descriptions of Big Data

13. From Gartner Research (Big Data Description 1 of 2)
Diverse, high-volume, high-velocity information assets that require new forms of processing to enable enhanced decision making, insight discovery, and process optimization. (

14. From the Bid Data Institute (Big Data Description 2 of 2)
Exhibit variety
Includes structured, unstructured, and semi-structured data
Are generated at high velocity with an uncertain pattern
Do not fit neatly into traditional, structured, relational databases
Can be captured, processed, transformed, and analyzed in a reasonable amount of time only by sophisticated information systems. (

15. Defining Big Data Big Data Generally Consist of:
Traditional enterprise data
Machine-generated/sensor data
Social Data
Images captured by billions of devices located around the world
Digital cameras, camera phones, medical scanners, and security cameras

16. Characteristics of Big Data
Volume
Velocity
Variety

17. Managing Big Data
When properly analyzed big data can reveal valuable patterns and information.
Database environment
Traditional relational databases versus NoSQL databases
Open source solutions

18. Leveraging Big Data Creating Transparency Enabling Experimentation
Segmenting Population to Customize Actions
Replacing/Supporting Human Decision Making with Automated Algorithms
Innovating New Business Models, Products, and Services
Organizations Can Analyze Far More Data

19. 5.3
The Database Approach
The Data Hierarchy
Designing the Database

20. Databases Minimize Three Main Problems
Data Redundancy
Data Isolation
Data Inconsistency

21. Databases Maximize the Following
Data Security
Data Integrity
Data Independence

22. Data Hierarchy
Bit
Byte
Field
Data File or Table
Database

23. Designing the Database
Key Terms
Data Model
Entity
Instance
Attribute
Primary Key
Secondary Keys

24. Designing the Database
Entity-Relationship Modeling
Entity-Relationship Diagram
Cardinality
Modality

25. Database Management Systems
5.4
Database Management Systems
The Relational Database Model
Databases in Action

26. The Relational Database Model
Based on the concept of two-dimensional tables
Database Management System (DBMS)
Query Languages
Data Dictionary
Normalization

27. Database Solution for the German Aerospace Center
5.3
Database Solution for the German Aerospace Center

28. Data Warehouses and Data Marts
5.5
Data Warehouses and Data Marts
Describing Data Warehouses and Data Marts
A Generic Data Warehouse Environment

29. Describing Data Warehouses & Data Marts
A repository of historical data that are organized by subject to support decision makers in the organization
Data Mart
A low-cost, scaled-down version of a data warehouse designed for end-user needs in a strategic business unit (SBU) or individual department.

30. Describing Data Warehouses & Data Marts
Basic characteristics of data warehouses and data marts
Organized by business dimension or subject
Use online analytical processing (OLAP)
Integrated
Time variant
Nonvolatile
Multidimensional

31. A Generic Data Warehouse Environment
Source Systems
Data Integration
Storing the Data
Metadata
Data Quality
Data Governance
Users

32. Hospital Improves Patient Care with Data Warehouse
5.4
Hospital Improves Patient Care with Data Warehouse

33. 5.6 Knowledge Management Concepts and Definitions
Knowledge Management Systems
The KMS Cycle

34. Concepts & Definitions
Knowledge Management (KM)
A process that helps manipulate important knowledge that comprises part of the organization’s memory, usually in an unstructured format.
Knowledge
Explicit & Tacit Knowledge
Knowledge Management System (KMS)

35. Knowledge Management Systems (KMS)
Refer to the use of modern information technologies – the Internet, intranet, extranets, databases – to systematize, enhance, and expedite intrafirm and interfirm knowledge management.
Best practices

36. The KMS Cycle Create Knowledge Capture Knowledge Refine Knowledge
Store Knowledge
Manage Knowledge
Disseminate Knowledge

37. [ Closing Case Case Organizations Have Too Much Data? ]
The Problem
The Solution
The Results