MIS Exam #2 > Decision Making & Knowledge Management > Flashcards

Decision Making & Knowledge Management Flashcards

(54 cards)

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1
Q

What process do decision makers follow?

A

A repeatable process

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2
Q

Steps in the Decision Process

A
  1. Define the problem
  2. Identify limiting factors
  3. Develop potential alternatives
  4. Analyze the alternatives
  5. Select the best alternative
  6. Implement the decision
  7. Establish a control and evaluation system
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3
Q

Structured Data

A
  • Everything so far in course
  • ERD
  • Organizational Databases
  • ERP
  • Clearly defined data entities, types, relationships, hierarchies
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4
Q

Unstructured Data

A
  • User generated data
  • Email
  • Tweets
  • Comments
  • Images
  • Videos
  • Blogs
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5
Q

Types of Decisions You Face

A

Recurring/Non-recurring

Unstructured/Structured

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6
Q

What type of decision is your daily/weekly regimented tasks?

A

Structured & Recurring

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7
Q

What type of decision is using analytics to solve complex problems and questions?

A

Unstructured & Non-recurring

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8
Q

Analytics

A

The process of making sense of large data sets and unlocking patterns, often using data visualization, to enable better decision making

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9
Q

Data Analytics

A

Art/Science of examining raw data for the purpose of gaining insight and drawing actionable conclusions about business problems

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10
Q

Big Data Analytics

A

Process of examining big data to uncover hidden patterns, unknown correlations, and other useful information that can be used to make better decisions

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11
Q

What should we do with all of this data?

A

Data –> Information –> Knowledge

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12
Q

Descriptive Analytics

A
  • Tracks consumer behavior
  • Describes what is happening
  • “How do users interface with a website?”
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13
Q

Predictive Analytics

A
  • What will consumers buy?

- When will demand surge?

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14
Q

Why should you care about data?

A
  • Cost reduction
  • Faster, better decision making
  • New products and services
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15
Q

What does Google Analytics track?

A
  • Site metadata/user engagement
  • # of sessions
  • Average session duration
  • Number of pages visited and duration at each
  • Bounce rate
  • Conversion
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16
Q

Why do some organizations resist data driven decision making?

A

Because they don’t like what the data is telling them; People don’t like to be held accountable for problems

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17
Q

OTLP (Online Transaction Processing)

A

Class of information systems that facilitate and manage transaction-oriented applications, typically for data entry and retrieval transaction processing

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18
Q

OLAP (Online Analytical Processing)

A

Computer-based technique of analyzing data to look for insights. The term cube here refers to a multi-dimensional dataset, which is also sometimes called a hypercube if the number of dimensions is greater than 3

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19
Q

Characterization of OLTP vs. OLAP

A
  • OLTP: large number of short on-line transactions

- OLAP: relatively low volume of transactions

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20
Q

Hypercube

A

Multi-dimensional “cubes” of information that summarize transactional data across a variety of dimensions

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21
Q

Knowledge Management

A
  • Process of capturing, developing, sharing, and effectively using organizationalknowledge
  • Refers to a multi-disciplinary approach to achieving organizational objectives by making the best use ofknowledge
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22
Q

Why are CEOs worried about Baby Boomers?

Study These Flashcards
A

Because they are starting to retire so they will be losing out on these essential skills

23
Q

What are communities of practice composed of?

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A

Domain
Community
Practice
Purpose

24
Q

Examples of Business Constraints

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A

Budget, Schedule, Scope, Time

25
Satisfice
To make the best decision possible with all the information, time, and resources available
26
Nominal Group Technique
- Highly structured meeting/agenda - Restricts discussion during decision making process - Ensures equal input - Avoids conformity
27
Delphi Technique
- Participants never meet | - Written questionnaires to conduct decision making
28
How are sport franchises like small businesses?
- Think they are too small to benefit from big data | - Data shows different states then what people actually think
29
What did SAP do to change the world of sports?
Integrated a software for a drafting application that measures more meaningful and desirable traits
30
Why do people tend to resist analytics?
- Creates new accountability which makes people nervous - More data = more responsible for inefficiencies - More invested in acquiring analytic capabilities than confronting accountability crisis
31
What are the assets of Knowledge Managements?
-Databases, documents, policies, procedures, expertise
32
Explicit Knowledge
Information or knowledge that is set in tangible form
33
Implicit Knowledge
Information or knowledge that is not set in tangible form but could be made explicit
34
Tacit Knowledge
Information or knowledge that one would have extreme difficulty operationally setting out in tangible form
35
Lesson Learned Database
Databases that attempt to capture and to make accessible knowledge that has been operationally obtained and typically wouldn't be captured in fixed medium
36
Community of Practice
- Groups of individuals with shared interests that come together in person or virtually to tell stories, share/discuss problems/opportunities, talk about lessons learned, etc. - Ex: World Bank
37
KM Development Stages
- 1. Driven by IT - 2. HR/Corporate Culture - 3. Taxonomy and Content Management
38
Data Analytics
The use of tools and people to uncover hidden patterns in data that might not be readily available to the naked eye
39
Google Analytics
-Tracks web site metadata and user engagement
40
What is OTLP composed of?
High Transaction Volume + Quick Data Entry/Retrieval + Data Integrity
41
What is OLAP composed of?
Complex Queries + Data Mining + Multi Dimensional Reporting
42
What is the focus difference between OLAP and OLTP?
- OLTP focuses on business processes such as operations, business strategy, and master data transactions - OLAP focuses on business data warehouse with information and data mining analytics decision making
43
OTLP/OLAP Database and Data Warehouse
-OLTP Database information is dumped into OLAP Data Warehouse (business intelligence)
44
What does a Hypercube allow us to do?
Allows department to figure out sales at a certain time, territory, etc. by eliminating queries
45
Data Marts
- Chunks/smaller versions of a hypercube | - Merchandising, Advertising, Distribution, Sales, Marketing
46
The Four Vs of Big Data
- Volume (lots of data) - Variety (lots of types of data) - Velocity (data must be changing rapidly) - Veracity (truthful) - Ex: Weather Channel
47
Big Data Tools
- Storage: Hadoop - Processing: Hadoop Map Reduce - Analytics/Visualization: Tableau
48
Brain Drain
Anticipated loss of technical skill, historical knowledge, and ability over time due to this rapid rate of retirement
49
Tacit Knowledge
- Difficult to transfer to others, visualize, write down - Gained/Learned through experience - Action-Oriented "how we know" information - Ex: How to ride a bike, learning a language, team management
50
Explicit Knowledge
- Academic - What you know - Easy to describe in language and transfer between people - Anything documented - Work flow, SLD, Payroll
51
How do organizations transfer tacit knowledge to explicit knowledge?
Communities of Practice
52
Benefits of KM
- Improves performance - Decreases learning curve - Respond more rapidly - Reduces rework, share knowledge for new ideas
53
Challenges of KM
- Employee buy in - Knowledge overload - Keeping data accurate
54
Communities of Practice
- Community: creates environment for people to discuss topic - Domain: focuses on specific knowledge (expert) - Purpose: hear about everyones experiences and share own - Practice: continue to talk about techniques
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