Chapter 12: Business Intelligence Flashcards

1
Q

Process + Phases in Making a Decisions (check notes, this may be inaccurate)

A

i.e a rainy day outside, what do you take to cover yourself

  1. You examine the reality/problem, this examination leads to the

Intelligence Phase, where you see “What is the problem”

In this case, it’s trying to cover yourself from the rain

  1. Next you see/list the options for the decisions in

the Design Phase “What are My Options”

i.e.: You could use a Hoodie Jacket, an Umbrella, or your Backpack to cover yourself from the rain

You can validate the model (???) when you apply it to the reality of the Situations

  1. The Choice phase is where you” Pick an Option + Decide how to Implement it”

The Verification + Testing of the proposed solution can be applied to the problem in reality

i.e. you pick the Umbrella

  1. The implementation of the solution:

See if the option proposed worked

If it did = Success

If it didn’t = Failure, go back to the decision It’s process

Not every option works all the time, sometimes you can wear a Hoodie on lighter rains than carrying around an umbrella

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2
Q

3 Basic Roles of a Manager

A

Interpersonal

Informational

Decisional

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3
Q

Decision making is difficult due to following reasons

A
  1. # of alt’s is constantly increasing
  2. Most decisions must be made under time pressure
  3. Increased Uncertainty in the dec environment
  4. Often necessary to Rapidly Access Remote Info, Consult with Experts, or conduct a Group dec - making session
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4
Q

A Framework for Computerized Dec Analysis

A

Includes:

  1. Problem Structure (Structured (seen before_, Semi - Struct (somewhat seen before?), Unstruc decs (unique?))
  2. Nature of Decisions:

3 broad categories of Managerial Dec’s/of managerial control in dec - making:

Operational Control (1st Line Mang/Emps?)

Management Control (Higher up Mang’s)

Strategic Planning (Top Mang/CEO?)

  1. The Decision Matrix (links prob Strcut + Decision Nature together + IS used to support solution/dec?)

Computer Support for Structured Dec’s

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5
Q

Dec - Making + Problem - Solving occur

A

At each level in an org

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6
Q

Operational Dec - Making

A

Employees Develop, Control, _ Maintain core Bu activities required to run the day - 2 - day operations

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7
Q

Structured Decisions

A

Situations where established processes offer potential solutions (i.e. Problem in Factory machine, Struc Dec = Push button to call engineer)

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8
Q

Managerial Dec - Making

A

Emp’s/Mang’s? evaluate Company operations to Identify, Adapt to, + Leverage Change

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9
Q

Semistructured Dec’s

A

Occur in situations in which a few established processes help to evaluate potential solutions, but not enough to lead to definite recommended decisions

(i.e. Mang wants to fire emp based on claims of workplace shenanigans, knows criteria for firing, but how to decide on seeing if allegations true???)

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10
Q

Strategic Dec Making

A

Mang’s Develop overall Strats, Goals + Objectives

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11
Q

Unstruct Dec’s

A

Occurs in situations in which No procedures or rules exist to guide dec - making toward the correct choice

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12
Q

Scope of Bu Intelligence

A

The Development of 1 or a Few Related BI Applications

The Development of Infrastructure to Support Enterprise-wide BI

Support for organizational Transformation

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13
Q

Problem: Data Rich, Info Poor

A

Bu’s face a Data Explosion with Digital Images, Email in-boxes + broadband connections

The amount of data generated is doubling/2x ing every yr

Some believe it will son double monthly

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14
Q

BI Can Answer Tough Q’s

A

It starts from answering broader to answering specific questions

(i.e. Q: Why are Sales below Target —> A: Because we sold less in Western Region
…….

Q: Why did Cu Complaints inc? —-> A: Because late Deliveries went up 60%

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15
Q

Bu Analytics Process

A
  1. Starts with bU Problem/Org Pain points
  2. Moves that into Data Management Ware house (with things like External, Internat, Big Data being ETL(clean) to be put? in Data Warehouse)
  3. It then goes through Descriptive Analytics (i.e. Descriptive Statistics, Data Mining, Dec Support Systems) which shows:

“What has happened?”

  1. The through Predictive Analytics (i.e. Linear Regression, Logistic Regression, Multiple Regression) to see:

“What could happen?”

This uses Presentation tools like: Dashboards, Figures, Graphs + Reports

  1. Then it goes through Prescriptive Analytics (i.e. Optimization, Simulation + Dec Trees) to see:

“What should we do?”

  1. This all helps lead to Actionable Bu Dec’s/Recommended Actions
  2. After that you ask new Q’s
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16
Q

Underlying Technologies for the Previous Process

A

Increasingly Powerful Chips (GPUs)

Cheaper, Higher - Volume Storage

Increasingly Faster Broadband Internet

Neural networks

Machine learning

Deep Learning

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17
Q

Bu Analytics Tools

A

Excel

Multidimensional Analysis or Online Analytical Processing (OLAP)

Data Mining

Dec Support Systems (DSS)

18
Q

Data Mining + Bu Analytic Tools:

Predictive Data - Mining Applications (Apps) are used in:

A

Retailing + Sales

Banking

Manufacturing + Production

Insurance

Police Work

Healthcare

Marketing

19
Q

Prescriptive Analytics

A

Statistical procedures include: Optimization, Simulation + Dec Trees

20
Q

Dec Support System (DSS)

A

Sensitivity Analysis (0

What - if Analysis ()

Goal - Seeking Analysis ()

21
Q

Artificial Intelligence

A

The Theory + Development of IS’s able to perform tasks that normally require human intelligence

22
Q

Intelligent Behaviour

A

Learning or Understanding from experience, making sense of ambiguous or contradictory messages, + responding quickly + successfully to new situations

23
Q

Algorithm

A

A Problem - solving method expressed as a finite sequence of steps

24
Q

Technological Advancements that led to Advancements in AI

A

Advancements in chip Technology

Big Data

The Internet + Cloud Computing

+ Improved Algorithims

25
Q

Natural vs Artificial Intelligence

A

Large table, look at it on Page 26

AI Creativity could be a bit diff than what on table (but that’s Teacher’s opinion)

26
Q

Ai Technoogies 1: Expert Systems

A

Transfers expertise from a domain expert (or other source) to the system

27
Q

AIT 2: Machine Learning

A

Is the Ability to perform new, unseen tasks built on known properties from training or historical data that are labeled

28
Q

AIT 3: Deep Learning

A

Subset of Machine Learning in which the system discovers new patterns without being exposed to labeled or historical or training data

29
Q

AIT 4: Neural Network

A

A set of virtual neurons or central processing units (CPU’s) that work in parallel in an attempt to simulate the way the human brain works, although in a greatest simplified form

30
Q

AI Applications

A
  1. Computer Vision:

Refers to the ability of IS’s to identify objects, scenes, and activities in Images

Natural Language Processing

Robotic

Speech Recognition

Intelligent Agents

Infor Agents search for information + dispatch it to users

Best known Info Agents = Buyer Agents

Buyer agent (or Shopping Bot): Helps Cu’s find the Pr & Sr’s they need on a Web Site

31
Q

Dashboards

A

Brain is wired to look at visual inputs

You look at gauge not spreadsheet when driving car

Dashboards can make data easier to visualize/understand

i.e. Gauges to see Department Performance, Revenue per sale, etc

32
Q

Dashboard Capabilities 1: Drill Down

A

The ability to go to details, in several levels

It can be done in a series of menus or by clicking in a drillable portion of the screen

33
Q

DB2: Critical Sucess Factors (CSFs)

A

The factors most critical for the success of the Bu

These can be: Organizational, Industry, Departmental, or individ workers

34
Q

DB3: Key Performance Indicators (KPI’s)

A

The specific measures of CSF’s

35
Q

DB4: Status Access:

A

The latest data available on a KPI or some other metric, often on real time (current?)

36
Q

DB5: Trend Analysis

A

S, Medium, + L - term trend of KPI’s or metrics, which are Projected using forecasting methods

37
Q

DB6: Exception Reporting

A

Reports that highlight deviations larger than certain thresholds

Reports may include only deviations

38
Q

Data Visualization Technologies

A

Geographical IS’s:

Geocoding

Reality Mining:

Graphical IS (GIS) + Geographic Positioning Systems (GPS) together produce an interesting new type of tech which allows analysts to extract info from usage patterns of mobile phones + other wireless devices

i.e

NYC

Singapore

Other Major cities

(found in these cities or examples of cities where Reality Mining is used???)

39
Q

Bu in Action: Corporate Performance Management (CPM)

A

Uses Key Performance Indicators (KPI)

CPM =

40
Q

KPI’s

A

The quantifiable metrics a comp uses to evaluate progress toward Critical Success Factors

i.e.:

Turnover Rate of emp’s

of Product Returns

of New Cu’s

Avg Cu Spending