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DSS > Chapter 1 > Flashcards

Chapter 1 Flashcards

1
Q

Types of Business Analytics

A

Descriptive
Predictive
Prescriptive

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2
Q

Descriptive Business Analytic: Questions

A

What happened?
What is happening?

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3
Q

Descriptive Business Analytic: Enablers

A
  • Business Reporting
  • Dashboards
  • Scoreboards
  • Data warehousing
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4
Q

Descriptive Business Analytic: Outcomes

A

well-defined business problems and opportunities

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5
Q

Predictive Business Analytic: Questions

A

What will happen?
Why will it happen?

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6
Q

Predictive Business Analytic: Enablers

A
  • Data mining
  • Text mining
  • Web/media mining
  • Forecasting
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7
Q

Predictive Business Analytic: Outcomes

A

Accurate projections of future events and outcomes

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8
Q

Prescriptive Business Analytic: Questions

A

What should I do?
Why should I do it?

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9
Q

Prescriptive Business Analytic: Enablers

A
  • Optimization
  • Simulation
  • Decision modeling
  • Expert systems
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10
Q

Prescriptive Business Analytic: Outcomes

A

Best possible business decisions and actions

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11
Q

Define data wharehouse

A
  • collection of databases storing content and historical data
  • “single version of truth”
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12
Q

What are the components of data warehousing process?

A
  • data sources
  • ETL Process
  • Enterprise data warehouse
  • Metadata
  • Data mart
  • API/Middleware tools
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13
Q

OLTP: Purpose

A

to carry out day-to-day business functions

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14
Q

OLTP: Data source

A
  • transaction database
  • a normalized data repository
  • focused on efficiency and consistency
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15
Q

OLTP: Reporting

A
  • routine
  • periodic
  • narrowly focused
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16
Q

OLTP: Database requirements

A

ordinary rational databases

17
Q

OLTP: Execution speed

A
  • fast
  • recording of business transactions and routine reports
18
Q

OLAP: Purpose

A
  • to support decision making
  • to provide answers for business and management queries
19
Q

OLAP: Data source

A
  • data warehouse or data mart
  • a non-normalized data repository
  • focused on accuracy and complenetess
20
Q

OLAP: Reporting

A
  • Ad hoc
  • multidimensional
  • broadly focused
21
Q

OLAP: Database requirements

A
  • multiprocessor
  • large-capacity
  • specialized
22
Q

OLAP: Execution speed

A
  • slow
  • resource intensive, complex, large-scale queries
23
Q

Olap opperations: Slice

A

a susset if data corresponding to a single value set for one or more dimensions not in the subset

24
Q

OLAP operations: DICE

A

slice on more than two dimentions

25
Q

What are some other OLAP operations?

A
  • Drill Down/up
  • Roll up
  • Pivot
26
Q

What is the Drill Down/Up (other OLAP operations)?

A

navigation among levels of data ranging from the most summarized to the most detailed

27
Q

What is the Roll Up (other OLAP operations)?

A

computing all of the data relationships for one or more dimensions

28
Q

What is the Pivot (other OLAP operations) used for?

A

to change dimensional orientation of a report or an ad hoc

29
Q

Simon’s model (chart)

A
30
Q

What is a Problem and what does it require?

A
  • root cause
  • requires solutions for solving, substantive and tangible impact
31
Q

What is a symptom?

A

s visible recognizable sign of underlying problem, indication, observable effect

32
Q

What are DSS?

A

interactive computer-based systems wich support decision makers by utilization of data and models to solve semi structured problems

33
Q

What is data mining?

A

process that uses statistical, mathemathical and artificial intelligence techniques to extract and identify information

34
Q

Types of patterns

A
  1. Association (link analysis, etc)
  2. Prediction (classification, time series, regression)
  3. Segmentation (clustering)
35
Q

What are the learning types?

A
  1. Supervised (know classes)
  2. Unsupervised (unkown classes)
36
Q

What is the purpose of data science?

A

covers practical application of advanced analytics, statistics and encessary preparation in business context

DSS (4 decks)

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