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Business Analytics > Chapter 2 > Flashcards

Chapter 2 Flashcards

1
Q

it is a collaborative effort that involves multiple teams from multiple departments constantly communicating with each other.

A

Business analytics Implementation

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2
Q

is the starting point of an implementation, which will dictate which data will actually be conducive for the desired analysis.

A

Determining the information

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3
Q

what are the three major components of the system landscape?

A

Data sources
Enterprise data warehouse
Reporting and analysis tools

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4
Q

three main categories of
data sources in an Enterprise.

A

ERP systems
Other databases
Flat files

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5
Q

it is where an enterprises data is fed into and all reports are obtained directly from it

A

ERP System

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6
Q

makes extensive use of Master Data to help keep track of Business Partners and Items.

A

ERP System

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7
Q

Usually the maintenance of these is assigned to key people, who will be the ones to manage the creation of new Master Data or the updating of such.

A

ERP system

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8
Q

due to geographical or cost constraints, a branch of the company might be physically
impossible to connect to the corporate network.

A

Other databases

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9
Q

are usually Excel or delimited text files that business users create in order to make their own reports when needed.

A

Flat files

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10
Q

Delimited text files are usually either

A

Tab-delimited
Comma-separated value files (CSV)

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11
Q

is needed in order to work around these limitations.

A

Enterprise Data Warehouse

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12
Q

is built in order to consolidate the disparate data sources so that only the data necessary for reporting will actually be used.

A

Enterprise Data Warehouse

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13
Q

is concerned with
delivering “a single version of the truth”.

A

Consolidating Data

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14
Q

New hardware that will become the server hosting the Data Warehouse. It must be connected to the

A

Corporate Network

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15
Q

A dedicated project team from the Enterprise Side made up of

A

Business Users

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16
Q

is a tool to help build Data Warehouses,

A

SAP Business warehouse

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17
Q

is essentially a large Database, it is likely that technical column names are still used instead of more common, Business-friendly terms.

A

Enterprise Data Warehouse

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18
Q

is set up as a sort of “translator” so that the Business User can immediately understand what the data is, by allowing them to see technical terms as business terms.

A

Semantic Layer

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19
Q

what are the 3 tier architecture

A

Development (DEV)
Quality Assurance (QAS)
Production ( PRD)

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20
Q

is the most critical of the three, as it contains “live data”.

A

PRD

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21
Q

It is the system that is used in the day-to-day transactions of the company. A lot of redundancies might be required for this landscape, as it is needed for the proper function of the enterprise.

A

PRD

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22
Q

its physical hardware tends to be the most powerful of the three. Downtime for it must be reduced as much as possible due to its operational importance.

A

PRD

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23
Q

as its name states, is for development purposes.

A

DEV

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24
Q

When a new report needs to be created or a change in configuration needs to be made, it should be done here first.

A

DEV

25
Q

or the configuration does not result in catastrophic failure, they will be rolled up and applied/promoted to ?

A

QAS

26
Q

Other enterprises has a 4th, off-premises landscape known as

A

Disaster Recovery ( DR )

27
Q

This is essentially a copy of PRD that is placed separate from the other three landscapes.

A

Disaster Recovery

28
Q

It will act as a
contingency when PRD becomes subject to catastrophic failure

A

Disaster Recovery

29
Q

examples of data reliability inconsistencies

A

Inconsistent Terminology
Round Errors and Truncation
Nulls and Zeroes
Incorrect Inputs
Outright Data Discrepancies

30
Q

department might refer to an SKU as a

A

Product or material

31
Q

Consider the number of decimal places a given piece of
numeric data has.

A

Rounding errors and truncation

32
Q

This
could cause final numbers to deviate from the source.

A

Rounding Errors

33
Q

have the same effect, however, instead of rounding the number, decimal places are outright omitted

A

Truncation

34
Q

Null Values represent

A

Nothing

35
Q

this is where the concept of “Garbage In, Garbage Out” is very apparent.

A

Incorrect Inputs

36
Q

A company usually has some tactical decisions where promos and bundles of their products and services will be joined together

A

Outright Data Discrepancies

37
Q

is the first data model that can be fully described mathematically. All data (fields/columns) is represented in terms of tuples (rows/record), grouped into relations.

A

Relational Model

38
Q

can be obtained from multiple tables to produce one tuple of data by JOINing tables via their keys.

A

Data

39
Q

initially pushed as
the standard language for relational databases

A

SQL

40
Q

Three tables example

A

TXN
CUS_MAS
PROD_MAS

41
Q

initially pushed as
the standard language for relational databases

A

SQL

42
Q

stores all customer information.

A

CUS_MAS

43
Q

stores all product information.

A

PROD_MAS

44
Q

is a representation of the abstract structure of domain
information.

A

Schema or logical data model

45
Q

It is often expressed as a diagram, and is used as foundation to designing database structures.

A

Schema

46
Q

There are many different kinds of schemas, but the most-commonly used one in enterprise computing is
the

A

Star Schema

47
Q

It is comprised of a Fact Table (usually just one) referencing any number of Dimension Tables.

A

Star Schema

48
Q

records measurements for a specific event

A

Fact Table

49
Q

by contrast will contain less records than Fact Tables.

A

Dimension Table

50
Q

The data contain in dimension table are sometimes referred to as

A

Master Data

51
Q

ensure that each row of data within the table is unique.

A

Keys

52
Q

columns that automatically
increment, the more rows are populated, using some sort of algorithm

A

ID

53
Q

Types of keys

A

Primary or foreign

54
Q

Is to maintain a separate database that records all transactions for the day

A

Work around

55
Q

One of the defining features today in Business Analytics Tools is what’s called

A

Self-Service BI.

56
Q

is made available to the market to test its viability

A

trial run

57
Q

SQL Meaning

A

stuctured query Language

58
Q

It is the most
common way to store and access enterprise data, as it uses some form of Structured Query Language

A

Relational Model

59
Q
A

Business Analytics (4 decks)

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