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Big data > Ch 1 Big data > Flashcards

Ch 1 Big data Flashcards

1
Q

big data

A

analysis, processing, and storage of large collections of

data that frequently originate from disparate sources.

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2
Q

datasets

A

Collections or groups of related data are generally referred to as datasets

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3
Q

data analysis and its goal

A

Data analysis is the process of examining data to find facts, relationships, patterns,
insights and/or trends. The overall goal of data analysis is to support better decisionmaking.

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4
Q

Data analytics

A

Data analytics is a
discipline that includes the management of the complete data lifecycle, which
encompasses collecting, cleansing, organizing, storing, analyzing and governing data.

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5
Q

four general categories of analytics

A

descriptive analytics
• diagnostic analytics
• predictive analytics
• prescriptive analytics

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6
Q

Descriptive Analytics

A

Descriptive analytics are carried out to answer questions about events that have already
occurred. This form of analytics contextualizes data to generate information.

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7
Q

Diagnostic Analytics

A

Diagnostic analytics aim to determine the cause of a phenomenon that occurred in the past
using questions that focus on the reason behind the event. The goal of this type of
analytics is to determine what information is related to the phenomenon in order to enable
answering questions that seek to determine why something has occurred.

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8
Q

Predictive Analytics

A

Predictive analytics are carried out in an attempt to determine the outcome of an event that
might occur in the future.

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9
Q

Prescriptive Analytics

A

Prescriptive analytics build upon the results of predictive analytics by prescribing actions
that should be taken.

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10
Q

Business Intelligence (BI)

A

BI enables an organization to gain insight into the performance of an enterprise by
analyzing data generated by its business processes and information systems.

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11
Q

Key Performance Indicators (KPI)

A

A KPI is a metric that can be used to gauge success within a particular business context.
KPIs are linked with an enterprise’s overall strategic goals and objectives.

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12
Q

Big Data Characteristics

A
  • volume
  • velocity
  • variety
  • veracity
  • value
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13
Q

Velocity

A

From an enterprise’s point of view, the
velocity of data translates into the amount of time it takes for the data to be processed once
it enters the enterprise’s perimeter.

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14
Q

Variety

A

Data variety refers to the multiple formats and types of data that need to be supported by
Big Data solutions.

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15
Q

Veracity

A

Veracity refers to the quality or fidelity of data. Data that enters Big Data environments
needs to be assessed for quality, which can lead to data processing activities to resolve
invalid data and remove noise

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16
Q

Noise in data

A

Noise is data that cannot be converted into information and thus has no value,

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17
Q

signals in data

A

whereas signals have value and lead to meaningful information.

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18
Q

Value, and its dependencies

A

Value is defined as the usefulness of data for an enterprise. The value characteristic is
intuitively related to the veracity characteristic in that the higher the data fidelity, the more
value it holds for the business. Value is also dependent on how long data processing takes
because analytics results have a shelf-life

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19
Q

Human-generated data

A

Human-generated data is the result of human interaction with systems, such as online
services and digital devices.

20
Q

Machine-generated data

A

Machine-generated data is generated by software programs and hardware devices in
response to real-world events.

21
Q

Structured Data

A

Structured data conforms to a data model or schema and is often stored in tabular form and stored in a relational database

22
Q

Unstructured Data

A

Data that does not conform to a data model or data schema is known as unstructured data.
It is estimated that unstructured data makes up 80% of the data within any given
enterprise

23
Q

Semi-structured Data

A

Semi-structured data has a defined level of structure and consistency, but is not relational
in nature. Instead, semi-structured data is hierarchical or graph-based.

24
Q

Metadata

A

Metadata provides information about a dataset’s characteristics and structure. This type of
data is mostly machine-generated and can be appended to data.

25
Q

The first and most important step in any data analysis project is

A

The first and most important step in any data analysis project is to establish a clear goal, not a goal
defined only by the data or the method, but a goal that makes sense to the business as a whole. In

26
Q

Descriptive analysis

A

technique that allows you to view and measure your company and
customer characteristics.

27
Q

Customer Profile

A

snapshot of exactly who is buying your products or

services.

28
Q

Market penetration analysis and wallet share analysis

A

are techniques for measuring the

performance of your customer base in comparison with the performance of the overall market for your industry

29
Q

response mode

A

typically the first type of target model that a company seeks to develop.

30
Q

win-back model

A

A win-back model is used to invite former customers to reconsider their relationship to the business

31
Q

activation model

A

An activation model predicts whether a prospect will become a customer

32
Q

revenue model

A

predicts the dollar amount of an expected sale

33
Q

usage model

A

predicts the amount of use given to a product or service

34
Q

cross-sell model

A

cross-sell model is used to predict the probability or value of a current customer’s buying a different product or service from the same company.

35
Q

up-sell model

A

An up-sell model predicts the probability or

value of a customer’s buying more of the same product or service

36
Q

Among three drugs, which one provides the best results?

A

Prescriptive Analytics

37
Q

When is the best time to trade a particular stock?

A

Prescriptive Analytics

38
Q

What are the chances that a customer will default on a loan if they have missed a
monthly payment?

A

Predictive Analytics

39
Q

What will be the patient survival rate if Drug B is administered instead of Drug A?

A

Predictive Analytics

40
Q

If a customer has purchased Products A and B, what are the chances that they will
also purchase Product C?

A

Predictive Analytics

41
Q

Why were Q2 sales less than Q1 sales?

A

Diagnostic Analytics

42
Q

Why have there been more support calls originating from the Eastern region than
from the Western region?

A

Diagnostic Analytics

43
Q

Why was there an increase in patient re-admission rates over the past three months?

A

Diagnostic Analytics

44
Q

What was the sales volume over the past 12 months?

A

Descriptive analytics

45
Q

What is the number of support calls received as categorized by severity and
geographic location?

A

Descriptive analytics

46
Q

What is the monthly commission earned by each sales agent?

A

Descriptive analytics

Big data (8 decks)

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