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ISM3004 > Scavenger Hunt 8 > Flashcards

Scavenger Hunt 8 Flashcards

1
Q

What is the rate of data growth?

A

Doubling every 6 months…unprecedented and will continue regardless of budget restraints

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2
Q

What is “information overload”? What is its alleged impact?

A

Vast flood of info is disruptive, liking drinking from a fire hydrant
People’s cant do their jobs with so much data

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3
Q

By contrast, what is “information abundance” and what are its implications for knowledge workers?

A

Take advantage of the presence of all the new data to learn how to make meaningful info from it and gain managerial knowledge
Geek up!

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4
Q

Business Intelligence

A

Combining aspects of reporting, data exploration & ad hoc queries, & sophisticated data modeling & analysis

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5
Q

Analytics

A

Statistical and quantitative analysis of data
Explanatory and predictive models, and fact-based management to drive decisions and actions

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6
Q

Data

A

Raw facts and figures
Tells you nothing alone

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7
Q

Information

A

Data that has been presented in such a way that it answers questions or supports decision making

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8
Q

Knowledge

A

Insight derived from experience and enterprise-savvy information

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9
Q

Structured Data

A

Organized data that conforms to a model so that it can be searched, analyzed, and queried using traditional analysis tools

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10
Q

Unstructured Data

A

Not organized, no schema
Ex. Text, email, Facebook pages, news stories
Binary

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11
Q

Table

A

Organized collection of data made up of records and fields

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12
Q

Record

A

Part of table
Row of data
Individual observation

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13
Q

Fields

A

Part of table
Column
Attribute for data (fixed schema-textual data)

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14
Q

Relational Database

A

A database that correlates data from multiple tables

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15
Q

Relational Database Benefits

A

Combines and simplifies data

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16
Q

Relational Database Key Field

A

One of the fields in a table marked with a key so that the data items are unique for that row
Never repeat

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17
Q

Relational Database Valid Relationship Types

A

One:One -> Exactly one occurrence in the key field and Table B has only 1 occurrence as well
One:Many -> One occurrence in the key field but many occurrences in other tables

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18
Q

Relational Database Views

A

Display data from multiple tables relationships by combining them for reporting and display

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19
Q

SQL

A

Structured Query Language
Most common language for creating & manipulating databases
Ruling champion database in business world

20
Q

TPS

A

Transaction Processing Systems
Ex. ATM, retail sales transactions, websites, searches

21
Q

(TPS) What is a “transaction?” What are its two key characteristics?

A

Any business exchange
1. Standardized-schema
2. Occurs repeatedly

22
Q

(TPS) Point of Sale system

A

Retail computer systems that collect sales data and are hooked directly into the store’s inventory-control system
Scan barcode, transaction happens

23
Q

(TPS) How do loyalty cards generate valuable data?

A

Membership program in which company is paying you through bonuses for data about you that you otherwise would not give them

24
Q

Enterprise Software

A

CRM, ERP, SCM
Applications that address the needs of multiple users throughout an organization or work group

25
Q

CRM

A

Customer Relation Management System
Every sales call, every customer inquiry, every follow up call=data

26
Q

ERP

A

Enterprise Resource Planning System
Paychecks, invoices, payments=business transactions/data to seek insights

27
Q

SCM

A

Supply Chain Management System
Each order for finished goods/raw materials=transactions

28
Q

Business operations — examples

A

Health care patient data
Michigan tags cows at birth which gives lifetime stream of data for each cow raised in the state
Transportation industry: Plane engine produces 10tb of data every 30 min (sensors on aircrafts)
Swiss Rails: 100 data items a second (sensors on train & track)

29
Q

Sources of customer-provided data

A

Customer surveys: customer insight on products/services they received

Product registration cards: data about customer income, where they live, highest education, hobbies

Contests: cheap data where thousands of people apply, giving the company new data from all the entries

30
Q

Data aggregator

A

A company whose sole job is to collect data from a wide variety of sources and organize it, clean it, and connect it to each other and then sell access to it to others

31
Q

Data silos

A

Data collections completely separated with no possibility of communication or sharing

32
Q

How do data silos come into being?

A

Company may have some data trapped inside of obsolete legacy systems

33
Q

Why are data silos a problem?

A

Incompatible systems make it so there’s missed opportunities to see patterns, trends, correlations, and develop new insights to answer questions and make decisions

34
Q

How do inconsistent data formats impact a business?

A

Makes it hard to sort data

35
Q

Operational data

A

Data that is produced by your organization’s day to day operations Things like customer, inventory, and purchase data

36
Q

How does the analysis of operational data compete with customers?

A

Delays and lost sales due to significant amount of additional load to the system during business hours (best if we do not query operational data)

37
Q

What can a company do about the operational data vs customers problem?

A

Separate data repository:
- One for operational data
- One for reporting and analytics

Combine data from many sources and cleaning it

Historical data builds as months and days go by; used as a resource to see trends

Periodic import from operational systems allows analytical system to be up to date enough to come up with inferences

38
Q

What is a Data Warehouse? What are its characteristics

A

Collection of databases that supports decision making
- Many sources
- Operational systems-periodic transfer
- Historical data
- Fast Queries
- Exploration

39
Q

How is a Data Mart different from a Data Warehouse?

A

Same thing, different scale
Data Mart looks at specific problem/unit rather than the entire enterprise

40
Q

What three characteristics are necessary for something to be “Big Data”? (three V’s) Explain each

A

Volume:
notion data is “too big” to be analyzed with traditional methods (hundreds and millions of data items)

Velocity:
Rapid arrival & feedback loop. Data is too fast. Cannot react fast enough.

Variety:
text, images, sound, video, human input, sensors, servers, so many types of data

41
Q

What is Hadoop?

A

open source system designed to be able to consume ANY data you want (structured, unstructured, all data types)

Distributing computing platform

Scalable, cost effective, flexible, fault-tolerant

42
Q

Examples of big data - How do you see the Three V’s in each?

A

Predictive Policing in LA
(historical crime data)

Tesco grocery chain
(optimized fridge costs with in store fridges providing 70M data points per store per year; proactive maintenance reduced maintenance costs by ID’ing problems before they happen)

Actions speak louder than words
(Veteran Therapy; military suicide prevention in the US; uses pattern recognition to identify signs and types of psychological distresses through video measurements)

43
Q

Canned Reports + Pros & Cons

A

Reports that provide regular summaries of information in a predetermined format
Answer specific questions
Pros: Easy & useful
Cons: Inflexible & IT overhead

44
Q

Ad-Hoc Reporting Tools + Pros & Cons

A

Tools that put users in control so that they can create custom reports on an as-needed basis by selecting fields, ranges, summary conditions, and other parameters
Pros: Users define their own resorts, Powerful/flexible
Cons: Demanding of user, Potentially steep learning curve, Business knowledge, Understand data schema

45
Q

Dashboards

A

Graphic view of what is happening inside the software system
Some customization
A picture is worth a thousand words

46
Q

OLAP + Pros & Cons

A

Online analytical processing

The manipulation of information to create business intelligence in support of strategic decision making

used for enormous amounts of data

Pros: Huge data, Pre-processed + Summarized, User reports fast
Cons: No access to details; user only sees summary

47
Q

Data Mining

A

The process of analyzing data to extract information not offered by the raw data alone

Enormous historical datasets
Identify patterns
Build Models
Predict Future

ISM3004 (11 decks)

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