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MIS 6903 > Exam 1 Review > Flashcards

Exam 1 Review Flashcards

1
Q

Business Intelligence

A

Everything you need to know about your customers, suppliers, industry, environment, and internal operations.

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2
Q

Digital Data Genesis

A

Smart devices are everywhere, sensing more data than ever before. up to 2003, generated 5gb of data, now generate that every 2 days.

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3
Q

Data Warehouse

A

Logical collection of information gathered from operational databases, used to create business intelligence that supports business analysis activities and decision-making tasks. Doesn’t need to be updated all the time. Multidimensional - rows and columns, layers, often called hypercubes.

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4
Q

Data Mart

A

A subset of a data warehouse in which only a focused portion of the data warehouse information is kept. Limits access to data analyst.

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5
Q

Competitive Advantage

A

When one firm is able to appropriate more value to themselves than others in their industry.

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6
Q

Sustainable Competitive Advantage

A

Ability of a firm to protect its competitive advantage over time.

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7
Q

Big Data

A

Huge Volume - petabyte and larger.
Rapid Velocity - Generated rapidly
Great Variety - free form text, different formats of web server and database log files. streams of data about user responses to page content; graphics, audio, and video files

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8
Q

Downside of Competitive Advantage

A

Sustainability is a continuum. how much time to erode the advantage. how much money to erode the advantage.

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9
Q

Job titles for Business Intelligence and Data Mining Professionals

A

Data Engineer/Data Miner - Very technical.
Data Analyst/Data Scientist - Marketing Focused.
Data Intelligence Architect - Strategy Focused.

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10
Q

3 Areas of expertise data analysts need

A

Data Management - essential to being able to collect, store, and access data.
Quantitative Method - Represent the analytical tools that are used to mine data.
Business Intelligence - Critical to both identifying opportunities for using data and for mobilizing findings into value creating initiatives.
Acquire data, Perform Analysis, Publish Results

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11
Q

Hypercube

A

Multidimensional data warehouse that has rows, columns, and layers

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12
Q

Web 2.0

A

a perceived 2nd generation of web software that allow novice users to add content to the web. lots of data. new challenges in garnering value from data (information overload, veracity). Empowered consumers: wrong them and everyone will know.

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13
Q

Internet of Things

A

the connecting of multiple devices embedded with sensors and actuators which allow these devices to collect and exchange data. bw 50 and 100 billion devices by 2020.

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14
Q

Data

A

Recorded measurements of some phenomena. What we use to inform our decisions. measuring is costly and not always accurate, but worth it. data has value and comes from many sources.

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15
Q

Sentiment

A

Measures attitudes about a product, service, or topic. Text Mining.

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16
Q

Word of Mouth

A

Measures how effectively individuals can be influenced by the espoused attitudes of others toward products or services.

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17
Q

Crowdsourcing

A

Empowering consumers to help you: build something, design products, provide information

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18
Q

Drivers of Digital Data Genesis

A

Mobile phones, social data, and the internet of things are the primary drivers

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19
Q

Factors that historically led business to store data

A

the cost to store data became cheap. store more things as social and economic structures grew and became more complex. advent of computers and automated storage.

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20
Q

Data Sizes

A

HP believes we generate 4.5zb in 2013, and will generate 180zb in 2025. 2 hour movie takes about 6gb. terabyte could hold all x-ray files recorded at Vident’s greenville hospital. petabyte could hold 13 years of hdtv content. 5 exabytes could hold every word in every human language. 42 zetabytes could hold every human word ever spoken in every language. youtube alone 300 hours of video uploaded every minute. square kilometer radio telescope array generates 1 exabyte of data every 4 years.

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21
Q

Marketing

A

the activity, set of instructions, and process for creating, communicating, delivering, and exchanging offerings that have value for customers, clients, partners, and society at large (AMA)

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22
Q

Price Differentiation

A

Being able to charge different customers different prices based on what we know about them.

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23
Q

4 Ps of Marketing

A

Product, Price, Placement, Promotion

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24
Q

Product

A

One of the 4 P’s of Marketing. Understanding consumer desires. Market segmentation became possible, leading to more thoughtful and more appealing product design

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25
Q

Price

A

One of the 4 P’s of Marketing. Evaluating the value that customers place on product. Past data helped influence demand, yielding optimization functions which identify optimal pricing strategies.

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26
Q

Placement

A

One of the 4 P’s of Marketing. Making products available to consumers in a convenient way. Transaction analysis allowed for better market segmentation and understanding of in-store behavior, leading to adjustments to store layout and product availability.

27
Q

Promotion

A

One of the 4 P’s of Marketing. Creating a sense of value in the minds of the customers (advertising). In addition to market segmentation, data mining allowed marketers to experiment with various promotional efforts and analyze results, yielding more effective promotional campaigns.

28
Q

Areas of marketing influenced by big data

A

Market segmentation, product development, advertising, distribution, and placement

29
Q

Reasons why it is harder to influence consumers now than it was before web 2.0

A

consumers are more empowered, and generally less responsive to advertising. Consumers have access to more information.

30
Q

BIO

A

Business Intelligence Opportunities.

31
Q

What happens when executives are unable and/or unwilling to become educated about the potential benefits of Business Intelligence

A

A strategic barrier to BI success because it translates to inaction - in the form of insufficient funding and/or insufficient commitment of business people to BI projects. Top executives won’t act if they don’t understand how value would be created with BI, and this is clearly a barrier to BI success.

32
Q

Reports

A

Standard, preformatted information for backward looking analysis of business trends, events, and performance

33
Q

Scorecards/Dashboards

A

Convenient forms of multidimensional analyses that are common across an organization, that enable rapid evaluation of business trends, events, and performance results, and that facilitate use of a common management framework and vocabulary for measuring, monitoring, and improving business performance

34
Q

Advanced Analytics

A

Automated applications that distill historical business information so that past business trends, events, and results can be summarized and analyzed via well-known and long-used statistical methods

35
Q

Predictive Analytics

A

Automated applications that leverage historical business information, descriptive statistics, and/or stated business assumptions to predict or simulate future business outcomes that can be analyzed for their business impact

36
Q

Structured Data

A

The typical business data used by companies for decades - represented as numerical values, calculated measures and metrics, and business facts such as financial results, customer characteristics, factory output, or product characteristics - and which has ben typically stored in relational databases

37
Q

Unstructured Data

A

Digital content such as pictures, video clips, text messages, document images, and web logs. Unstructured data is substantially equivalent to big data – but differs in that sensor data, location data, and machine data are typically structured data and are included as examples of the variety of data that collectively constitute big data

38
Q

Business Performance Gap

A

Performance gaps for any process can be measured using the common characteristics that all processes: they cost money, they take time, they achieve a service level, they achieve a quality level, they use assets, and they provide outputs to internal and/or external customers.

39
Q

Influence of business complexity on the strategic importance of business intelligence

A

BI is more strategically important in more complex businesses and industries, and less so in more straightforward business. The strategic importance of BI varies by industry, company, and the company business model. The more complex and information-intensive an industry is, the greater strategic importance of BI and the greater the opportunity for competitive differentiation.

40
Q

Business Intelligence meaning

A

Business intelligence means different things to different people

41
Q

Ways in which business intelligence can increase business value

A

increasing revenues, reducing costs, or both. enabling more impactful decisions that improve the effectiveness of core business processes, including business performance management processes.

42
Q

Strategy Map

A

A means of aligning business strategies, goals, and objectives with: the company’s value propositions for its customers; the internal business process through which customer value propositions are achieved; and the internal processes by which the company develops itself and its people. Communicates how a company intends to compete and achieve economic results - and thus it reveals what business processes are important to measure, manage, and improve. We can map BIOs to the strategy map. The framework assumes that business performance should be managed from four key perspectives: financial, customer, internal, and learning & growth.

43
Q

Industry Drivers

A

Key activities that guide the financial and operational results of a business. Company strategy, goals, objectives, and industry drivers are used in conjunction to determine how a company competes and to create business value.

44
Q

Top-Down BI Opportunity Analysis

A

By systematically developing an understanding of the linkages between industry drivers, company strategies, how the company competes, and the core business processes that are essential to how the company competes, we have a foundation for identifying potential uses of BI to improve the effectiveness of those core processes – and thereby create business value.

45
Q

How investments create value

A

Investments must increase net after-tax cash flows into the business. BI opportunity analysis used to identify and document the specific ways that BI can be used to increase revenues, reduce costs, or both.

46
Q

Node

A

Used in Enterprise Miner to perform essential data mining activity such as sample, explore, modify, model, or assess (SEMMA)

47
Q

Diagram

A

Diagram used to build, edit, and run process flow diagrams

48
Q

Properties Panel

A

Properties panel used to view and edit the settings of data sources, diagrams, nodes, and users

49
Q

Decision Tree

A

A decision tree is a diagram or chart that people use to determine a course of action or show a statistical probability. Readily accommodate nonlinear associations between input
variables and one or more target variables.

50
Q

Leaf

A

Any segment that is not further segmented. The final leaves in a tree are called terminal nodes.

51
Q

Pruning

A

The process of removing nodes from a decision tree when those nodes involve less than optimal decision rules.

52
Q

Subtree Assessment Plot

A

Plots the assessment criterion versus the number of leaves. The assessment criterion for a categorical target is the proportion of misclassified observations.

53
Q

Datasource

A

A datasource stores the metadata of an input data set. A data source must be defined in order to use sample data that is stored in a SAS data set in a SAS EM project.

54
Q

Library

A

A library is used to indicate to SAS the location in which the sample data is stored. When a library is created, you give SAS a shortcut name and pointer to a storage location in your operating environment where you store SAS files.

55
Q

SEMMA

A

The data mining process that is used by SAS Enterprise Miner. Sample, Explore, Modify, Model, and Assess.

56
Q

Sample

A

Sample the data by creating one or more data sets

57
Q

Explore

A

Explore the data by searching for relationships, trends, and anomalies in order to gain understanding and ideas. Statistical reporting, graphical exploration, variable selection methods, and variable clustering.

58
Q

Modify

A

Modify the data by creating, selecting, and transforming the variables to focus the model selection process. Defining transformations, missing value handling, value recording, and interactive binning.

59
Q

Model

A

Model the data by using the analytical tools to train a statistical or machine learning model to reliably predict a desired outcome. Linear and logistic regression, decision trees, neural networks, partial least squares, LARS and LASSO, nearest neighbor, and importing models defined by other users or even outside of SAS EM.

60
Q

Assess

A

Assess the data by evaluating the usefulness and reliability of the findings from the data mining process. Comparing models and computing new fit statistics, cutoff analysis, decision support, report generation, and score code management.

61
Q

Model Comparison Node

A

Enables you to compare the performance of competing models using various benchmarking criteria

62
Q

Validation

A

used to prevent a model node from overfitting the training data and to compare models

63
Q

Training

A

used for preliminary model fitting.

MIS 6903 (1 decks)

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