Ch 12 BANAAAAAAAAAAAAAAAA :D

Question 1

What is bana

Answer 1

process of developing deciisons or reccomendations based on insights from the data

Question 2

what is business intelligence

Answer 2

apps, technolgoeis, and prcoesses for gathering, storig, accessing and analyzing data to elp business users make better decisions

Question 3

what do info systems do

Answer 3

support the managers job

Question 4

3 roles of the manager job ACCORIDNG TO MINTZBERG

Answer 4

1. interpersonal roles: figurehead, leader
2. informational role: monitor, disseminator, spokesperson
3. decisional roles: entrepreneur, disturbance handler

Question 5

3 steps in deciison making

Answer 5

intelligence (identify problem, define opportunity)

design (construct a model for addressing situation)

choice (select a solution oro course of action to solve problem)

Question 6

why do managers need IT support

Answer 6

too many alts

time pressure

need a sophisticated analysis

often necessary to rapidly access remote info, consult experts, or conduct a group decision making session

Question 7

problem structure

Answer 7

Problem Structure: dtructured, semi-strucutre, unstructure

NATURE OF DECISIONS: 3 categories of managerial decisions (operational, management, strategic)

The first dimension is problem structure, in which decision-making processes fall along a continuum ranging from highly structured to highly unstructured (see the left column in Figure 12.2). Structured decisions deal with routine and repetitive problems for which standard solutions exist, such as inventory control. In a structured decision, the first three phases of the decision process—intelligence, design, and choice—are laid out in a particular sequence, and the procedures for obtaining the best (or at least a good enough) solution are known. These types of decisions are candidates for decision automation.
At the other extreme of complexity are unstructured decisions. These decisions are intended to deal with “fuzzy,” complex problems for which there are no cut-and-dried solutions. An unstructured decision is one in which there is no standardized procedure for carrying out any of the three phases. In making such a decision, human intuition and judgement often play an important role. Typical unstructured decisions include planning new service offerings, hiring an executive, and choosing a set of research and development (R&D) projects for the coming year. Although BA cannot make unstructured decisions, it can provide information that assists decision makers.
Located between structured and unstructured decisions are semistructured decisions, in which only some of the decision-process phases are structured. Semistructured decisions require a combination of standard solution procedures and individual judgement. Examples of semistructured decisions are evaluating employees, setting marketing budgets for consumer products, performing capital acquisition analysis, and trading bonds

Question 8

nature of deicisons

Answer 8

The second dimension of decision support deals with the nature of decisions. All managerial decisions fall into one of three broad categories:

Operational control: Executing specific tasks efficiently and effectively
Management control: Acquiring and using resources efficiently in accomplishing organizational goals
Strategic planning: The long-range goals and policies for growth and resource allocation

Question 9

the decision matrix

Answer 9

The three primary classes of problem structure and the three broad categories of the nature of decisions can be combined in a decision-support matrix that consists of nine cells, as diagrammed in Figure 12.2. Lower-level managers usually perform the tasks in cells 1, 2, and 4. The tasks in cells 3, 5, and 7 are usually the responsibility of middle managers and professional staff. Finally, the tasks in cells 6, 8, and 9 are generally carried out by senior executives.
Today, it is difficult to state that certain organizational information systems support certain cells in the decision matrix. The fact is that the increasing sophistication of ISs means that essentially any information system can be useful to any decision maker, regardless of their level or function in the organization. As you study this chapter, you will see that business analytics is applicable across all cells of the decision matrix.

Question 10

3 targets of bana

Answer 10

1. devloping one/many related analytics applications
2. developing infrastructure for enterprise wide analytics
3. support for org transformation

Question 11

what are pain points

Answer 11

business problems

Question 12

what do we need to do bana

Answer 12

underlying technologies!! we need a lot of data with high transmission speed

Question 13

what is data management

Answer 13

we need to clean data and get into data marts and data warehouses through ETL

Question 14

bana tools

Answer 14

excel
OLAP (multi deminsional analysis)
data mining
dss
regression

Question 15

what is descriptive analytics

Answer 15

first step in data reduction- it summarizes what has happened in the past, enables decision makers to learn from past behaviours

Question 16

BA tools for descriptive analytics
OLAP
Data mining
dss
-sensitivty anlaysis
-what if analysis
-goal seeking analaysis

Answer 16

olap: slicing and dicing data (either drill down or roll up) USE DATA CUBES

data mining: identifying patterns and predicting trends in large database

DSS:mathematical models to analyze semistructred & unstructured problems
-sensitivity: how sensitive to change
-what if: concerning uncertainty
-goal seeking: backward solution approach

Question 17

descriptive analytics apps names

Answer 17

fandango
optumRx

Question 18

Predictive analytics meaning

Answer 18

examine recent and historical data to detect patterns and predict future trends

Question 19

pPredictive analytics BA Tools
data mining
linear regression

Answer 19

Data mining: use vast amounts of data to predict the future!! ex:targeted marketing, forecasting levels of bad loans, tracking crime patterns

linear regresison: using data to predict futurre with statistics

Question 20

example of predictive analytics paplications

Answer 20

FANDANGO: sending emails to people who might be interested in concerts similar to what they went too

websites predicting which ads youll click on

leading online dating sites!

Question 21

consequences of preditcitvie analytics UNINTENDED

Answer 21

-Target: preicted woman was pregnant and started iving ad
=Uber: RAISING rates too high as trying to get away from a shooter

Question 22

how do descriptive analytics and predictive analytics work together to bridge gap between data management and actionable busienss deciison

Answer 22

DATA MANAGEMENT:
To investigate this business problem, you collect data on service calls for the past year from the CAASCO locations within your district. You first “clean” the data by adjusting for outliers and missing values, resulting in a final sample of 3,219 service calls.
You are now ready to build a regression model using temperature as the independent variable to predict the number of customer service calls received on any given day; this is the dependent variable. You will use the results to predict how many employees should be available to receive and dispatch customer service calls.

DESCRIPTIVE ANALYTICS: or this example, the correlation between daily low temperature and the number of service calls was found to be –0.84, with an average of 48 service calls per day. The correlation is negative because as the temperature decreases, the data indicate that the number of service calls increases.
The square of this correlation, R2 = .71, is the predictive power of the model. That is, the model, using low daily temperature, explains approximately 71 percent of the variation in the number of calls received each day. Therefore, 1 minus R2 means that 29 percent of the variance in the dependent variable is due to extraneous or unexplained variables.

PREDICTIVE ANALYITSC: The manager has decided to use linear regression for the predictive analysis. To do so, certain reasonable assumptions must be met:

There must be at least 30 data points.
The relationship between the independent and dependent variables must be linear. The linearity assumption can best be tested with scatterplots.
Even though the data are assumed to be normally distributed, the manager should check this assumption.

The sample size of 3,219 satisfies the first assumption. The manager then used Excel to test the second assumption, producing a scatterplot to determine if the plot of each ordered pair of data (independent variable, dependent variable) produced a linear pattern. The scatterplot for the data did exhibit a linear pattern. Therefore, linear regression is an acceptable statistical procedure for these data.
To check for normality, the manager used the Ryan-Joiner normality test to calculate the correlation between the data and the normal scores of the data. If the value is near 1, then the sample dataset is likely to be normal. This dataset met the normality assumption.
Now that the three linear regression assumptions have been met, the next step is to define the linear regression model between these two variables using Excel or a similar statistical package. The linear regression model is:

Number of calls received = 124.79 – 1.5 × (daily low temperature)

These results indicate that for one degree of increase in the daily low temperature, the predicted daily number of calls received will decrease by 1.5. That is, CAASCO will expect to receive fewer calls on warmer days and more calls on colder days. At a temperature of 0 degrees (x = 0), the expected number of calls will be approximately 125.

ACTIONABLE BUSINESS:Based on the linear regression, the district manager is able to use the projected daily low temperature for up to 14 days in advance to predict how many service calls the offices will receive each day. (The Weather Network provides reasonably accurate daily temperatures on a 14-day outlook.) Therefore, the manager can predict how many employees will be needed to manage the expected number of service calls in order to ensure low wait times for the customers.

ASK NEXT QUESTION: t this point, the manager can return to the data management stage with new input variables. For the CAASCO data in this example, it is feasible to consider another business problem with the appropriate inputs or to expand the analysis by considering other variables relevant to the business question in the example. For example, the manager might want to include the actual time of day, by hour, so that staffing levels could be more accurately decided. The manager also might want to examine the location of the CAASCO branches as a variable. Adding these variables would require the use of new regression models.
You also want to recall that our example is for the South Central Ontario district, where temperatures are warmer than in some other regions of the country. Therefore, if we were to predict data in the northern regions, then we would have to build a different model based on the data from the North.
Our example proceeds from data management, to descriptive analytics, to predictive analytics (through a simple linear regression model). We address how the results of predictive analytics often lead to additional questions that include additional variables, which would require a multiple linear regression model.
From a statistical perspective, we might ask: Aren’t there many different analytical approaches to solving the same problem? The answer is yes. But a more important question to ask is: Which one approach is the best? The answer to this question is—none! The best approach depends on the kind of data you are working with. And, because data come in all shapes and sizes, there cannot be one best approach for all problems. Therefore, selecting the best model for the particular data is always an important exercise in data analytics.

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Question 23

What is prescriptive abalytics

Answer 23

recommends a course of action, identified likely outcome of each deciison

Question 24

BA tools used in prescirptive analyics

Answer 24

fandango: using the most desirable movie time to maximize profit by raising price

google driverless car: ccar anyicipating pedestrians to not hit them

Question 25

presentation tools
-dashboards
-GIS

Answer 25

dashboards: simple and easy access to timely information, create reports

gIS: is a computer-based system for capturing, integrating, manipulating, and displaying data using digitized maps. Its most distinguishing characteristic is that every record or digital object has an identified geographical location. This process, called geocoding, enables users to generate information for planning, problem solving, and decision making. The graphical format also makes it easy for managers to visualize the data. There are countless applications of GISs to improve decision making in both the public and private sectors.
For example, when the Toronto Police Service decided to develop a tool to improve the understanding of policing, increase transparency, and enhance confidence in the Service, it found that using geospatial data can help it accomplish this mission better. The result was a public safety data portal build using GIS software from ESRI (www.esri.ca).
The tool provides a centralized platform to easily access useful and timely crime data. A wide range of crime information, including shootings, homicides, and major crime indicators, are included in the app. In addition, users can select a set of dates, types, and neighbourhoods based on which the app will query the dataset and visually depict the results on a map. This app is used by various users, such as researchers, media outlets, university professors, students, and residents concerned about public safety in their neighbourhood. The Vancouver Police Department has also developed a similar app using GIS software from ESRI.
These apps help visualize crime hot spots and enable crime analysts to study the reasons for certain areas being targets of criminal activities. Therefore, the apps enable the police to develop crime prevention strategies and tactics in order to reduce the number of crime incidents. In addition, these apps can increase police transparency and accountability as they shed light on the key activities and crime issues that the police are engaged with.

Question 27

bana and bi are interchangeable

Answer 27

word

Question 28

what are dashboards

Answer 28

provide easy access to timely info and direcct access to management reports

(user friendly, craphics, management can drill down orscale up)

Question 29

strategic/tactical dashboards

Answer 29

-SHOW KPIS, and analyze historical data and visualize trends

Question 30

operational and informational dashboards

Answer 30

understand events, projects ro asset statuts

inform and engagee audiences

Question 31

what is a network operations centre

Answer 31

a centralized location where computer, telecommunications or satellite networks systems are monitored and managed 24-7

CONTROL ROOM

Question 32

WHAT DOES THE CPS THING HERE SHOW

Answer 32

VISUALIZATIONS