Study sets, textbooks, questions
Upgrade to remove ads
Chapter 6 - Business Intelligence and Analytics
Terms in this set (85)
What does BI stand for?
BI stands for Business Intelligence.
What is Business Analytics? - Definition
Business analytics is the extensive use of data and quantitative analysis to support fact-based decision making within organizations.
What are the five things that Business Analytics can be used for/to do?
Business Analytics can be used for/to do:
1. Gain a better understanding of current business performance.
2. Reveal new business patterns and relationships.
3. Explain why certain results have occurred.
4. Optimize current operations.
5. Forecast future business results.
What does BI stand for again?
BI stands for Business Intelligence.
What is Business Intelligence? - definition
Business Intelligence includes a wife of applications, practices, and technologies for the extraction, transformation, integration, visualization, analysis interpretation, and presentation of data to support improved decision making.
The data that is used in BI is oftentimes pulled from blank sources.
The data that is used in Business Intelligence is oftentimes pulleys from multiple sources?
What type of sources can the data b pulled from?
The data to Business Intelligence may be pulled from multiple sources that are either internal or external to the organization (the data may come from both - internal and external data sources).
The data can be used to build large collections of data. True or False? What are the three large collections of data?
True; data can indeed be used to build large collections of data.
The three large collections of data are:
What are the five benefits that can be achieved with the use of both Business Intelligence and Analytics (Business Analytics):
There are a total of five benefits that can be achieved with the effective use of both business Intelligence and Business Analytics:
Who are the individuals who view situations from many angles and determine what tools to be used for better understanding of a situation?
The individuals who view a situation from many angles and determine the best tools to use in order to better understand situations are data scientists.
Data scientists are individuals who combine what? - there are three things!
Data scientists are individuals who combine:
Strong business acumen
A deep understanding of Analytics
A healthy appreciation of the limitations of her data, tools, and techniques to deliver real improvements.
Data scientists often work in what type of settings with business managers and specialists?
Data scientists often work in team settings with business managers and specialists.
Data scientists view situations from blank blank.
Data scientists view situations from many angles.
These men and women determine what data and tools should be used to further an understanding of the situation. True or False?
True; Data Scientists do indeed determine what data and tools should be used to further an understanding of the situation.
Data scientists are highly blank.
Data scientists are highly inquisitive.
Are the educational requirements in order to become a data scientist rigorous? What are they?
The education requirements in order to become a data scientist are indeed quite rigorous:
Requires a mastery in mathematics, statistics, and computer programming.
Certain positions for a data scientist may require what type of degree?
Certain positions for or of a data scientist may indeed require an advanced degree.
What are three analytical-related disciplines that many schools offer courses, degrees, and even certificates for?
Three analytical-related disciplines that many colleges offer courses, degrees, and even certificates for are:
How is the job outlook for data scientists?
The job outlook for data scientists is extremely bright:
I believe that in the upcoming years, there will be a 140,000-190,000 job shortage for the data scientist career - many jobs will be available!
What are the three components required for effective Business Intelligence and Analytics?
The three components required for effective business Intelligence and Analytics are:
1. Existence of a solid data management program, including data governance.
2. Creative data scientists
3. Management team
What is data governance?
Data governance defines the roles, responsibilities, and processes needed for ensuring that the data can be trusted and used by the entire organization.
For the management team: the management team must have a strong commitment to what?
The management team must have a strong commitment to data-driven decision making.
What are five out of the eight business Intelligence and Analytics tools that can be used?
Five out of the eight business Intelligence and Analytics tools that can be used are:
Data Visualization tools
Online Analytical Processing (OLAP)
Reporting and Querying tools.
Business managers often import data into a spreadsheet program. True or False?
Definitely true. Business managers do indeed oftentimes import data into a spreadsheet program.
What can the spreadsheet be used for?
The spreadsheets can be used to perform operations on the data based on formulas created by the end user.
What two other things, based on the data, can spreadsheets formulate?
Based on the data available, spreadsheet programs are able to create/formulate both reports and graphs.
What is the Excel Scenario Manager used to do?
The excel scenario manager is used to perform what-if analysis to evaluate other alternatives.
What has been used to better understand the clinical use of drugs, the efficacy of treatment, and the associated costs? - a spreadsheet program.
Microsoft Power BI for Office 365 has been used to better understand the clinical use of drugs, the efficacy of treatment, and the associated costs.
Explain figure 6.1 - Microsoft Power BI for Office 365.
The data itself is collected from a variety of sources that can be both internal and external to the organization. The data is extricated and transformed, and with the use of a power pivot, linked (calculated) to Microsoft excel - this is where (power view) graphs and reports (and tables and maps - power map) are created from the basis of the data - these figures are then distributed and shared with other workers through the sharepoint program.
Reporting and querying tools are used to present day in what way?
Reporting and querying tools are used to present data in an easy-to-understand fashion.
The data is presented in an easy-to-understand fashion via:
Reporting and querying tools are used to present data in an easy-to-understand fashion via:
Many of these reporting and querying tools allow individuals to make their own data requests and format the results without what?
Many of the reporting and querying tools allow for the users to make data requests and formate the results without the need of additional help from the IT organizations.
What does data visualization mean?
Data visualization corresponds to the presentation of data in a pictorial or graphical format.
Representing data in visual form(s) automatically brings impact to full and boring numbers. True or False?
Representing data in visual form(s) does indeed bring an immediate impact to both dull and boring numbers. True.
What is a word cloud?
A word cloud is a visual depiction of a set of words grouped together based on the frequency of their occurrence.
What is a conversion funnel?
A conversion funnel is a graphical representation that summarizes the steps a consumer takes in making a decision to buy a product and become a customer.
What does OLAP stand for?
OLAP stands for Online Analytical Processing.
What is OLAP? Definition
Online Analytical Processing (OLAP) is a method that is used to analyze multidimensional data from many different perspectives.
OLAP allows users to identify issues and opportunities to perform blank analysis.
Online Analytical Processing enables users to identify issues and opportunities to perform trend analysis.
Data cubes contain numeric facts - True or False?
True; data cubes do indeed contain numeric facts.
What are these numeric facts within data cubes known as? What are they categorized by? Examples?
Data cubs contain numeric facts that are known as Measures. Measures are categorized by dimensions. Two examples of dimensions can be time and geography.
Data cubes can be built to summarize what?
Data cubes can be built to summarize unit sales of a specific item on a specific day for a specific store.
What are the three dimensions that are on the data cube of figure 6.5.?
The three dimensions (that categorize the numeric facts or measures of a data cube) that are in the data cube in figure 6.5. are:
1. Metric dimension
2. Time dimension
3. Geography dimension.
What is drill-down analysis?
Drill-down analysis involves the interactive examination of high-level summary data in increasing detail to gain insight into certain elements.
Explain the four levels of drill-down analysis associated with the example of the VP of a company wanting to review the worldwide sales for the past quarter.
The four levels of drill-down analysis associated with the example of a Vice President wanting to review the worldwide sales for the past quarter may be:
Drill down to view the sales for each country
Drill down even more to examine the sales for a particular country during the last quarter.
The third level drill-down analysis may be done to see the sales of a specific county for a specific month of the quarter.
The fourth analysis may include drilling down to sales by product line for a particular for a particular country by month.
Is linear regression a mathematical technique?
Yes, linear regression is indeed a mathematical technique.
Linear regression definition.
Linear regression is a mathematical technique used for predicting the value of a dependent variable based on a single independent variable and the linear relationship between the two.
Does linear regression consist of finding the best-fitting straight line between various independent and dependent variables (typically on a graph)?
Yes, linear regression does indeed involve formulating a best-fitting straight line between many observations, independent variables, and their dependent variables (typically on a graph).
What is the linear regression line formula?
The linear regression formula is:
Y = a + bX + e
What does the X stand for?
The X is the value of the independent variable that is being observed.
Y is the value of the dependent variable that is predicted.
a is the value of Y when X=0; the Y intercept.
b is the slope of the regression line.
e is the error that must be dealt with when predicting the value of Y, in relation to a given value of X.
What are the (3) key assumptions that must be satisfied when using linear regression on a set of data?
The three key assumptions that must be satisfied when using linear regression on a set of data are:
1. A linear relationship between the independent variable (X) and the dependent variable (Y) must exist.
2. The errors in the prediction of the value of Y are distributed in a manner that approached the normal distribution curve.
3. The errors in the prediction of the value of Y are all independent of one another.
Data mining is a blank tool.
Data mining is a Business Intelligence Analytics tool.
What is Data Mining?
Data Mining is a BI Analytics tool that is used to explore large amounts of data for hidden patterns to predict future trends and behaviors for use in decision making.
What are the three most commonly used data mining techniques?
The three most commonly used data mining techniques are:
Association analysis corresponds to a set of algorithms that sorts through data and forms statistical rules about the relationships among the items. True or False?
Association analysis corresponds to the set of algorithms that sorts through data and forms statistical rules about he relationships among items.
What is neural computing?
Neural computing is one of the three most commonly used data mining techniques:
Historical data is examined for patterns that are used to make predictions.
Historical if-then-else cases are used to recognize patterns.
What does CRISP-DM stand for?
CRISP-DM stands for Cross-Industry Process for Data Mining.
What is the CRISP-DM?
The Cross-Industry Process for Data Mining is a six-phase structured approach for the planning and the execution of data mining.
What are the six phases associated with the Cross-Industry Process for Data Mining?
The six phases associated with the Cross-Industry Process for Data Mining are:
What are three examples of how data mining can efficiently be used?
Three examples of how data mining can efficiently be used are:
Analyze demographic data and behavior data of potential customers to identify those who would be the most profitable customers to recruit.
Study the demographic data and characteristics of an organization's most valuable employees to help focus recruiting effort.
Recognize how changes in the DNA sequence can cause someone to develop certain diseases such as Alzheimer's or cancer.
With dashboards, what are measures?
With dashboards, measures are metrics that are used to track progress in executing chosen strategies to attain organizational objectives and goals.
These metrics are also known as...
These metrics are also known as KPIs.
What does KPI stand for?
KPI stands for Key Performance Indicator.
What four things do KPIs or metrics consist of?
Key performance indicators, or metrics, consist of:
Identify the direction, target, measure, and time frame of this KPI example:
For a university. Increase the five-year graduation rate for incoming freshman to at least 80 percent starting with the graduating class of 2022.
The direction, target, measure, and time frame in this key performance indicator example are:
Increasing - direction
Incoming freshmen - measure
At least 80% - target
2022 - time frame.
A dashboard presents a set of KPIs...
A dashboard presents a set of key performance indicators of a process at a specific point in time.
Do dashboards provide rapid access to information in an easy-to-interpret and concise manner?
Yes, dashboards do indeed provide rapid access to information in an easy-to-interpret and concise manner.
Dashboards provide users at one level of the organization the information they need to make improved decisions. True or False?
False; Dashboards provide users at all levels of the organization's with information needed to improve decisions.
What type of dashboards draw data from sources in real time from various sources?
The type of dashboards that draw data in real time from various sources is known as Operational Dashboards.
Operational dashboards can be designed to draw data from various sources in real time, including:
Operational dashboards can be designed to gather data from various sources in real time, including:
Widely used BI software comes from many different vendors. True or False?
True; widely used BI software does indeed come from many different vendors.
Provide three widely used BI Software Vendors and their product(s) - give a description of the product of just two of them.
Three widely used BI software vendors and their products may be:
Hewlett-Packard (HP) - Autonomy IDOL - enables the processing of structured and unstructured data - can examine relationships between data to answer "why has this happened?"
Microsoft - Power BI for Office 365 - allows users to model and analyze data and query large data sets - also allows for the analyzation (example) of the clinical use of medicine, the efficacy of treatment, and the associated costs - data can be placed onto Excel.
Oracle - Business Intelligence
Oracle - Hyperion
What is self-service Analytics?
Self-service Analytics includes the training, techniques, and processes that are used to empower the end user. This individual is able to independently access data from approved sources and format the data in his or her own liking (perform their own analyses with an endorsed set of tools).
Self-service Analytics pushes for nontechnical users to make decisions based on blank and blank rather than intuition.
Self-service Analytics pushes for nontechnical users to make decisions based on facts and analyses rather than intuition.
Users can do what (3 things) with the use of self-service Analytics?
With self-service Analytics, users can do multiple things:
Gain new insights
Identify opportunities and issues
Speed up the decision making process by formulating heir own graphs, tables, charts, reports, and so on.
Is it true that data or technology professionals, in efficient self-service analytics programs, lose all of their governance over data control?
Absolutely FALSE; good self-service analytics programs are ones in which the data or technology professionals retain some form of data control and governance and prohibit/limit certain individuals from the IS staff from being involved in routine tasks.
Self-service Analytics tools should be blank to use. If they are not like this, end users will not find it very useful to create their reports and graphs and format their data (with limited training of course).
Self-service Analytics tools should be EASY to use - like this, they will be embrace by the end users and used to analyze complicated sets of data and to format the data itself into many different formats - all without extensive training.
What does figure 6.10, which includes the Importance of Data Management show?
Figure 6.10 illustrates the importance of having a data management team/program in order to make sure that the organization's self-service Analytics (analysis) and important/sensitive business information are in balance with one another.
What are the four pros associated with self-service BI and Analytics?
The four pros associated with self-service Business Intelligence and Analytics are:
This type of program gets the valuable data into the hands of the people who most need and most value it - the end users.
Encourages nontechnical users to make decisions based on facts on analyses rather than intuition.
Accelerated and improves decision making.
Business people from across the organization can take part in this self-service analytics program. They are able to analyze data and such without the extensive need of professionals - this can then help fill in the shortage gap of trained data scientists.
What are the four cons associated with Self-Service BI and Analytics?
The four cons associated with Self-Service Business Intelligence and Analytics are:
If not well-managed, the analyses of the data and the formatting of the data can become erroneous (and error-prone), making certain business decisions irrational, time consuming, and costly.
Different types of analyses can yield different types of results, which then makes it time-consuming when having to analyze all of the data once more. Also, analyses may be performed more than once for no reason, causing a waste of money (data islands).
Overspending and the use of unapproved data or BI and Analytics tools.
Sometimes, certain organizations may not have a great data management team, which eliminates the checks and balances of data analyzations and usage (extra costs and release of sensitive data).
Recommended textbook explanations
Computer Organization and Design MIPS Edition: The Hardware/Software Interface
David A. Patterson, John L. Hennessy
Introduction to the Theory of Computation
Introduction to Algorithms
Charles E. Leiserson, Clifford Stein, Ronald L. Rivest, Thomas H. Cormen
John Buck, William Hayt
Sets found in the same folder
Business Intelligence (CH6)
Chapter 6 - Business Intelligence
Chapter 6: Data and Business Intelligence
Chapter 6 (Business Intelligence)
Sets with similar terms
MIS 3305 ~ Esserman Exam 3
Chapter 9: Business Intelligence Systems
MIS 3305 Test 3 Esserman
Other sets by this creator
GRE Vocabulary (Verbal Reasoning/Analytical Writin…
Chapter 8: Segmenting and Targeting Markets
Chapter 9: Marketing Research: Vocabulary Terms
Chapter 10: Product Concepts - Vocabulary
Other Quizlet sets
ACCT 261 Exam #1
Internal Analysis Text
VOLUME 2 - UNIT 1 - Data Management
The ability of a CT system to maintain consistent Hounsfield values across the entire image of a homogeneous object is termed:
Today's data is mostly unstructured. What does that mean when merging Big Data sets? Provide an example of an unstructured data type.
25) In the research literature case study, the researchers analyzing academic papers extracted information from which source?
An Atlanta photographer who photographs her family home and neighborhood