ACC 409: AIS - FINAL EXAM QUESTIONS AND ANSWERS
WITH COMPLETE SOLUTIONS VERIFIED
data warehouse
is a collection of information gathered from an assortment of external and operational
(i.e, internal) databases to facilitate reporting for decision making and business analysis.
Data marts
-Subset of data warehouse
-Summarized or highly focused portion of firm's data for use by specific population of
users
-Typically focuses on single subject or line of business
Business Intelligence
is a computer-based technique for accumulating and analyzing data from databases
and data warehouses to support managerial decision making.
processes of BI
1.Gather information (either internal information, external information or both) from a
variety of sources.
2.Analyze the data to discern patterns and trends from that information to gain
understanding and meaning.
3.Make decisions, hopefully better informed ones, based on the information gained.
Data mining
,is one technique used to analyze data for business intelligence purposes. Data mining
is a process using sophisticated statistical techniques to extract and analyze data from
large databases to discern patterns and trends that were not previously known.
- find patterns
Digital dashboard
displays on a single screen graphs and charts of key performance indicators for
managing a company
XBRL
eXtensible Business Reporting Language is a variant of XML (eXtensible Markup
Language) specifically designed for use in communicating the content of financial data
XBRL taxonomy
defines and describes each key data element
XBRL instance documents
contain the actual dollar amounts or the details of each of the elements within the firm's
XBRL database
XBRL style sheets
take the instance documents and add presentation elements to make them readable by
humans
XBRL does
- XBRL serves as a means to electronically communicate business information to
facilitate business reporting of financial and nonfinancial data to users.
- XBRL greatly enhances the speed and accuracy of business reporting
big data
,a broad term for datasets so large or complex that traditional data processing
applications are inadequate.
volume
refers to the massive amount of data involved.
Velocity
is the speed that data is being generated (stock prices change on a micro-basis) or the
rate that data is being analyzed (continuous monitoring of heart rate)
Variety
refers to structured, semi-structured and unstructured and unprocessed data:
structured data
Balance Sheet and Income Statement
unstructured data
Comments in social media, blogs, pictures posted in Instagram.
semi-structured data
elements of structured and unstructured data
veracity
refers to the quality of the data including extent of cleanliness (without errors or data
integrity issues), reliability and representationally faithful.
Data analytics
the science of examining raw data (often described as Big Data), removing excess
noise and organizing the data with the purpose of drawing conclusions for decision
making.
Data analytics insights
, patterns, investigate anomalies, forecast future behavior and so forth.
Benefits and Costs of the Use of Data Analytics on Business
- Companies generally face two important limiting factors in their business systems
when dealing with Big - - Data: storage and processing.
- Storage: Many companies choose to use a cloud platform to lower the cost of data
storage. (Example: AWS by Amazon, Azure by Microsoft)
- Processing Power: The processing power required to obtain information valuable to
the company could be enormous or even impossible.
- Cost is also a consideration - - Twitter data, economic data ,etc.
ETL
Extract, Transform, and Load. Used to standardize data across systems, that allow it to
be queried.
- Before data can be analyzed and be useful, it must be scrubbed from extraneous data
and noise.
- Reformatting, cleansing, and consolidating large volumes of data from multiple
sources and platforms can be especially time consuming. Data analytics professionals
estimate that they spend between 50 percent and 90 percent of their time cleaning data
for analysis.
AMPS Model Data Analytics
1.Ask the Question.
2.Master the data.
3.Perform the analysis.
Share the story.
WITH COMPLETE SOLUTIONS VERIFIED
data warehouse
is a collection of information gathered from an assortment of external and operational
(i.e, internal) databases to facilitate reporting for decision making and business analysis.
Data marts
-Subset of data warehouse
-Summarized or highly focused portion of firm's data for use by specific population of
users
-Typically focuses on single subject or line of business
Business Intelligence
is a computer-based technique for accumulating and analyzing data from databases
and data warehouses to support managerial decision making.
processes of BI
1.Gather information (either internal information, external information or both) from a
variety of sources.
2.Analyze the data to discern patterns and trends from that information to gain
understanding and meaning.
3.Make decisions, hopefully better informed ones, based on the information gained.
Data mining
,is one technique used to analyze data for business intelligence purposes. Data mining
is a process using sophisticated statistical techniques to extract and analyze data from
large databases to discern patterns and trends that were not previously known.
- find patterns
Digital dashboard
displays on a single screen graphs and charts of key performance indicators for
managing a company
XBRL
eXtensible Business Reporting Language is a variant of XML (eXtensible Markup
Language) specifically designed for use in communicating the content of financial data
XBRL taxonomy
defines and describes each key data element
XBRL instance documents
contain the actual dollar amounts or the details of each of the elements within the firm's
XBRL database
XBRL style sheets
take the instance documents and add presentation elements to make them readable by
humans
XBRL does
- XBRL serves as a means to electronically communicate business information to
facilitate business reporting of financial and nonfinancial data to users.
- XBRL greatly enhances the speed and accuracy of business reporting
big data
,a broad term for datasets so large or complex that traditional data processing
applications are inadequate.
volume
refers to the massive amount of data involved.
Velocity
is the speed that data is being generated (stock prices change on a micro-basis) or the
rate that data is being analyzed (continuous monitoring of heart rate)
Variety
refers to structured, semi-structured and unstructured and unprocessed data:
structured data
Balance Sheet and Income Statement
unstructured data
Comments in social media, blogs, pictures posted in Instagram.
semi-structured data
elements of structured and unstructured data
veracity
refers to the quality of the data including extent of cleanliness (without errors or data
integrity issues), reliability and representationally faithful.
Data analytics
the science of examining raw data (often described as Big Data), removing excess
noise and organizing the data with the purpose of drawing conclusions for decision
making.
Data analytics insights
, patterns, investigate anomalies, forecast future behavior and so forth.
Benefits and Costs of the Use of Data Analytics on Business
- Companies generally face two important limiting factors in their business systems
when dealing with Big - - Data: storage and processing.
- Storage: Many companies choose to use a cloud platform to lower the cost of data
storage. (Example: AWS by Amazon, Azure by Microsoft)
- Processing Power: The processing power required to obtain information valuable to
the company could be enormous or even impossible.
- Cost is also a consideration - - Twitter data, economic data ,etc.
ETL
Extract, Transform, and Load. Used to standardize data across systems, that allow it to
be queried.
- Before data can be analyzed and be useful, it must be scrubbed from extraneous data
and noise.
- Reformatting, cleansing, and consolidating large volumes of data from multiple
sources and platforms can be especially time consuming. Data analytics professionals
estimate that they spend between 50 percent and 90 percent of their time cleaning data
for analysis.
AMPS Model Data Analytics
1.Ask the Question.
2.Master the data.
3.Perform the analysis.
Share the story.
