Data Management and Analytics

Question 1

What is Big Data and the 5 V’s of Big Data?

Answer 1

• the corporate accumulation of massice amounts of data that can be used for analysis, commonly referred to as data analytics
• volume, velocity, variety, vercity and value

Question 2

Define volume

Answer 2

• the quantity or amount of data points

Question 3

Define velocity

Answer 3

• the speed of data accumulation or data processing

Question 4

Define variety

Answer 4

• the different types of data that are involved in the analysis
• unstructured, structured and semi-structured

Question 5

Define veracity

Answer 5

• represents the reliability, quality or integrity of the data (trustworthiness)

Question 6

Define value

Answer 6

• the insights Big Data can yield

Question 7

What is a primary key?

Answer 7

• unique identifiers for a specific row within a table and are made up of one or more attributes
• each row must have a unique primary key

Question 8

What is a foreign key?

Answer 8

• attributes in one table that are also primary keys in another table
• the link between a primary key in one table and a foreign key in another table is what creates a relationship between tables

Question 9

What is the extract, transform and load process (ETL)?

Answer 9

• the process in which data is captured from its source and transferred to an org’s custody so that it can then be further analyzed

Question 10

What is data extraction?

Answer 10

• can take the form of an automated process, semiautomated process or manual extraction
• the native source and the means of accessing the data must be determined in the initial ETL phase which will dictate the tools needed for designing the overall process of extraction

Question 11

What is manual extraction?

Answer 11

• a person may have to use specialized data mining software or write customized queries to obtain the data
• tools used must ensure the data is coming from the correct location and is complete and accurate

Question 12

What is data transformation?

Answer 12

• one of the most time consuming steps in the ETL process because it entails taking the often-unstructured raw data, cleaning it, manipulating it and validating it to ensure it is accurate and ready for analysis

Question 13

What is data validation?

Answer 13

• needed after transformation to ensure data is not lost or inappropriately modified in the cleaning process
• may be a visual review for simple data sets
• if data set is large, basic statistical sets may be required to ensure the data has maintained integrity

Question 14

What is data load?

Answer 14

• loading the data into a software program for analysis or into a data storage location
• main concern is that the data has been extracted and transformed into a format that is compatible with the software program or storage destination
• may be stored in an Operational Data Store (ODS), data warehouse, data mart or data lake

Question 15

What are the 4 key applications in data analytics?

Answer 15

Descriptive analytics- describing or explaining WHAT HAS occurred (summarizes the activity)

Diagnostic analytics- diagnosing or explaining WHY it occurred (uncovers correlations, patterns, and relationships)

Predictive analytics- predicting WHAT WILL occur (forecasts and projections)

Prescriptive analytics- prescribing WHAT COULD or SHOULD occur (recommendations and next steps)

the 2 D’s are backward looking
the 2 P’s are forward looking

Question 16

What is data mining?

Answer 16

• allows users to obtain data themselves from databases or data warehouses
• allows users to perform disgnostice analytics to drill down into underlying data to answer q’s/better understand the data
• cannot perform without a computer

Question 17

What are examples of the uses of data analytics?

Answer 17

Customer and Marketing analytics- supports digital marketing and allows a company to deliver timely, relevant and anticipated offers to customers

Managerial and Operational analytics- use data mining and data collection tools to plan for more effective business operations

Risk and Compliance analytics- used to monitor transactions through continuous monitoring, continuous auditing and fraud detection

Financial analytics- monitor financial performance through data mining and ratio analysis on a continuous basis

Audit analytics- assessing risk, providing assurance around certain ops, establishing thresholds and expectations, improving the quality of the audit by testing full populations

Tax analytics- organize tax info and guidelines, improve tax planning, monitor tax performance indicators