Richardson Ch 1

What is big data?

refers to datasets which are too large and complex to be analyzed traditionally.

What do accountants need to be able to do in relation to business analytics?

• Articulate business problems. • Communicate with data scientists • Draw appropriate conclusions • Present results in an accessible manner • Develop an analytics mindset Be comfortable with • Data scrubbing and data preparation • Data quality • Descriptive data analysis • Data analysis through data manipulation • Define and address problems through statistical analysis • Data visualization and data reporting

How does data analytics affect auditing?

-data analytics will enhance audit quality -data analytics enables enhanced audits, expanded services, and added value to clients -external auditors will stay engaged beyond the audit Theoretically replace staff auditors. Testing complete populations, but you never really know if it's a complete population

What is the IMPACT model?

1. Identify the questions 2. Master the data 3. Perform the test plan 4. Address and refine results 5. Communicate insights 6. Track outcomes

What are the 4 V's of Big Data?

1.Volume - size/quantity 2.Velocity - frequency, processing speed 3.Variety - different 4.Veracity - quality (reliability and accuracy)

Data Reduction

A data approach that attempts to reduce the amount of information that needs to be considered to focus on the most critical items (i.e., highest cost, high- est risk, largest impact, etc.). It does this by taking a large set of data (perhaps the population) and reducing it with a smaller set that has the vast majority of the critical information of the larger set. An example might include the potential to use these tech- niques in auditing. While auditing has employed various random and stratified sampling over the years, Data Analytics suggests new ways to highlight which transactions do not need the same level of vetting as other transactions.

Classification

An attempt to assign each unit (or individual) in a population intoa few categories. An example classification might be, of all the loans this bank has offered, which are most likely to default? Or which loan applications are expected to be approved? Or which transactions would a credit card company flag as potentially being fraudulent and deny payment?

Profiling

An attempt to characterize the "typical" behavior of an individual, group, or population by generating summary statistics about the data (including mean, standard deviations, etc.). By understanding the typical behavior, we'll be able to more easily identify abnormal behavior. When behavior departs from that typical behavior—which we'll call an anomaly—then further investigation is warranted. Profiling might be used in accounting to identify fraud or just those transactions that might warrant some additional investigation (e.g., travel expenses that are three standard deviations above the norm).

Co-occurence grouping

An attempt to discover associations between individuals based on transactions involving them. Amazon might use this to sell another item to you by knowing what items are "frequently bought together" or "Customers who bought this item also bought . . ." as shown in Exhibit 1-2.

Clustering

An attempt to divide individuals (like customers) into groups (or clusters) in a useful or meaningful way. In other words, identifying groups of similar data ele- ments and the underlying drivers of those groups. For example, clustering might be used to segment a customer into a small number of groups for additional analysis and marketing activities.

Regression

An attempt to estimate or predict, for each unit, the numerical value of some variable using some type of statistical model. An example regression analysis might be, given a balance of total accounts receivable held by a firm, what is the appro- priate level of allowance for doubtful accounts for bad debts?

Similarity Matching

An attempt to identify similar individuals based on data known about them. The opening vignette mentioned Alibaba and its attempt to identify seller and customer fraud based on various characteristics known about them to see if they were similar to known fraud cases.

Link Prediction

An attempt to predict a relationship between two data items. This might be used in social media. For example, because an individual might have 22 mutual Facebook friends with me and we both attended Brigham Young University, is there a chance we would like to be Facebook friends as well? Exhibit 1-3 provides an example of this used in Facebook. Link prediction in an accounting setting might work to use social media to look for relationships between related parties that are not other- wise disclosed.

How does data analytics affect financial reporting?

Better estimates of collectability, write‐downs, etc. Managers can better understand the business environmentthrough social media Identify risks and opportunities through analysis of Internetsearches

Step 5

Communicate effectively using clear language and visualizations:

Step 6

Follow up on the results of the analysis.

Step 4

Identify issues with the analyses, possible issues, and refinethe model

What happens in step 1?

Identify the Question: Understand the business problems that need to be addressed

Step 2

Master the data: Know what data are available and how they relate to the problem

Step 3

Perform the Test Plan: Select an appropriate model to find a target variable. Classification Regression Etc

How does data analytics affect taxes?

Tax strategy and planning Understanding of tax consequences of internationaltransactions, investment, mergers and acquisitions Better organization of tax tables and other tax data

What is data analytics and what is the purpose?

The process of evaluating data with the purpose of drawing conclusions to address business questions. Data Analytics aims to transform raw data into knowledge to create value. Effective Data Analytics provides a way to search through largestructured and unstructured data to identify unknown patterns or relationships.