Chapter 1 Cook - By the Book

Look at Hands on Example on Page 13 of Text Book or LO 1-6

Need to Look here before Exam

I of IMPACT is?

Identify the Questions

T of IMPACT is?

Track Outcomes

Accountants should know the following regarding data analytics:

1. Clearly articulate the business problem the company is facing. 2. Communicate with the data scientists about specific data needs and understand the underlying quality of the data. 3. Draw appropriate conclusions to the business problem based on the data and make recommendations on a timely basis. 4. Present their results to individual members of management (CEOs, audit managers, etc.) in an accessible manner to each member.

7 Skills Analytic minded accountants should have:

1. Develop an analytics mindset—recognize when and how data analytics can address business questions. 2. Data scrubbing and data preparation—comprehend the process needed to clean and prepare the data before analysis. 3. Data quality—recognize what is meant by data quality, be it completeness, reliability, or validity. 4. Descriptive data analysis—perform basic analysis to understand the quality of the underlying data and its ability to address the business question. 5. Data analysis through data manipulation—demonstrate ability to sort, rearrange, merge and reconfigure data in a manner that allows enhanced analysis. 6. Define and address problems through statistical data analysis—identify and implement an approach that will use statistical data analysis to draw conclusions and make recommendations on a timely basis. 7. Data visualization and data reporting—report results of analysis in an accessible way to each varied decision maker and his or her specific needs.

Classification Definition

A data approach that attempts to assign each unit in a population into a few categories potentially to help with predictions. Cont: An attempt to assign each unit (or individual) in a population into a few categories. Example: 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 Definition

A data approach that attempts to characterize the "typical" behavior of an individual, group, or population by generating summary statistics about the data (including mean, standard deviations, etc.). Example: 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-Occurance Definition

A data approach that attempts to discover associations between individuals based on transactions involving them. An attempt to discover associations between individuals based on transactions involving them. Example: 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

Clustering Definition

A data approach that attempts to divide individuals (like customers) into groups (or clusters) in a useful or meaningful way. 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 elements and the underlying drivers of those groups. Example: For example, clustering might be used to segment a customer into a small number of groups for additional analysis and marketing activities.

Regression Definition

A data approach that attempts to estimate or predict, for each unit, the numerical value of some variable using some type of statistical model. A data approach used to predict a specific dependent variable value based on independent variable inputs using a statistical model. Example: An example regression analysis might be, given a balance of total accounts receivable held by a firm, what is the appropriate level of allowance for doubtful accounts for bad debts?

Similarity Matching Definition

A data approach that attempts to identify similar individuals based on data known about them. An attempt to identify similar individuals based on data known about them. Example: 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 Definition

A data approach that attempts to predict a relationship between two data items. 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? Example: 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 otherwise disclosed.

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, highest risk, largest impact, etc.). Example: An example might include the potential to use these techniques 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.

A of IMPACT is?

Address and Refine Results

C of IMPACT is?

Communicate Insights

M of IMPACT is?

Master the Data

Big Data

Datasets that are too large and complex for businesses' existing systems to handle utilizing their traditional capabilities to capture, store, manage, and analyze these datasets.

P of IMPACT is?

Preform the Tests

Data Analytics

The process of evaluating data with the purpose of drawing conclusions to address business questions. Indeed, effective Data Analytics provides a way to search through large structured and unstructured data to identify unknown patterns or relationships.

Book Summary

With data all around us, businesses and accountants are looking at Data Analytics to extract the value that the data might possess. Data Analytics is changing the audit and the way that accountants look for risk. Now, auditors can consider 100 percent of the transactions in their audit testing. It is also helpful in finding anomalous or unusual transactions. Data Analytics is also changing the way financial accounting, managerial accounting, and taxes are done at a company. The IMPACT cycle is a means of doing Data Analytics that goes all the way from identifying the question, to mastering the data, to performing data analyses and communicating results. It is recursive in nature, suggesting that as questions are addressed, new important questions may emerge that can be addressed in a similar way. Eight data approaches address different ways of testing the data: classification, regression, similarity matching, clustering, co-occurrence grouping, profiling, link prediction, and data reduction. These are explained in more detail in Chapter 3. Data analytic skills needed by analytic-minded accountants are specified and are consistent with the IMPACT cycle, including the following: 1. Develop an analytics mindset. 2. Data scrubbing and data preparation. 3. Data quality. 4. Descriptive data analysis. 5. Data analysis through data manipulation. 6. Define and address problems through statistical data analysis 7. Data visualization and data reporting.

Response AKA (Dependent Variables)

also known as dependent variables A variable that responds to, or is dependent on, another.

Predictor AKA (Explanatory / Dependent Variable)

also known as independent variables A variable that predicts or explains another variable, typically called a predictor or independent variable.

Mastering the Data requires?

requires one to know what data are available and whether those data might be able to help address the business problem. We need to know everything about the data, including how to access, availability, reliability (if there are errors), and what time periods are covered to make sure the data coincide with the timing of our business problem, etc.