Ch 1 Data Analytics for Accounting and Identifying the Questions (Textbook)

Explain the new approach to accounting that developed from Data Analytics.

-No longer will they be simply checking for errors, material misstatements, fraud, and risk in financial statements or merely be reporting their findings at the end of the engagement. -Instead, audit professionals will now be collecting and analyzing the company's data similar to the way a business analyst would to help management make better business decisions -external auditors will stay engaged with clients beyond the audit -the audit process will be changed from a traditional process toward a more automated one, which will allow audit professionals to focus more on the logic and rationale behind data queries and less on the gathering of the actual data.

Data Analytics

-The process of evaluating data with the purpose of drawing conclusions to address business questions. -effective Data Analytics provides a way to search through large structured and unstructured data to identify unknown patterns or relationships -involves the technologies, systems, practices, methodologies, databases, statistics, and applications used to analyze diverse business data to give organizations the information they need to make sound and timely business decisions -aims to transform raw data into knowledge to create value

how does data analytics affect tax?

-With the shift in focus in tax, executives must develop sophisticated tax planning capabilities that assist the co. with minimizing its taxes in such a way to avoid or prepare for a potential audit -this makes tax data analytics valuable for its ability to help tax staffs to predict what will happen rather than reacting to what just did happen. (predictive analytics) -ex. of predictive analytics: capability to predict the potential tax consequences of a potential international transaction, R&D investment, or proposed merger or acquisition

profiling

-an attempt to characterize the "typical" behavior of an individual, group, or population by generating statistics about the data (including mean, std. dev., etc.) -by understanding the typical behavior, we'll be able to more easily ID abnormal behavior -When behavior departs from the typical behavior, which we'll call an anomaly, then further investigation is warranted. ex. used to ID accounting fraud or just those transactions that might warrant some additional investigations, like travel expenses that are three std. dev. above the norm

What musts accountant know how to do, concerning DA?

-clearly articulate the business problem the company is facing -communicate with the data scientists about specific needs and understand the underlying quality of the data -draw appropriate conclusions to the business problem based on the data and make recommendations on a timely basis -present their results to individual members of mgmt (CEOs, audit managers, etc.) in an accessible manner to each member

Data Analytics impact on Financial Reporting

-data analytics will improve the quality of estimates and valuations in financial reporting -Date analytics may also allow an accountant/auditor to assess the probability of a goodwill write down, warranty claims, or the collectability of bad debt based on what customers, investors, and other stakeholders are saying about the co. in blogs and in social media (like Facebook and Twitter). This information might help the firm determine both its optimal response to the situation and appropriate adjustment to its financial reporting. -may be possible to use Data Analytics to scan the environment-that is, scan Google searches and social media (Instagram and Facebook) to ID potential risks and opportunities to the Firm. ex. may allow the firm to monitor its competitors and customers to better understand opportunities and threats around it. ex. are it's competitors, customers, or suppliers facing financial difficulty, etc., that might affect the company's interactions with them and/or open up new opportunities that otherwise it wouldn't have considered?

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 -3 Vs: volume (the sheer size of the data set) velocity (the speed of data processing) variety (the number of types of data)

What are the issues of performing predictive Data Analytics?

-efficient organization and use of data stored across multiple systems on varying platforms that were not originally designed for the tax department -organizing data into a data warehouse to model and query the data is the most important step

Why does Data Analytics matter to businesses?

-estimates state it could generate up to $3 trillion in value per year in just a subset of the total possible industries affected -could transform the manner in which companies run their businesses in the near future because the real value of data comes from Data Analytics -use Data analytics to discover the various buying patterns of their customers, investigate anomalies that were not anticipated, forecast future possibilities, and so on. -Data analytics affects internal processes, improving productivity, utilization, and growth

How has Data Analytics impacted Audit?

-given many public accounting firms incentives to invest in technology and personnel to capture, organize, and analyze financial statement data to provide enhanced audits, expanded services, and added value to their clients. -Data analytics is expected to be the next innovation in the evolution of the audit and professional accounting industry

Chapter Objectives

1. Define Data Analytics. 2. Understand why Data Analytics matters to businesses. 3. Explain why Data Analytics matters to accountants. 4. Describe the Data Analytics Process using the IMPACT cycle. 5. Describe the skills needed by accountants. 6. Explain how to translate common business questions into fields and values.

General steps of the IMPACT cycle

1. Identify the Questions 2. Master the Data 3. Perform test plan 4. Address and refine results 5 & 6: Communicate Insights and Track Outcomes Back to step 1

Progress check: 1. How does having more data around us translate into value for a company? 2. Banks know a lot about us, but they have traditionally used externally generated credit scores to assess creditworthiness when deciding whether to extend a loan. How would you suggest a bank use Data Analytics to get a more complete view of its customers' creditworthiness? Assume the bank has access to a customer's loan history, credit card transactions, and direct deposit registration. How could it assess whether a loan might be repaid?

1. The plethora of data alone does not necessarily translate into value. However, if we carefully use the data to help address critical business problems and questions, the data may create value 2. Banks could certainly use credit scores from companies like Experian, TransUnion, and Equifax, but if they have access to all of the banking information of their clients, arguably they could make more informed decisions. Banks would know how much money they have and how they spend it. Banks would know if they had prior loans and if they were paid in a timely manner. Banks would know where they work and their monthly income via the direct deposits. All of these combined, in addition to a credit score, might be used to assess creditworthiness to gain a better evaluation of customers' creditworthiness when they would like a loan. It might also give us needed information for a marketing campaign to target potential creditworthy customers.

7 skills that 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 it's 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

Progress Check: 3. Let's assume a brand manager at Samsung identifies that an older demographic might be concerned with the use of Samsung Galaxy smartphone and the radiation impact it might have on the brain. How might Samsung use Data Analytics to assess if this a problem? 4. How might Data Analytics assess the higher cost of paying employees to work overtime? Consider how Data Analytics might be helpful in reducing a company's overtime direct labor costs of manufacturing setting.

3. The brand manager at Samsung might use Data Analytics to see what is being said about Samsungs phones on social media websites, particularly those that attract an older demographic. This will help the manager assess if there is a problem with the perceptions of its phones. 4. Data analytics might be used to collect information on the amount of overtime. Who worked overtime? What were they working on? Do we actually need more full-time employees to reduce the level of overtime? All of these questions could be addressed by looking at recent records explaining the use of overtime records.

Progress Check: 5. How could the use of internal audit data analytics find the pattern that one accountant enters the majority of journal entries each quarter? How might this data be used to check is segregation of duties was appropriately maintained? Why might this be an issue that would need addressing? 6. How specifically will data analytics change the way a tax staff does it's taxes?

5. Data analytics could tabulate the number of journal entries by an accountant to see who entered the most journal entries. This might be an issue if there was a perception of a problem in risk, such as segregation of duties in having one person enter so many journal entries or just how the accounting workload is distributed across accounting staff. 6. The tax staff would become much more adept at efficiently organizing data from multiple systems across an organization and performing Data Analytics to help with tax planning to structure transactions in a way that might minimize taxes.

Explain the A in the IMPACT cycle

A - Address and Refine Results -slice and dice the data, find correlations, ask further questions, ask colleagues what they thing, and revise and rerun the analysis -after that, we have the results ready to communicate to interested stakeholders

Explain the C and T in the IMPACT cycle

C&T - Communicate Insights and Track Outcomes -Insights are formed by decision makers and are communicated and some outcomes will be continuously tracked -ways to communicate results: use of static reports, digital dashboards, and data visualizations

Explain the M of the Impact Cycle

M - Master the data -mastering the data requires one to know what data are available and whether those data might be able to help address the problem -NTK everything about the data - 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

Explain the I of the Impact Cycle

I - Identify the Questions -understanding a business problem that needs addressing -having a specific question that is potentially answerable by DA is the first step ex qs: -are employees circumventing internal controls over payments? -are there any suspicious travel and entertainment expenses? -how can we increase the amount of add-on sales of additional goods to our customers

Explain the P in the impact cycle

P - Perform test plan -analysis of the data -identify a relationship between the RESPONSE or DEPENDENT variables and those items that affect the response (aka PREDICTOR, EXPLANATORY, or INDEPENDENT variables), by making a model to address this purpose

What are the 8 different approaches to data analytics depending on the question:

Written by Provost and Fawcett 1. Classification 2. Regression 3. Similarity matching 4. Clustering 5. Co-occurence grouping 6. profiling 7. link prediction 8. data reduction

Does Data Analytics matter to businesses?

Yes, bc: -gives a competitive advantage -the area of investment that tops CEO's list of priorities is business analytics

Does Data Analytics play an important role in the future of audit?

Yes; Forbes Insights/KPMG report states the vast majority of survey respondents believe: 1. Audit must better embrace technology. 2. Technology will enhance the quality, transparency, and accuracy of the audit.

Is data preparation and scrubbing an important part of mastering the data?

Yes; Data Analytics professionals estimate that they spend between 50-90% of their time cleanings data so the data can be analyzed

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 (highest cost, highest risk, largest impact) - takes 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 ex. while auditing has employed various random and stratified sampling over the years, DA suggests new ways to highlight which transactions do not need the same level of vetting as other transactions

regression

a data approach used to predict a specific dependent variable value based on independent variable inputs using a statistical model ex. given a balance of total AR held by a firm, what is the appropriate level of ADA for bad debts?

co-occurrence grouping

an attempt to discover associations between individuals based on transactions involving them ex. Amazon using this by selling items "frequently bought together" or "customers who bought this item also bought" ...

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 elements and the underlying drivers of those groups ex. clustering might be used to segment a customer into a small number of groups for additional analysis and marketing activities

classification

an attempt to assign each unit (or individual) in a population into a few categories ex. of all the loans this bank has offered, which are most likely to default? Or which loan application are expected to be approved? or which transactions would a credit card co. flag as potentially being fraudulent and deny payment

similarity matching

an attempt to identify similar individuals based on data known about them ex. Alibaba IDying 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 -used in social media -ex. because an individual might have 22 FB friends with me and we both attended BYU, is there are chance we would like to be FB friends as well??

predictive analytics

predicting the future

what is arguably one of the things that Data Analytics does best?

predictive analytics

Draw the IMPACT Cycle

see textbook diagram

model

simplified representation of reality

What's the final step in the IMPACT cycle?

there is no final step, it's a cycle