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How CPAs can prepare for handling the big data world 9.22.2021 | MICHELLE SINGERMAN

Successfully analyzing major data sets may mean brushing up on current skills—and learning some new ones, too

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Using different skills to analyze big data, such as natural curiosity, will be advantageous to future accountants (Getty Images/eclipse_images)

Digitization is affecting many facets of day-to-day living. According to a recent report, e role of professional accountants in data (/en/foresight-initiative/data-governance/role-professional- accountants-in-data), from CPA Canada and the International Federation of Accountants (IFAC), digital data was estimated to be at 40 zettabytes (40 trillion gigabytes) in 2020, up from 1.2 zettabytes in 2010.

Maintaining relevancy in the digital age of AI-compiled data sets, as the report shows, means accountants will have to learn new skills and competencies to succeed. One such strategy being undertaken by the CPA profession is the dra of the new “Way Forward” Competency Map (CM2.0 (/en/news/accounting/the-profession/2021-07-07-competency-map)), which was developed to better align the needs of this changing world with the skills of an accountant. 

“e data sets are changing and getting bigger,” says FCPA Tim Jackson, chair of the Competency Map Task Force (/en/become-a-cpa/why-become-a-cpa/the-cpa-certi�cation-program/the-cpa- competency-map/competency-map-task-force) and CEO of Shad Canada, Canada’s premier summer enrichment program for high school students focused on STEAM (science, technology, engineering, arts and mathematics) and entrepreneurship. “But we also have access to more tools that make it easier to select those datasets.” 

As Jackson points out, CPAs are already skilled at analyzing data and completing sample selections. “In many ways,” he says, “I don’t think [the role] is changing, the information is changing.”

CONTINUOUS LEARNING

To remain successful in this changing digital landscape, Jackson says CPAs will have to continue being lifelong learners. Upskilling through various courses (/en/foresight-initiative/data- governance) and CPD credits will help accountants be in the know when it comes to digital trends and data crunching. Much of this, he says, will need to happen both through training and by organizations providing roles that allow employees to evolve—such as data scientist and data controller, as the IFAC and CPA Canada report states (/en/foresight-initiative/data- governance/role-professional-accountants-in-data). 

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“at’s an investment [employers] have to make,” he says. But he adds that the responsibility also rests on employees, who must have an appetite for continued learning—whether through training offered by employers or staying up to date on new information on their own accord. 

“is idea of being curious will be the key characteristic for new CPAs,” says Jackson. “If we bring people into the profession who are curious, it implies they’re constantly trying to �gure out what’s new, what’s coming. And, with that curiosity, comes knowledge.”

POSITIVE CHANGE

e upside to the shiing role, according to Jackson, is that it allows accountants to do more analysis. “We still need to apply and validate and understand the historical data, but now it’s the interpretation, saying ‘how do I then use our data moving forward to inform decision making,’” he says.

Traditionally, the accounting profession has had a historical approach, looking back to understand how things will look in the future. But, as Gigi Dawe, corporate oversight and governance lead at CPA Canada explains, “Big data is bringing more unstructured and intangible data (/en/foresight-initiative/data-governance/mastering-data/canadas-digital-economy-and- the-cpa), and data that will help you to assess what’s going to happen versus telling you what did happen, for measuring. at’s the biggest challenge that we’re facing—we don’t really have experience in dealing with this unstructured or intangible data.”

While CPAs have the tools to handle an in�ux in digital information, Dawe explains that the introduction of big data means accountants must now shi how they manage it. “We’ve really got to start looking at what we do with that AI-enabled data and working with that,” she says, “and learn those skills and ensure that we’re understanding and dealing with that.” 

To tackle this hurdle, CPA Michael Lionais, managing director, Technomics Canada—which specializes in decision support—and consultant on CPA Canada’s Foresight (/en/foresight- initiative) initiative, says accountants will have to learn a new data language, analyzing unstructured data against the new ways it is processed, “so that [accountants] actually take the data and get it into a format that they can then use,” he says. 

EXPECT THE UNEXPECTED

e most important skill Lionais foresees accountants needing? Learning to accept uncertainty. “e accounting profession is all about precision and reconciliation,” he says. “What we’re going to have to start learning is, how do we embrace uncertainty and learn how to re�ect that in the

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advice that we are giving?”

e other concern, he says, is whether or not CPAs have the mindset for this. “It’s not just, ‘can you develop the skills to understand data and manipulate data,’” he says, but you must have the inquisitiveness to appropriately manipulate the data, too. 

Lionais sees these changes as an opportunity for the profession to move into more integrated, cross-functional roles. “It becomes a much more relevant, much more interesting, much more dynamic profession,” he says. 

ENHANCE YOUR DATA KNOWLEDGE

Expand your skills with CPA Canada’s data management cour (/en/career-and-professional- development/webinars/core-areas/management-accounting/management-reporting-needs-and- systems/data-management-certi�cate)se and delve deeper into the roles professional accountants can get involved in (/en/foresight-initiative/data-governance/role-professional-accountants-in- data) to oversee and manage data.

Also, learn to capitalize on the bene�ts of the digital economy (/en/career-and-professional- development/webinars/trends-issues/technology-and-information-management/cpas-accelerate- digital-transformation-with-dcam) and �nd out where the profession is headed in the future (/en/foresight-initiative). 

About the Author

Michelle Singerman Michelle is a Toronto-based writer and digital content creator who began her career in local news

reporting more than a decade ago. Michelle has been with CPA Canada since 2013.

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https://www.youtube.com/watch?v=6qPZJfe5jXc

“Big data is NOT about the data.”

Gary King, Harvard University

“If you torture the data long enough, it will confess.”

Ronald Coase, economist

“Information is the oil of the 21st century, and analytics is the combustion engine.”

Peter Sondergaard, then Head of Research, Gartner Research

Data Analytics in Auditing

©McGraw-Hill Education

1

Learning Objectives

Identify situations in which audit data analytics can be used in gathering audit evidence.

Understand the steps that are taken in performing audit data analytics.

Understand the requirements for documentation of audit data analytics.

Identify some of the tools that can be used for performing audit data analytics.

Apply data analysis techniques to client financial statement data.

Analyze output from audit data analytic techniques.

©McGraw-Hill Education.

2

The Auditing Data and Analytics Cycle

©McGraw-Hill Education.

Advantages of data analytics in audit

4

Customization

Tailor the analytics solutions to support client needs (e.g. journal entry testing)

Predictability

Ability to replicate processes across type of work and client engagements

Test Size

Provides ability to test entire population instead of a sample

Data Insight

Visualization and analytics tools allow for a better view of the data and pinpoints areas of interest for auditors

Efficiency

Performance of data analytics maximizes time spent structuring data into information

PwC | Applications of data analytics in auditing

©McGraw-Hill Education.

4

Common Uses of Audit Data Analytics

Risk Assessment Procedures

Trend analysis of inventory costs

Preliminary three-way match testing in the revenue cycle

Accounts receivable collection periods by region

Inventory aging and days inventory in stock by item

Tests of Controls

Proper approval of purchase transactions over a threshold

Employees and Suppliers with same address

Journal entry testing by employee entry amount limits

Substantive Analytical Procedures

Predictive model of interest expense

Aging of accounts receivable

©McGraw-Hill Education.

5

Heat Map of Fraud Risk Factors

©McGraw-Hill Education.

6

Common Uses of Audit Data Analytics (cont.)

4. Tests of Details

Comparing cash collections to sales invoices and discounts

Analysis of capital expenditures vs repairs and maintenance

Detailed recalculation of depreciation using entire database and exact purchase dates

5. Procedures to help form an overall conclusion

Gross profit percentage by class of revenue

©McGraw-Hill Education.

7

Visualization to Assess Control Environment

©McGraw-Hill Education.

8

Visualization of Word Cloud – Employee Morale

©McGraw-Hill Education.

9

Visualizations to Assess the Market’s Perception of a New Product

©McGraw-Hill Education.

10

Visualizations to Assess the Market’s Perception of a New Product

©McGraw-Hill Education.

11

Visualizations Depicting Uncertainty around a Line Graph of Price Increase

©McGraw-Hill Education.

12

Conducting Audit Data Analytics (AICPA)

©McGraw-Hill Education.

13

Step 1: Plan the ADA

Determine the significant financial statement accounts and relevant assertions that are being tested.

Specific relevant assertion about a significant account

Determine the nature, timing, and extent of the work that will be completed as part of the ADA.

Specify the exact purpose and specific objectives of the ADA.

WCGW?

Select the techniques and tools to be used.

©McGraw-Hill Education.

14

Step 1: Plan the ADA (cont.)

Determine the population to be analyzed or tested, including matters which may affect the relevance and reliability of the data.

Completeness and Accuracy

System Reliability

Select the ADA that is best suited for the purpose.

©McGraw-Hill Education.

15

Step 2: Access and Prepare the Data

Data must be assessable and in a usable format.

Clients may store data in a variety of formats and systems, e.g. Enterprise Resource Planning (ERP) and external data repository (cloud)

Many generalized audit software tools, such as IDEA, can import from a variety of sources.

No commonly used standardized format exists, although voluntary Audit Data Standards exist.

Auditor must ensure data security and integrity.

Management may be concerned that auditor access leads to data breaches or customer confidentiality concerns.

Auditor may need to subject their systems to reliability testing.

©McGraw-Hill Education.

16

Step 2: Access and Prepare the Data (cont.)

Cleansing of data

Some fields may be empty, which could lead to errors in analysis.

Date fields may have numbers or letters.

Data may be outside relevant date range.

Format of dates may vary (D-M-Y vs M-D-Y vs Y-M-D).

Country-specific differences, such as currency ($1.22 vs 1,22E)

©McGraw-Hill Education.

17

Step 3: Consider the Relevance and Reliability of the Data Used

The auditor must consider whether the data has a logical connection to the purpose of the audit procedure and the assertion being tested.

What data would be most relevant to performing the ADA?

Is the data considered most relevant available?

If not, are there alternative ways to obtain the data? Alternative data that could be used?

Similarly, auditors must evaluate the reliability of any data used in ADA.

Source reliability

Nature and relevance of information available

Internal controls over data preparation

©McGraw-Hill Education.

18

Step 3: Consider the Relevance and Reliability of the Data Used (cont.)

Completeness and Accuracy of data must be ensured.

Reliability of accounting systems and Information Technology General Controls (ITGCs) must be tested prior to using data from a client system.

To determine the reliability of data, the auditor may consider

Whether the ADA is a risk assessment procedure, a test of controls, etc.

The risk assessment associated with the account/assertion

The extent of other audit procedures

The nature and source of data (e.g. internal vs. external)

The process used to produce the data

Additional procedures to ensure data reliability

©McGraw-Hill Education.

19

Step 3: Consider the Relevance and Reliability of the Data Used (cont.)

Characteristics of data that may affect relevance and reliability

Nature (e.g. financial vs. non-financial, historic, time-sensitive, economic, etc.)

Source (controlled by accounting department, controlled internally but outside accounting department, external)

Format (numerical, text, fixed fields, unstructured)

Timing (point in time or period of time, rate of change)

Extent (volume and variety of subject matter/sources)

Level of Aggregation (account balance vs. transaction, annual vs. hourly, consolidated vs. segment)

©McGraw-Hill Education.

20

Characteristics of Data

©McGraw-Hill Education.

21

Step 4: Perform the ADA

Actual performance of the ADA varies greatly depending on the purpose of the ADA.

If initial results indicate ADA needs to be revised, consider revisions and reperformance.

If the ADA has been properly designed and performed, consider additional procedures on identified items that warrant further attention.

©McGraw-Hill Education.

22

Step 5: Evaluate the Results and Conclude

Have the objectives of the ADA been achieved?

If not, plan and perform different procedures.

Gather additional evidence to help reduce risk of material misstatement; design and perform procedures on notable items.

Duplicate items.

Missing items.

Items with higher assessed risk.

Address risk of material misstatement for remaining population items.

Consider whether risk of material misstatement exists in items not identified as notable.

It may be appropriate for auditor to conclude that no additional risk of material misstatement is present.

Document work performed.

©McGraw-Hill Education.

23

Documentation Requirements

AU-C 230 applies to ALL audit documentation, including ADA.

Documentation should be prepared to be sufficient such that an experienced auditor, with no prior connection with the engagement can understand:

Nature, timing and extent of procedures performed

Results of procedures and evidence obtained

Conclusions reached and significant judgments made

The auditor should record:

Identifying characteristics of specific items or matters tested

Who performed the work and date of performance

Who reviewed the work, date of review, and extent of review

©McGraw-Hill Education.

Documentation Requirements (cont.)

Auditor may record the scope of the procedure and population analyzed.

No requirement to include the data analyzed (generally impractical)

Possible documentation specific to ADA:

Objectives of the procedure

Risks of material misstatements addressed at the financial statement or assertion level

Sources of the data and how it was determined to be sufficient and appropriate (complete and accurate)

The nature of the ADA and the tools and techniques used

Tables or graphics used, including how they were generated

Steps taken to access data, including the system accessed and how the data were extracted and transformed

Evaluation of matters identified as a result of applying the ADA and actions taken

Identifying characteristics of specific items or matters tested

Preparer and reviewer information as required by AU-C 230

©McGraw-Hill Education.

Documentation Requirements (cont.)

Screenshots of graphics generated in performing an ADA may be included in documentation.

Only graphics necessary to support the auditor’s work and conclusions should be included.

The auditor need not document every matter considered or professional judgment made.

All misstatements identified other than those considered clearly trivial should be documented.

©McGraw-Hill Education.

Common Tools Used in ADA

Generalized Audit Software

IDEA

ACL

Data Preparation and Statistical Analysis Tools

Alteryx

R

SAS

Python

Visualization Tools

Tableau

Microsoft Power BI

All-Purpose Tools

Excel

©McGraw-Hill Education.

27

Professional Skepticism in ADA

An auditor must plan and perform an audit with professional skepticism, and must exercise professional judgement.

Some areas where professional skepticism and judgment apply in ADA:

Assessing the completeness and accuracy of client data

Making assumptions in planning the procedures and evaluating the results

Considering unusual circumstances

Appropriately generalizing in drawing conclusions

©McGraw-Hill Education.

9/10/2020 Data Governance in Digital Transformation - Strategic Finance

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MAG A ZINE TOPIC S BLOGS ABOUT US

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T E C H N O L O G Y |

DATA G OV E R NA N C E I N D I G I TA L T R A N S F O R M AT I O N BY ROD KOCH, CMA, CSCA, PMP, CSM, AND TATYANA CORBAN, CPA

September 1, 2020

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Data policies, corporate culture, organization structure, technology infrastructure, and workforce development are the

foundations of data governance.

What does digital transformation mean to you? For many, it means the rapid

creation of personalized customer experiences. But digital transformation is also

driving a surge in data, requiring careful management and control with

heightened attention to the security and privacy of the customer information

10

9/10/2020 Data Governance in Digital Transformation - Strategic Finance

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that enables it. The recent Harvard Business Review (HBR) research “A Blueprint

for Data Governance in the Age of Business Transformation” (bit.ly/346v5uw)

shows that corporate executives, senior and middle managers, and other cross-

functional stakeholders understand these constraints and view investments in

data governance as a way to enable data-driven decision making, enhance their

organization’s reputation, improve competitiveness by protecting intellectual

property (IP), and reduce the costs and fines associated with data breaches.

Creating trust by applying robust data governance also helps organizations retain

and attract customers while increasing revenues. How can organizations meet

the expectations of rolling out digital transformation and responding quickly to

customer needs while protecting corporate IP and customer information?

According to the HBR research, creating effective data governance rests on five

pillars: (1) data policies, (2) corporate culture, (3) organization structure, (4)

technology infrastructure, and (5) workforce development.

DATA POLICIES

Before creating data policy, the first step is to define what data governance is

appropriate for your organization. Data governance is a data management system

that ensures that business objectives are supported by high-quality data and

controls across the complete life cycle of data. It supports data availability,

usability, consistency, integrity, and security by establishing accountability for

data quality and promoting accessibility and proper use of data across the

organization.

Experts agree that effective data governance is one of the first principles of

proper data management. Data governance identifies what data will be collected,

how it will be collected and protected, and how data compliance and

confidentiality requirements will be achieved. Creating effective data policies

9/10/2020 Data Governance in Digital Transformation - Strategic Finance

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and systematically communicating them throughout the organization will ensure

that all employees are consistently aware and follow proper data security and

management protocols.

The next step is to define all valuable or potentially valuable organizational data,

including all customer data, and to perform a data policy gap analysis. The

analysis should include all business units and consider both internal policies and

external regulations. A risk-assessment heat map should be created to identify

and close the gaps.

Now create or update the policies based on the results of the findings, giving top

priority to areas with the highest ROI and potential impact. Finally, set up an

ongoing review process to continue updating the policies as needed, based on

business, legal, and regulatory compliance as well as changes in the economic

environment.

CORPORATE CULTURE

Corporate culture often requires significant changes for an organization to

become a data-driven enterprise. Why is creating a data-driven culture so

important? Gartner advises, “Culture and data literacy are the top two

roadblocks for data and analytics leaders” (gtnr.it/3kSGIv3). Overcoming these

roadblocks by creating a data-driven culture allows organizations to better serve

their customers and accelerate decision making.

Tableau advises that data-driven cultures require five common elements: trust,

commitment, talent, sharing, and mind-set. “Becoming truly data-driven

requires changing mindsets, attitudes, and habits—embedding data into the

identity of the organization. People have to want to use data and encourage

others to do the same. In a Data Culture, people ask the hard questions and

challenge ideas. They come together with a shared mission to improve the

9/10/2020 Data Governance in Digital Transformation - Strategic Finance

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organization and themselves with data. Leaders inspire through action, basing

decisions on data, not intuition” (tabsoft.co/3iRLs2q). For organizations to

successfully adopt these new cultural norms, leadership must choose and

systematically apply a change management methodology, including a strong

communication plan.

ORGANIZATION STRUCTURE

To bring sustainable change in establishing data-driven culture, the most

successful organizations have added the role of chief data officer (CDO).

NewVantage Partners’ Annual Big Data Executive Survey 2018 found that 62.5%

of senior Fortune 1000 business and technology decision makers said their

organization had appointed a CDO. The CDO’s primary purpose is to provide

leadership in treating data as an organizational asset, with robust and

comprehensive data governance. CDOs work with IT and business-unit leaders

to identify and communicate the business value of the data and then lead all

aspects of data strategy around data management, including governance.

Another prominent C-suite role with the specific focus on driving information

security initiatives and programs pertaining to internal and external threads is

that of chief information security officer (CISO). More than half of regulated

industry organizations surveyed by HBR agreed about the essential role of the

CISO.

Having a CDO and CISO isn’t enough. Good data governance requires cross-

functional cooperation and leadership. Senior executives must understand the

importance and ROI of data as an asset and become its stewards and enthusiastic

supporters of data governance. CFOs can be instrumental in leading the charge,

due to their broad understanding of financial and organizational data. All

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business-unit leaders should align with the data governance strategy and follow

the correct policies and procedures. Good data governance will increase

customer trust and reduce the risk of its loss.

TECHNOLOGY INFRASTRUCTURE

Investing in security infrastructure and data governance monitoring improves

governance maturity. Leading organizations pursue anti-malware, data-flow

tracking, e-discovery, and behavior-monitoring investments.

Understanding what data exists, which data is confidential, and how the data is

being used can be simplified using the correct technology tools. And applying

regular updates to infrastructure reduces the risk of breaches providing customer

reassurance, which is critical in maintaining both B2B and B2C customer

relationships.

WORKFORCE DEVELOPMENT

The weakest security link in most organizations is their workforce. Most

malware breaches occur because of employee mistakes. Organizations need

“soft” training (e.g., how to recognize phishing attacks, comply with

security/privacy policies, etc.) as well as training in any new tools.

Effective data governance rests on the five key pillars of data policies, corporate

culture, organization structure, technology infrastructure, and workforce

development. Although data governance is often behind digital transformation,

by focusing on these pillars, data governance can catch up and support digital

transformation innovations while protecting corporate IP and customer

information.

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All views, thoughts, and opinions expressed belong solely to the authors, and not

to the authors’ employers.

0

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Rod Koch, CMA, CSCA, PMP, CSM, is a member of IMA’s Technology Solutions and Practices Committee and the IMA Global Board of Directors. He can be reached at [email protected].

Tatyana Corban, CPA, is a member of IMA’s Technology Solutions and Practices Committee, IMA’s Portland Chapter, and the Society for Information Management, Portland Chapter, board of directors. Follow her on LinkedIn at bit.ly/3kCBDH5.

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©McGraw-Hill Education

“Big data is NOT about the data.” Gary King, Harvard University

“If you torture the data long enough, it will confess.” Ronald Coase, economist

“Information is the oil of the 21st century, and analytics is the combustion engine.” Peter Sondergaard, then Head of Research, Gartner Research

Big Data and Auditing

• A collection of data sets that are too large or too complex to analyze them with traditional databases and tools.

• Standard descriptions usually include: • Volume • Variety • Velocity • Veracity

What is Big Data?

March 3, 2017 16th Annual Accounting Educators Seminar - University of

Missouri - Kansas City

What is Big Data?

March 3, 2017

http://www.ey.com/gl/en/services/advisory/ey-big-data-big-opportunities-big-challenges 16th Annual Accounting Educators Seminar - University of

Missouri - Kansas City

• Accounting professionals need to know how to conduct data analytics regardless of whether it is “Big”.

• Transactional Data can tell us what has happened, Big Data and data analytics can often help explain why.

• We need to embrace both.

Data vs. Big Data

March 3, 2017 16th Annual Accounting Educators Seminar - University of

Missouri - Kansas City

What is the Impact on the Accounting Professional?

March 3, 2017 16th Annual Accounting Educators Seminar - University of

Missouri - Kansas City

• Audit – Internal and External

• Data driven audits

• Better experience for the client

• Better experience for the auditor

• More valuable insights

• Improving corporate compliance

Implications for Accounting Professionals

March 3, 2017 16th Annual Accounting Educators Seminar - University of

Missouri - Kansas City

• Advisory Services

• Identify questions

• Use analytics to help business improve performance

• Build analytical models

Implications for Accounting Professionals

March 3, 2017 16th Annual Accounting Educators Seminar - University of

Missouri - Kansas City

• An employee with the following skills:

• Ability to understand big data technology structures • Ability to construct experiments, gather and analyze data, make evidence-

based decisions • Strong communication skills • Strong quantitative skills in statistical analysis, visual analytics, machine

learning, and ability to analyze unstructured data • Business expertise – a good sense of where to apply analytics and big data

16th Annual Accounting Educators Seminar - University of Missouri - Kansas City

What are employers looking for?…

March 3, 2017

©McGraw-Hill Education.

Data and Analytics

• Data are facts and statistics collected together for reference or analysis.

– known or assumed as facts

• Payroll register

• Sales Journal

– make the basis for reasoning or calculations

• Analytics are the systematic computational analysis of data.

– Research potential trends

• Evaluate causes of increase in employee costs

– Identify risks

• Identify missing sales invoice numbers

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Social Media Text Analysis Please Insert Exhibit G.1

©McGraw-Hill Education.

Data Chain

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Analytics Chain

©McGraw-Hill Education.

The Next Generation of Auditing

• Currently, auditors focus on client data, as do most companies.

– Internal auditors have used big data to detect insurance and purchasing card fraud based on

anomalous payments.

– Target sends ads to women deemed “likely pregnant” based on specific non-baby-related purchases

and upset a teenage girl’s father by sending advertisements for baby supplies based on her

purchases. Turned out, Target knew before she did!

• However, it is easy to see how auditors could improve risk assessments and analytical

procedure expectations using external data.

– Walmart: Hurricanes increased sales of not only flashlights and water, but Pop tarts by 7x the

normal rate!

– Using Google’s Profile of Mood States and 10 million tweets, researchers predicted stock price

changes 3-4 days in advance.

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PwC | Applications of data analytics in auditing

A taxonomy for analytics

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PwC | Applications of data analytics in auditing

A taxonomy for analytics

•Descriptive(and diagnostic) analytics–What is happening? Why it is happening?

•Traditional business intelligence (BI) and visualizations (pie-charts, bar-charts, line-graphs, tables, or generated narratives).

©McGraw-Hill Education.

PwC | Applications of data analytics in auditing

A taxonomy for analytics

•Predictive analytics–“What is going to happen?” (What is likely to happen?)

•Regression analysis, forecasting, multivariate statistics, pattern matching, predictive modeling, and forecasting (among others).

©McGraw-Hill Education.

PwC | Applications of data analytics in auditing

A taxonomy for analytics

•Prescriptive analytics–“What should be done?” (or What can we do to make something happen?)

•Graph analysis, simulation, complex event processing, neural networks, recommendation engines, heuristics, and machine learning (among others).

©McGraw-Hill Education.

PwC | Applications of data analytics in auditing

Examples of analytics in ITGC

18

02

New User Testing

• Appropriate management needs to approve access to all new users

• A brand new employee that is a telephone operator should not get access to edit financial data

04 Revocation Testing • Appropriate management should revoke

access to users who no longer require access to an application

• If an employee leaves a company, he or she does not need access to any of the company’s applications.

ITGC’s

03 Change Management

• Controls are put in place to prevent the Segregation of Duties (SOD) risk, in which user roles are clearly distinguished to prevent an overlap of responsibilities.

• Developers and deployers should not be the same person.

• Users who have the ability to post financial data to systems should not have the ability to also approve the transactions.

• Appropriate management needs to approve every change that is made to an application.

• This ITGC is used to prevent unnecessary or harmful changes from being deployed to the application

01 SOD

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PwC | Applications of data analytics in auditing

Examples of analytics in key calculations/reports

19

• Companies rely on certain key calculations to assist in financial reporting.

• Procedure of testing key calcs entails understanding the underlying calculation, receiving and validating the input data, and reperforming the calculation.

Key calculations

Key reports testing

• Key reports are systematically generated reports which show the results of the key controls in an application.

• Companies test the completeness and accuracy of each key report.

• Management makes critical business decisions based on the results of these reports.

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PwC | Applications of data analytics in auditing

Big Data in the auditing field

•The pace of adoption of BD&A in statutory audit has been lower than in other fields (e.g. internal audit, marketing, strategic decision-making)

•Using BD&A in auditing is about enhancing audit quality

•BD&A is being approached in the auditing practice with the aim of improving the efficiency and effectiveness of audits

•BD&A has the potential to represent the most significant shift in how audits are performed since the adoption of paper less audit tools and technologies

©McGraw-Hill Education.

PwC | Applications of data analytics in auditing

Big Data in the auditing field

©McGraw-Hill Education.

PwC | Applications of data analytics in auditing

Big Data in the auditing field: what are the benefits?

•Auditors can test a (far) greater number of transactions, overcoming sample limits

•Auditors can test a (far) greater number of transactions, overcoming sample limits

•Audit quality can be increased by providing grater insights on auditee's processes

•Frauds will be easier to detect

•Auditors can better plan the audit engagements

©McGraw-Hill Education.

PwC | Applications of data analytics in auditing

Big Data in the auditing field: what are the benefits?

©McGraw-Hill Education.

PwC | Applications of data analytics in auditing

Challenges of Big Data in Auditing

•Focus of data analysis toward recognizing patterns within large amounts of data

•Consequent to continuous auditing systems the numbers of identified exceptions and anomalies are expected to increase dramatically

•Prioritization methodologies which incorporate the decision-support systems can greatly help alleviate the burden of processing information

•Lack of the adequate training and required skills to analyze Big Data

  • Slide 1
  • What is Big Data?
  • What is Big Data?
  • Data vs. Big Data
  • What is the Impact on the Accounting Professional?
  • Implications for Accounting Professionals
  • Implications for Accounting Professionals
  • What are employers looking for?…
  • Data and Analytics
  • Social Media Text Analysis
  • Data Chain
  • Analytics Chain
  • The Next Generation of Auditing
  • A taxonomy for analytics
  • A taxonomy for analytics
  • A taxonomy for analytics
  • A taxonomy for analytics
  • Examples of analytics in ITGC
  • Examples of analytics in key calculations/reports
  • Big Data in the auditing field
  • Big Data in the auditing field
  • Big Data in the auditing field: what are the benefits?
  • Big Data in the auditing field: what are the benefits?
  • Challenges of Big Data in Auditing

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