Please read the articles & watch the videos. (1) summarize and explain the main points of the articles that you choose from the assigned papers (2) conclude with your own opinio

©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

©McGraw-Hill Education.

Social Media Text Analysis Please Insert Exhibit G.1

©McGraw-Hill Education.

Data Chain

©McGraw-Hill Education.

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.

©McGraw-Hill Education.

PwC | Applications of data analytics in auditing

A taxonomy for analytics

©McGraw-Hill Education.

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

©McGraw-Hill Education.

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.

©McGraw-Hill Education.

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

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

https://sfmagazine.com/post-entry/september-2020-data-governance-in-digital-transformation/ 1/6

MAG A ZINE TOPIC S BLOGS ABOUT US

SEARCH STRATEGIC FINANCE 

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

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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

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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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“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

©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 Con

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