Jim Lindell - Analytics and Big Data for Accountants (AICPA)

1. Overview
2. Chapter 1
3. Chapter 2
4. Chapter 4
5. Chapter 5

1. Overview
2. Chapter 2
3. Chapter 4
4. Chapter 5

BY J IM L INDELL , MBA, CPA, CGMA

Notice to readers

Analytics and Big Data for Accountants is intended solely for use in continuing professional education and not as a reference. It does not represent an official position of the Association of International Certified Professional Accountants, and it is distributed with the understanding that the author and publisher are not rendering legal, accounting, or other professional services in the publication. This course is intended to be an overview of the topics discussed within, and the author has made every attempt to verify the completeness and accuracy of the information herein. However, neither the author nor publisher can guarantee the applicability of the information found herein. If legal advice or other expert assistance is required, the services of a competent professional should be sought.

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ISBN 978-1-11978-462-3 (Paper)
ISBN 978-1-11978-468-5 (ePDF)
ISBN 978-1-11978-467-8 (ePub)
ISBN 978-1-11978-469-2 (oBook)

Course Code: 746275
DAAN GS-0420-0A
Revised: May 2020

Traditional accounting is giving way to a hybrid of accounting, and analytics has been the driving force. Tools have been created to measure program effects and return on investment, visualize data and business processes, and uncover the relationship between key performance indicators many using the unprecedented amount of data now moving into organizations. In this course, you will discuss leadingedge topics in analytics and finance in a session that is packed with useful tips and practical guidance that you can apply immediately.

The accountant role will continue its migration from data creator, manipulator, and archivist to one of data scientist and storyteller. It is imperative that the accountant of today keep pace with technological change and recognize the need to move from historical analysis to predictive change and ultimately, prescriptive change.

Its against this backdrop that the advent of big data and analytics has already affected many organizations and will play a much more significant role in the future.

• Evidence-based techniques for finding or generating data, selecting key performance indicators, isolating program effects
• Relating data to return on investment, financial values, and executive decision-making
• Data sources including surveys, interviews, customer satisfaction, engagement, and operational data
• Visualizing and presenting complex results

• : What Are big data and Analytics?
• : Big Data History big data Sources and Characteristics
• : What Are the T rends in big data?
• : What Are the Strategy and Business Applications of big data?
• : Big Data Platforms and Operating Tools
• : Big Data End User and Accounting Tools
• : Examples of big data
• : Big Data in the Accounting Department
• : Ethics and Privacy With big data

As you progress through the course, consider the following challenges:

• What information does your organization currently have access to that is already in your systems and available for use?
• What information could you have access to that exists in your organization but is not captured?
• What information exists on the internet in regard to industry databases or government databases that you could access?
• What information is available through sensors and machines that could provide insights into the operational and strategic dynamics of your business?

It would be helpful to spend a couple of minutes to jot down the type of information your organization currently has available. This analysis will be helpful as you consider different applications of analytics and big data. Use to gather your information.

What information does your organization have available?

Before becoming immersed in the overall topic, it may be useful to discuss a broad picture of big data so that all participants have an initial understanding. Consider :

TEUs Twenty-Foot Equivalent Units

IPS Industrial Production Statistics

S&P Standard & Poors

NYMEX New York Mercantile Exchange