Analytics and Big Data for Accountants E-Book

Jim Lindell

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: AICPA
• Sprache: Englisch

Seitenzahl: 260

Veröffentlichungsjahr: 2020

Beschreibung

Why is big data analytics one of the hottest business topics today? This book will help accountants and financial managers better understand big data and analytics, including its history and current trends. It dives into the platforms and operating tools that will help you measure program impacts and ROI, visualize data and business processes, and uncover the relationship between key performance indicators. Key topics covered include: * 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

Leseprobe

ANALYTICS AND BIG DATA FOR ACCOUNTANTS

BY JIM LINDELL, 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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© 2020 Association of International Certified Professional Accountants, Inc. All rights reserved.

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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: 746275DAAN GS-0420-0ARevised: May 2020

Overview

Welcome to analytics and big data for accountants

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.

Introductory comments

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.

It’s 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.

Topics discussed

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

The chapters in this course

Chapter 1

: What Are big data and Analytics?

Chapter 2

: Big Data History — big data Sources and Characteristics

Chapter 3

: What Are the T rends in big data?

Chapter 4

: What Are the Strategy and Business Applications of big data?

Chapter 5

: Big Data Platforms and Operating Tools

Chapter 6

: Big Data End User and Accounting Tools

Chapter 7

: Examples of big data

Chapter 8

: Big Data in the Accounting Department

Chapter 9

: Ethics and Privacy With big data

Opening discussion

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?

Exercise

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 table 0-1 to gather your information.

Table 0-1: What information does your organization have available?

What Information Do You Have?

Customer

Vendor

Employee

Strategic

Operational

Other

1

1

1

1

1

1

2

2

2

2

2

2

3

3

3

3

3

3

4

4

4

4

4

4

5

5

5

5

5

5

6

6

6

6

6

6

7

7

7

7

7

7

8

8

8

8

8

8

9

9

9

9

9

9

10

10

10

10

10

10

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 illustration 0-1:

Illustration 0-1

TEUs — Twenty-Foot Equivalent Units

IPS — Industrial Production Statistics

S&P — Standard & Poor’s

NYMEX — New York Mercantile Exchange

MPP — Massively Parallel Processing

Data originates in a variety of places and in a variety of forms. Data from individual companies, organizations, streaming data, and so on accumulate in the cloud, as shown in illustration 0-1. Organizations access the information via in-house servers, laptops, tablets, and other mobile devices. Organizations may desire a unique combination of external databases and internal databases, including calculations, projections, and so on. That information is sent to multiple computers with individual processors to analyze the vast amounts of data. These multiple computers with their processors are known as massively parallel processing (MPP).

Example

Next, we will consider a simple example of how large amounts of data can be processed to help you create a narrative to supplement your accounting processes. Let’s assume that our company is in the lumber industry or is affected by lumber prices. The industrial production statistics are available at www.federalreserve.gov/RELEASES/g17/ipdisk/alltables.txt. The Federal Reserve’s monthly index of industrial production rates covers manufacturing, mining, and electric and gas utilities. The production index measures real output and is expressed as a percentage of real output.

The initial data found on that website looks like that in table 0-2. This raw data is overwhelming and not easy to interpret in its current form.

Table 0-2

We selected the IPS data for the NAICS codes B500001(Total Index Statistics for all industrial production) and G321(Wood Products Industry Statistics) to begin the analysis, as in table 0-3:

Table 0-3

Next, the monthly data was obtained for Louisiana-Pacific Corporation (LPX) from Finance.Yahoo.com, and then all three data series were combined utilizing Excel. Once the data series had been established, the graph in illustration 0-2 was generated.

Illustration 0-2

It’s easy to see that in graph form, this data comes to life in a new way. As we supplement our accounting skills with storytelling and data science, is it possible to make some summary conclusions from the data in this example? One interpretation is that:

The wood product production statistics seem to follow along a similar trend line as the LPX stock price.

The major IPS also appears to follow a similar trend.

A supposition could be that a decline in wood production precedes an overall economic decline (note the steep decline which corresponds to the Great Recession.)

A supposition could be that wood production statistics during the housing bubble were supported by an increase in LPX stock prices.

By manipulating the data available in just this small example, we were able to uncover trends that could be used to make business predictions. However, most real-life examples of big data are much more complex than this. Most use much larger databases and more sophisticated technological tools than Excel. This example was meant to demonstrate the fundamental concept of big data so that you can see how powerful it is and help you relate to it.

There is also a significant amount of confusing definitions in the big data, analytics arena. Consider the following image as it attempts to align various themes for the discussion today.

As the course develops, we will be exploring significantly larger and more varied types of data. The software programs used to interpret big data are more complex than Excel, although Microsoft has created a product — Power BI, which will most likely be the tool that accountants will prefer because of most accountants’ current levels of familiarity with Excel.

By the end of this course, you should be familiar with the sources, types, and trends of big data as well as the various tools available for processing and interpreting this information. We will take a look at some more examples and learn how you can apply these techniques in your own practice.

Chapter 1What Are Big Data and Analytics?

Learning objectives

Identify the three different types of data.

Recall what type of data volume big data represents.

Recognize big data terminology.

Introduction

In the early 20th century, businesses kept track of financial and operational results using paper and ink. It was difficult enough just to record the date of the transactions, let alone summarize information with financial statements. The main form of automation that helped improve the efficiency of accounting clerks was limited to innovations in carbon copy paper, mimeograph machines, copy machines, and the like. When computers were finally available for operational and financial use, the systems were based on a batch recording of transactions. Again, the focus was on capturing internal data to help an organization understand its financial and operational results. As computers advanced and became more powerful, the focus increased in obtaining more internally generated operational and financial information as well as analyzing the myriad information because of increased computing power, increased data, and more userfriendly tools.

Prior to the advent of the internet, an organization worked mainly with its internal data. With the subsequent advances in internet use in the latter half of the 20th century and the beginning of the 21st century, external information became accessible that could be integrated with internal data. Companies moved from producing batch information to employees generating information (on both the corporate and personal level), to sensors producing data about all aspects of our lives. This last point can be frightening because appliances, sensors, and different apparatuses are generating more data in shorter periods of time than ever before. This has resulted in a flood of information, the concept of big data and predictive analytics.

Definition — What is big data?

Big Data is a set of high-volume, high-velocity, and high-variety information that demands cost-effective, innovative forms of information processing for enhanced insight and decision making.1

The end goal of big data should be to leverage the information resulting in increased value to the customer and an organization.

How big is “big”? Volume levels in big data

In addition to transactional data and user-created data, the advent of the internet opened the floodgates to new databases, new forms of data, and data that no longer needed to be created by human intervention.

DOMO.com created an analysis of the amount of data that is processed or created every minute over the internet.2,3,4

Consider the following by-the-minute volumes:

Netflix hours of video streamed — 694,444 (2019); 97,222 (2018)

Skype calls made — 231,840 (2019); 176,220 (2018)

Instagram photos uploaded — 55,140 (2019); 49,380 (2018); 46,740 (2017)

Amazon sales generated per minute — $533,713.85 (2019); $270,01 5.22 (2018)

Internet data used — 4,416,720 GB (2019); 3,183,420 GB (2018); 2,657,700 GB (2017)

YouTube videos watched — 4.5M (2019); 4.333M (2018)

Internet World Stats Live estimates that there were 4.574 billion users in 2019, increasing from 4.2 billion global users in 2018.

The amount of data continues to grow exponentially. There’s nothing on the horizon that suggests this increase of information will not continue. The challenge for the accountant is managing the expansion of information in terms of collecting, archiving, accessing, and interpreting. The growth in structured data, unstructured data, streaming data, and the like will only continue.

Knowledge check

How can big data best be described?

Large systems in multi-national companies.

Structured data, unstructured data, and streaming data.

Enterprise resource planning systems with all software applications in the organization.

Data processed with serial processing.

It is estimated that Instagram users share approximately how many photos every minute of the day?

Approximately 25,000.

Approximately 55,000.

Approximately 75,000.

Approximately 100,000.

Examples of volume

What type of data volumes does big data involve?

Table 1-1

Acronym

Description

Size

(B)

Byte

(KB)

Kilobyte

(MB)

Megabyte

(GB)

Gigabyte

(TB)

Terabyte

(PB)

Petabyte

(EB)

Exabyte

Megabytes, gigabytes, terabytes ... what are they?

How much data could be contained in the preceding measurements? We turned to WhatsAByte.com to find out.5

Byte: 100 bytes equates to an average sentence like this one.

Kilobyte: 100 kilobytes equals a page of words like the one you’re reading now.

Megabyte: 100 megabytes equals a couple of volumes of encyclopedias. 600 megabytes is about the amount of data that will fit on a CD-ROM disk.

Gigabyte: 100 gigabytes could contain an entire library floor of academic journals.

Terabyte: A terabyte could hold 1,000 copies of the Encyclopedia Britannica. Ten terabytes could hold the printed collection of the Library of Congress.

Petabyte: A petabyte could hold approximately 20 million four-door filing cabinets full of text. It could contain 500 billion pages of standard printed text.

Exabyte: It’s estimated that five exabytes would be equal to all of the words ever spoken by mankind.

Zettabyte: 1 ZB is equivalent to approximately 152 million years of high-definition video.6

Knowledge check

A petabyte could contain how many billion pages of standard text?

100.

500.

900.

750.

The accountant and big data

Although many organizations have sought to leverage big data applications and resources, they have not had the time or resources to pursue the dream fully. The American Productivity and Quality Center and Grant Thornton conducted a study in 2015 on big data and financial planning and analysis. The respondents indicated the following areas would have more attention if additional time was made available.

Simple aggregation of exposures and losses 60%

Basic cause-and-effect analysis 57%

Scenarios and “what-if” analyses to identify possible outcomes 36%

Predictive analysis techniques to project probable outcomes 24%

The Institute of Management Accountants, conducted a study on the Organizational Attitude toward Leading Edge Analytics and Big Data.7

The pressure is on for finance and accounting professionals to deliver more insights.8

Robert Hernandez discussed the seven data science skills that accountants will need in the future.

Advanced Excel — Excel will still be used in conjunction with other big data tools. However, they will need the command of higher skills, including the use of sophisticated data tables, statistical functions, report automation, and self-correcting models. The objective should be to learn the tools to allow access, manipulation, and reporting of large files of raw data.

Data mining/SQL programming. SQL is still a dominant tool to query transaction databases. Data science will revolve around data and analytic functions, primarily in SQL databases. The accountant should understand the basics to query large internal and external databases.

Advanced revenue analytics. An organization will always keep people around that can increase revenue. Using data to find more effective pricing, discounting, cross-selling opportunities, new forms of revenue will enhance the value of the accountants.

Mathematical optimization. Optimization opportunities are all around us. Analytic tools that help to improve efficiencies in costs and revenues are mandatory. The accountants are in a position to identify and prescribe actions to increase the profitability of the company.

Analytical segmentation. The traditional financial statement approach needs to be reduced to a granular level. Determining profitability by department, division, or company is still necessary, but the new approach will be to understand profitability at the customer level.

Visualization. Charts and graphs can immediately convey internal and external trends and areas that require action. Different charts may be easier for different functions. The accountant must know how to present data with meaning in graphs and be a better communicator.

Real-time models. The world has moved to 24/7 and immediacy of data. Businesses today are no different. Not only do businesses need immediate feedback on operations, but they also require predictions and projections to envision what will happen in the future. Streaming data will be available to help with current and future state operating results.

Adaptive insights provide a contrary opinion about the hiring requirements for accountants and the significance of Excel expertise. Consider the following chart that illustrates this and other traits desired by CFOs in their staff.

9

Will Excel go away? Possibly, but not soon. Power BI is a better visualization and analytics tool. However, let’s put forth a hypothetical scenario. Your company has been learning Power BI, you are by no terms an expert. You are at novice level. You have an 8am deadline, and you are having difficulties with getting Power BI to do what you need it to do. It is 11pm. You are tired. Do you continue to try to get Power BI to work for you, or do you try to use Excel?

One other key feature that promotes the longevity of Excel is the experience of the accountant. The transition from Lotus to Excel had some drawbacks. Some of the most successful changes in software were those that allowed the individual to operate in both new and old mode. The transition time may be longer, but the productivity and frustration level were better.

There is also one very important concept when it comes to hiring employees. Excel is a standard. When a job seeker moves from one company that had its financial analysis tools in a different business intelligence platform, he or she takes a step backward. A person might also limit his or her mobility because the financial planning and analysis restricts their expertise. The same problem confronted programmers when they specialized in a certain programing language.

Accounting’s big data problem10

According to CFO.com, unless accountants and finance executives work for companies in businesses that provide or deliver data products and services, they may not be participants in the big data trend because most of them have been trained almost exclusively on structured data (data that fits into tables, Excel spreadsheets, databases, and the like) rather than unstructured data.

Keep in mind that unstructured data represents the most significant segment of existing data and will probably yield the largest benefit.

One such example of the unstructured data comes from Trax — a Singapore-based firm that provides an image recognition app to gather data from photos taken of shelves at retail stores. The photos allow an organization to better manage inventories.

In a recent interview with PYMNTS, David Gottlieb, Trax’s general manager of global retail, discussed the company’s vision for digitized brick-and-mortar retailers, including how image recognition and Artificial Intelligence (AI) can be harnessed to streamline shelf management. Shelf management could not be done frequently before solutions like those offered by Trax and others in the space were available, Gottlieb explained, largely because it required a manual and labor-intensive process.

“At some point during the day, you’d have an associate walk through the store looking for holes and then maybe scanning them with a scanner,” he explained. This often ate up substantial chunks of time, particularly for retailers with large stores or a wide range of inventory. Trax’s system aims to cut down on the time required to perform shelf management tasks by using a camera for real-time shelf monitoring. It relies on AI-based image recognition to identify changes in stock, helping ensure that retailers obtain an accurate account of which products they have to sell or need to reorder.11

Those images can be used for more than just shelf availability information, however. The pictures are also stored to provide a large well of customer and product information, Gottlieb explained, which can then be used for analytical purposes

“It is not just the image recognition technology we are offering,” he said. “We are storing data on multiple levels — raw data generated from the images, the key performance indicator data, and then meta data, the master data.”

Another example of unstructured data can be found in corporations’ published text in the following sources:

10-Ks and 10-Qs Management’s Discussion and Analysis

Press releases

Interviews with corporate executives

Big Data terminology

As in any new field, big data has some terms that must be mastered. The following list is not meant to be all-inclusive, but it identifies many of the common terms related to big data, analytics, and business intelligence.

Artificial intelligence (AI). Refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind, such as learning and problem-solving.12

Business intelligence (BI). The integration of data, technology, analytics, and human knowledge to optimize business decisions and ultimately drive an enterprise’s success. BI programs usually combine an enterprise data warehouse and a BI platform or toolset to transform data into usable, actionable business information.13

Data analytics (DA). The science of examining raw data with the purpose of drawing conclusions from that information. DA is used in many industries to allow companies and organizations to make better business decisions, and in the sciences to verify or disprove existing models or theories.14

Cloud computing. A model for delivering information technology services in which resources are retrieved from the internet through web-based tools and applications rather than a direct connection to a server. Data and software packages are stored in servers. However, cloud computing allows access to information as long as an electronic device has access to the web. This type of system allows employees to work remotely.15

Dashboards. A BI software interface that provides preconfigured or customer-defined metrics, statistics, insights, and visualization into current data. It allows the end and power users of BI software to view instant results into the live performance state of business or DA.16

Data lake. A centralized repository that allows you to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data and run different types of analytics — from dashboards and visualizations to big data processing, real-time analytics, and machine learning to guide better decisions.17

Data mining. The practice of searching through large amounts of computerized data to find useful patterns or trends.18

Data scientist. An employee or BI consultant who excels at analyzing data, particularly large amounts of data, helping a business gain a competitive edge.19

Data visualization. The presentation of data in a pictorial or graphic format.

Deep learning (DL).