LEARNING OBJECTIVES

After completing this chapter, you should be able to do the following:

•     Identify distinctive points of computing history and their impact on the evolution of Big Data today.

•     Identify Big Data sources.

•     Recall Big Data from the perspectives of a small business and the accountant.

INTRODUCTION

This chapter examines the history as well as the sources and characteristics of Big Data. To accountants, the concept of Big Data relates to the ability to access, manipulate, analyze, and report on data using tools such as electronic spreadsheets. Understanding the history of computing and the evolution from clay tablets to modern day computer systems is essential. To best employ Big Data, we must recognize the past, present, and future implications of Big Data for our organizations as well as our profession.

Big Data can be created in a multitude of ways and transformed into knowledge by many avenues, providing that the organization has the skills and resources to do so. It is also important to recognize that data can be identified by a variety of characteristics.

THE ACCOUNTANT’S PERSPECTIVE—BIG DATA = SPREADSHEETS

Prior to 1978, the accountant’s job was a factor of the underlying accounting systems. The accountant recorded, processed, and analyzed transactions and reported on the financial results. Also, the accountant created additional value by evaluating results based on ratio and variance analysis and industry comparisons. In 1978, the world’s first spreadsheet, VisiCalc, was created. As a result, accountants began to input transactional data into spreadsheets and create new analyses. Spreadsheets resulted in customized reports and an increase in the overall volume of data. Over the last three decades, spreadsheet usage has increased. Database applications and large enterprise systems have become more available, user-friendly, accessible, and powerful. The accountant who was data-challenged prior to 1978 is today awash with so much data that it has become overwhelming. Accountants drown in data, but they lack the knowledge that is hidden in the data stream.

BIG DATA FROM THE ACCOUNTANT’S PERSPECTIVE

Accountants tend to look at data from their traditional data perspectives of acquiring, gathering, categorizing, aggregating, analyzing, and reporting information. Larger systems facilitated integrated and more complex analysis. The availability of Big Data allows for increased complexity and an increased ability to perform deep data analysis which was not possible previously.

KNOWLEDGE CHECK

1.     What was the first spreadsheet developed in 1978?

a.     Lotus.

b.     Excel.

c.     VisiCalc.

d.     Multi-mate.

HISTORY

When the history of Big Data is viewed through the lens of accounting, it can be categorized by the interaction of seven different areas:

•     Bookkeeping

•     Accounting

•     Calculating machines

•     Computers

•     Internet

•     Cloud computing

•     Internet of things

Each of these seven areas has been built upon or interacted with the other categories to result in the current access and application of Big Data. Consider some of the significant occurrences within each of the categories as they paved the way for Big Data. The first three categories remind us from where we came. As you consider the fourth through fifth categories, jot down the impact that any of the items has made for you or your company.

Bookkeeping

Bookkeeping has been a part of human civilization from the very beginnings of recorded history. Chaldean-Babylonian civilization is attributed with having the first formal bookkeeping or recordkeeping activities. Archaeological evidence of the code of Hammurabi (leader of Babylonia from 2285–2242 B.C.) includes the following:

104. If a merchant gives an agent corn, wool, oil, or any other goods to transport, the agent shall give a receipt for the amount, and compensate the merchant therefor. Then he shall obtain a receipt [from] the merchant for the money that he gives the merchant.

105. If the agent is careless and does not take a receipt for the money which he gave the merchant, he cannot consider the unreceipted money as his own.

106. If the agent accepts money from the merchant but has a quarrel with the merchant (denying the receipt), then shall the merchant swear before God and witnesses that he has given this money to the agent, and the agent shall pay him three times the sum.¹

In the earliest civilizations, transactions were recorded on clay tablets. During Egyptian times, transactions were recorded on papyrus. Systems continued evolving through Greek, Roman, and Israeli civilizations. Eventually, to promote accountability of officials, public accounts were chiseled into stone. Records were used to facilitate transactions, tax assessment, and payment.

KNOWLEDGE CHECK

2.     Among the earliest historical evidence of the need for transaction recording was

a.     The Roman tax code.

b.     Code of Hammurabi.

c.     Greek merchant records.

d.     Chinese trade records.

Accounting

In 1494, Fra Luca Pacioli (who wrote and taught in the fields of mathematics, theology, architecture, games, military strategy, and commerce) published Summa de Arithmetica, Geometria, Proportioni et Proportionalita (The Collected Knowledge of Arithmetic, Geometry, Proportion and Proportionality). A section of the book contained a treatise on bookkeeping, a section that ensured Pacioli a place in history as "The Father of Accounting," although he did not invent the practice of accounting—but rather described the process of double-entry accounting, known as the method of Venice.²

His system included most of the accounting cycle as we know it today. He published information on the use of journals and ledgers and described the process of debits and credits, including the fact that debits should equal credits. "As Pacioli says, this is the most important thing to note in Venetian bookkeeping: ’All creditors must appear in the ledger at the right-hand side and all the debtors on the left. All entries made in the ledger have to be double entries—that is, if you make one creditor, you must make someone debtor."³

His ledger included assets, which included receivables, and inventories, liabilities, capital, income, and expense accounts. The Summa was eventually translated into German, Russian, Dutch, and English.

Pacioli’s system is the basis for the accounting systems still in use today. Fundamentally, not much has changed, even through the Industrial Revolution and the rise of corporations. However, a pivotal event in the American history of accounting came in 1913 when the Sixteenth Amendment was ratified. The amendment required a federal income tax to be paid by all individuals working in the United States.

Income tax and corporate tax were little understood and heavily resisted in their formative years. As a result, most corporations and individuals were simply not filing or were filing incorrectly.

A few years later in 1917, the Federal Reserve published Uniform Accounting, a document that attempted to set industry standards for how financials should be organized both for reporting tax and for financial statements.

EXAMINE BIG DATA THROUGH THE EYES OF A SMALL BUSINESS

Next, we will consider an example of a landscaping business. What are the ways that it could leverage sources of Big Data to produce additional insights into the operations, the financial operations, and the customer?

Let us imagine that you are the owner of a landscaping business with fewer than 50 employees. Without any great insight into accounting or management theories, you began the business with just some equipment and a truck. The sum total of your equipment was limited to the truck, trailer, and commercial lawn mower. You went out and obtained customers using a fixed fee approach based on your best guess of what the customer was willing to pay. You worked from sunup to sundown loading your lawn mower on your trailer, driving to your customers, unloading the lawn mower, performing the mowing services and then started again the next day. At the end of any given week, month, or landscaping season, you judged the success of your business based on the remaining cash in your account. The business was simple. You were the only employee. Your expenses were for gasoline, repairs and maintenance, minimal advertising, and minimal professional help. As your landscaping business grew, you hired employees, purchased more equipment and hoped that the additional investment in employees and equipment would result in a larger increase in cash profits.

As an owner, you eventually implemented financial and operational systems to make the business a little more sophisticated. Although still concerned with cash, you are now receiving regular financial statements from an accounting firm. You are operating as the typical small business with employees—struggling to find customers, operate the business, meet financial obligations, and pay yourself.

The following chart describes operating the small business in the normal course of events. How could the lawn maintenance business have capitalized on Big Data concepts to improve the business?

Small businesses may be limited in their ability to pursue Big Data opportunities. However, the expense is usually not prohibitive, and the information (when used appropriately) can make the company more efficient, productive and profitable.

Also, based on the preceding example, the owner of the landscape business could analyze many Big Data opportunities as well as customer behavior including payments, timeliness of payments, renewal dates, social media comments, and net promoter scores.

BIG DATA SOURCES

There are a variety of Big Data information sources that an organization can access. Consider table 2-3, which breaks down the sources of data among five different categories:

Note in the preceding tables that the sources of data are broken down into five major categories:

1.     Structured data that the company has

2.     Structured data that the company has but has not developed

3.     Unstructured data that the organization has

4.     Structured data that the organization does not have

5.     Unstructured data that the organization does not have

Accountants are very familiar with the first and fourth categories mentioned. These categories are typically represented by traditional accounting and financial applications. Also, these categories are used to perform ratio and interpretive analysis to provide insight or understanding to structured data within these systems. Some companies may have explored the use of category two items (unused structured data that they have) especially if they have developed areas of emphasis such as key performance indicators. The real challenge is for the organization to leverage categories three and five (unstructured data.)

In table 2-4, the tools required to access each of the categories are outlined as follows:

•     In category one, reporting and processing involve traditional accounting accumulation, reporting, forecasting, and analysis.

•     Category two requires the organization to identify what information is necessary to provide insight into the organization. Once identified, the company must catalog the information to allow access to it. The curator process prunes the necessary information and discards that which is not desired. Lastly, the information is related to other financial information to determine if it can be used to infer some impact on the financial performance of the organization.

•     Tools that can be used in the third category assess unstructured data that the organization has not considered before but currently has access to. Typically, the organization will run analysis to determine if the data are valuable for further analysis. If so, the organization follows the same pattern as category two.

•     In the fourth category, tools must identify structured data that is available outside of the organization but not collected. Once identified, it must be collected, transformed, or related to other existing data and analyzed for insights. An example of this would be obtaining industrial production statistics on a monthly basis and comparing that information with the monthly sales to determine if there is any correlation. The monthly industrial production statistics would also be made available for regular analysis of the organization activities (for example, sales).

•     The fifth category represents unstructured data that the organization does not have and that the organization does not know the value of. In this category, the organization must first acquire the unstructured data and, once acquired, evaluate with analytics to determine if there is any value in the data to predict the financial impact on the organization.

SOURCES OF BIG DATA

Sources of Big Data are everywhere. You are probably aware of a few of these sources, such as Facebook, LinkedIn, and other large social media sites that many people use daily. However, you might not realize that sites like Facebook and LinkedIn use every feature of the site, including your posts, messages, photos, and searches, to collect data. Other sources of Big Data include the following:

•     Google searches

•     Email databases

•     Retail customer relationship management (CRM)

•     Health records, including insurance, hospital, mental health, prisons

•     Forums

•     Social media

•     Twitter

•     RFID tags

•     GPS-enabled devices

•     Smart meters

•     YouTube

•     Government databases

•     Amazon.com

•     Cross-selling

Do you know what the largest source of Big Data is? According to an IBM survey, the major source of Big Data that is currently being tracked is transactions. The rest of the survey results are depicted in table 2-5.¹¹

Another major source of Big Data comes from the Information of Things that is collected from objects, animals, and people. The activities or tools that generate Big Data are depicted in table 2-6 using information gathered from a Vitria survey.¹²

CHARACTERISTICS OF BIG DATA

KNOWLEDGE CHECK

4.     What was the estimated number of wearable, wireless health monitors projected to be in 2014?

a.     240 million.

b.     360 million.

c.     420 million.

d.     500 million.

EXAMPLE: RETAIL SURVEY

Let’s assume that we run a retail store with multiple locations. The store sells a variety of pet products including supplies, food, and toys. Consider the following examples of data the store may wish to obtain or capture from the customer or the vendor:

1.     Customer: The organization’s customer interacts in a variety of ways. An organization interested in Big Data will collect data from the customer through a variety of different methods and stages.

a.     Internet access: Assume that the customer is visiting the store via an Internet portal. The store would try to identify the customer through any of the following:

i.     A login identity

ii.     A home store

b.     Key product and or sales data

i.     What are people searching for?

(1)     Specific products, prices or price changes

(2)     Location of stores as well as inventory levels

(3)     What quantities do customers wish to buy?

(4)     Do they wish to pick up the item at the store?

(5)     What accessories or related items are being considered as well?

c.     Targeted email campaigns: Based on preferences shared by the customer, additional contacts can be made by email or text to induce a customer to make a purchase.

2.     Vendor: The organization’s vendor should also want significant access to data residing in the system.

a.     Performance: How fast is the product moving through inventory? Were the purchase requirements fulfilled correctly?

b.     Stock levels: What are the inventory levels? Also, and based on the lead times, when will it be necessary to replenish inventories level? If there are significant returns, what was the reason the products were returned?

c.     Pricing

i.     Comparisons to competitor pricing.

ii.     Is there any change in the mentor and movement based on the change in the retail price?

3.     Demographic

a.     Where did the customer come from—or where is their home location?

b.     Would the customer be willing to complete a survey?

c.     Is the customer a member of a customer loyalty group?

4.     Traffic count

a.     How many individuals entered through the front door?

b.     At what time did they enter through the front door?

5.     Open hours

a.     Which hours of business accounted for the greatest sales?

b.     Which hours of business had the highest returns?

c.     Which hours of business had the most ancillary services sold such, as a small food shop (in-house fast food station?)

6.     Credit card relationships

a.     How did people pay for the transaction?

i.     Cash

ii.     Check

iii.     Credit card

(1)     Affiliated

(2)     Type of card

(3)     Was the card rejected?

7.     Benchmarking by store, department. Each of the sales transactions can be summarized by department or section within the store.

a.     Square footage analysis

b.     Sales relation to specific departments’ weekly marketing brochures, emails, and the like

8.     Social media information

a.     Did the user post a comment regarding any of the products or services?

b.     Did the user attempt to access online coupons for the product?

c.     Did the user review a secondary store if their home store was out of the inventory?

Practice Questions

1. Discuss the historical evolution of Big Data based on the interrelationships of recordkeeping, accounting, calculating machines, and Big Data?

2. Describe the sources of Big Data.

3. What are the four Vs?

4. List any three of the Big Data sources or uses of structured or unstructured data for a small retail business.