ACC-315 Accounting Information Systems

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CH05_Savage_AIS_PPT.pptx

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Accounting Information Systems

1st Edition

Savage ● Brannock ● Foksinska

Chapter 5

Data Storage & Analysis

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Chapter Preview

Types of data

How we store our data

Characteristics of data

How we use our data

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Learning Objectives

5.1 Differentiate between data elements and data types.

5.2 Explain how data is stored.

5.3 Summarize the five characteristics of big data.

5.4 Apply data analytics to accounting problems.

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Learning Objective 5.1

Differentiate between data elements and data types.

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What is Data?

Data consists of facts and statistics about a person or object that are collected for reference or analysis

It can include numbers, words, measurements, observations, or even just the description of the object it references

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Data Elements

There are six data elements that together comprise an IT system

Bit

Byte

Field

Record

File

Database

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Data Element Example

ILLUSTRATION 5.1 The hierarchy of data elements is demonstrated using the Julia’s Cookies employee file

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Hierarchy of Data Elements

Data Element

Databases:	are composed of files
Files:	are composed of records
Records:	are composed of fields
Fields:	are composed of bytes
Bytes (characters):	are composed of bits
Bits:	the smallest element in a computer system

TABLE 5.2 Hierarchy of Data Elements

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Data Types

Data is categorized as one of two types based on how that data is stored on a computer

Structured Data – organized and fits nicely into tables

Unstructured Data – doesn’t fit into a traditional table

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Structured Data

Defined data types like numerical, text, or date

Can easily be displayed as a table

Require less storage space and are easier to manage

Can easily be displayed as a table and are easier to manage

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Unstructured Data

Images, audio, video, and more

Require more storage space and are harder to manage

Cannot easily be displayed as a table and are harder to manage

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Structured vs. Unstructured Data

ILLUSTRATION 5.2 The key differences between structured and unstructured data relate to format and storage

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Web 2.0

Web 2.0 refers to user-generated content and user participation on the internet

The internet was once a “read-only” environment where users mainly consumed published content

Web 2.0 is rich in interactive applications and socialization – truly a “read and write” environment

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Data Growth in Exabytes

ILLUSTRATION 5.3 Over time, unstructured data has grown much more quickly than structured data

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Data That Changes

Another important characteristic of data is whether it is static or dynamic

Static data doesn’t change once it’s created

Dynamic data may change after it is recorded and must be updated

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Let’s Chat! (1 of 4)

Why should accountants be knowledgeable about the difference between structured and unstructured data? What opportunities and challenges do they present to accounting professionals?

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Solutions (Ch5-DQ1): Knowing how to use unstructured data will help accounting professionals add value for their employers and clients. By not using unstructured data and ignoring around 80% of the data out there, accountants will forgo opportunities to provide valuable insights and solve problems in innovative ways.

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Learning Objective 5.2

Explain how data is stored.

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Introduction to Databases

A database is a set of logically related files (tables) that contains an organized collection of data that is accessible for fast searching and retrieval

Relational databases organize structured data in interrelated tables which are connected by similarities between tables

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Relational Databases

ILLUSTRATION 5.4 Tables with commonalities are connected to one another in a relational database

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The Basics of Databases

Database Management System (DBMS)

Queries

Querying Languages

Schema

Database Scalability

Production Database

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Database Scalability

ILLUSTRATION 5.5 Vertical scaling increases the size of the machine, while horizontal scaling increases the number of machines

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Types of Data Storage

A data lake is a vast pool of data as it is designed to contain all a company’s data and acts as a central repository for data

A data warehouse is designed specifically for reporting and data analysis and contains relevant data that has already been transformed for reporting use

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Data Lake vs. Data Warehouse

	Data Lake	Data Warehouse
Type of Data:	Unstructured and structured data from across the company (raw data)	Historical data in a structured format designed for a relational database (processed data)
Purpose:	Cost-effective storage of big data	Aggregated big data for analytics and business decisions
Users:	Data scientists	Data analysts
Activities:	Storing big data Big data analytics (data science)	Supporting business analysis Read-only queries for aggregating or extracting data
Scope of Data:	All data in a company	Only data relevant to analysis

TABLE 5.3 The Differences Between a Data Lake and a Data Warehouse

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The Flow of Data

ILLUSTRATION 5.6 Data flows through an enterprise data lake to an enterprise data warehouse

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Let’s Chat! (2 of 4)

Why is it important for accounting professionals to understand the structure of a relational database?

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Solutions (Ch5-DQ4): Since most reports for accounting purposes are created from structured data that is stored in relational databases—including data warehouses and data marts—knowing how data is linked and where the data is stored gives an accounting professional comfort that they are using accurate information. Querying data using SQL is a highly sought-after skill for accounting professionals.

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Learning Objective 5.3

Summarize the five characteristics of big data.

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What Makes Data “Big Data”?

Big data refers to extremely large and complex data sets that can be analyzed using recent technological innovations to reveal patterns and associations

It is often so large, generated so fast, and so unstructured that it surpasses the limitations of traditional systems and databases

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The 5Vs of Big Data

ILLUSTRATION 5.7 Big data is characterized by five attributes call the 5Vs

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The Five Characteristics of Big Data

Volume: the quantity and scale of data generated every second

Velocity: the speed at which data is generated

Variety: the diversity of data created or collected

Veracity: the accuracy and truthfulness of the data

Value: arguably the most important of the 5Vs because data isn’t useful to a business unless it can be converted into valuable information

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Let’s Chat! (3 of 4)

Identify and define the 5Vs that characterize big data. Which one is most important for accounting professionals and why?

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Solutions (Ch5-DQ5): a. Volume is the quantity and scale of data generated every second. b. Velocity is the speed at which speed data changes and is processed. c. Variety is the diversity of data created or collected and is categorized into two types—structured and unstructured. d. Veracity is the extent to which data can be trusted for insights. e. Value is the usefulness, worth or degree of importance of the results of the analysis. Value is the most important of the 5Vs because data isn’t useful to a company unless it can be converted into valuable or useful information to aid decision-making. If the result of the analysis is not useful, why bother doing the analysis at all?

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Learning Objective 5.4

Apply data analytics to accounting problems.

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The Four Categories of Data Analysis

Descriptive analytics: What has happened?

Diagnostic analytics: Why did it happen?

Predictive analytics: What is likely to happen?

Prescriptive analytics: How should we act?

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Dashboards and Visualizations

Dashboards are interactive, real-time reports

Visualizations are graphical representations of information and data

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Dashboards Example (1 of 2)

ILLUSTRATION 5.8 Dashboards with visualizations are graphical representations of information and data

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Data Analytics in Accounting

Accounting professionals today must perform tasks requiring data analytics in all areas of accounting specialization, including:

Audit and compliance

Financial accounting

Managerial accounting

Tax accounting

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Dashboards Example (2 of 2)

ILLUSTRATION 5.9 Data analytics and technology innovations allow accounting professionals to focus on more interesting work

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Audit & Compliance (What has happened?)

Descriptive: What has happened?

Data Analytics	Explanation
Three-way match for purchasing	For the whole population of inventory purchases for the period, match the firm’s purchase order with the firm’s receiving documentation and the vendor’s invoice. Agree the quantity per the invoice to the quantity received and to the quantity ordered. Agree the price per the invoice to the price on the purchase order. Then examine discrepancies and determine internal control deficiencies.

TABLE 5.5 Auditing and Data Analytics

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Audit & Compliance (Why did it happen?)

Diagnostic: Why did it happen?

Data Analytics

Explanation

Three-way match for purchasing

Descriptive analysis results revealed that 0.9% of the transactions were not subject to the three-way match. Drilling down to the data and investigating revealed that only one vendor and one accounts payable supervisor were involved in these transactions. The supervisor manually approved the invoices for payment without matching purchase orders and receiving documentation. This led to the discovery of a purchasing fraud scheme. The brother of the supervisor received payment for nonexistent purchases.

TABLE 5.5 Auditing and Data Analytics

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Audit & Compliance (What is likely to happened?)

Predictive: What is likely to happen?

Data Analytics	Explanation
Allowance for doubtful accounts	Build a model to predict the amount of the allowance for doubtful accounts by using inputs such as payment history, debt types, and industry norms. Compare this prediction to the balance in the ledger.

TABLE 5.5 Auditing and Data Analytics

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Audit & Compliance (How should we act?)

Prescriptive: How should we act?

Data Analytics	Explanation
Intelligent audit planning	Run historical audit findings and observations data through an artificial intelligence algorithm to analyze risks and activities. Predict which audits should be performed next, based on past risk indicators.

TABLE 5.5 Auditing and Data Analytics

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Financial Accounting (What has happened?)

Descriptive: What has happened?

Data Analytics	Explanation
Perform financial statement analysis	Use ratio analysis, horizontal analysis, and vertical analysis to analyze a company’s financial statements and display the results on an interactive dashboard. Knowledge of these ratios is essential for accounting certifications like the CPA, CMA, and CFE. Ratios include profitability, liquidity, solvency, and activity ratios.

TABLE 5.6 Financial Accounting and Data Analytics

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Financial Accounting (Why did it happen?)

Diagnostic: Why did it happen?

Data Analytics

Explanation

Compare accounting ratios and vertical analyses to competitors and the industry using XBRL data

Benchmark a company’s ratios by comparing them to the firm’s own past performance, competitors’ ratios, and industry ratios. For example, if there is a return on equity (ROE) ratio of 15% this year and 12% for the prior year, this indicates that managers are employing the funds entrusted to them by the shareholders to generate improved returns. If the company’s main competitor has an ROE of 18%, the company is being outperformed. If the industry ratio is 13%, then the company outperformed the industry this year but not last year. You can also drill down into ROE on an interactive dashboard to decompose it into the numerous underlying ratios that help pinpoint areas for improvement. Decomposing ROE is like peeling back the layers of an onion to get to the underlying explanations for changes.

TABLE 5.6 Financial Accounting and Data Analytics

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Financial Accounting (What is likely to happened?)

Predictive: What is likely to happen?

Data Analytics	Explanation
Predict future financial performance	Produce forecasts of the income statement, statement of cash flows, and balance sheet for each of the next five years. Horizontal analysis over the past several years should identify trends. There are key drivers for this task—normally the sales forecast and profit margin. If a company is sensitive to the economic cycle, it is necessary to consider macroeconomic conditions in the forecasts.

TABLE 5.6 Financial Accounting and Data Analytics

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Financial Accounting (How should we act?)

Prescriptive: How should we act?

Data Analytics	Explanation
Classify leases as finance or operating	Use machine learning based on previous lease classifications to evaluate new leases and assign a classification to each for compliance with GAAP, continuously using historical data to further train the program.

TABLE 5.6 Financial Accounting and Data Analytics

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Managerial Accounting (What has happened?)

Descriptive: What has happened?

Data Analytics	Explanation
Develop key performance metrics	Display key performance metrics for a firm on a digital dashboard to monitor performance, including gross profit margin, operating profit margin, EBITDA (earnings before interest, taxes, depreciation, and amortization), customer retention rate, market share, market growth rate, time to market, employee churn rate, recycling rate, waste reduction rate, carbon footprint, and water footprint.

TABLE 5.7 Managerial Accounting and Data Analytics

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Managerial Accounting (Why did it happen?)

Diagnostic: Why did it happen?

Data Analytics	Explanation
Perform variance analysis by comparing actual performance to benchmark performance and identifying causes for significant variances	A firm’s days sales outstanding has significantly increased. This means that the firm is taking longer to collect accounts receivable. Use diagnostic analysis to provide insights into why this has happened.

TABLE 5.7 Managerial Accounting and Data Analytics

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Managerial Accounting (What is likely to happen?)

Predictive: What is likely to happen?

Data Analytics

Explanation

Reduce employee turnover costs

Identifying employees at risk of resigning and reducing the resignation rate through early intervention, could lead to significant savings. In performing the analysis, the input data could include employee reviews, pay data, peer reviews, time at the firm, time since last promotion, and LinkedIn data. Remember that big data may know when you’ll resign before you do! Assume that a firm has 2,000 employees, average salary is $50,000, cost to replace employees is an average of 120% of the employee’s annual salary, and voluntary resignations average 10% per year (or 200 employees). A quick calculation reveals that the annual cost of employee turnover averages $12 million!

TABLE 5.7 Managerial Accounting and Data Analytics

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Managerial Accounting (How should we act?)

Prescriptive: How should we act?

Data Analytics	Explanation
Determine the best crops to plant	A multinational agricultural corporation identifies the optimal mix of crops to plant in different geographic locations by utilizing artificial intelligence and inputs such as real-time weather patterns and climate change indicators.

TABLE 5.7 Managerial Accounting and Data Analytics

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Tax Accounting (What has happened?)

Descriptive: What has happened?

Data Analytics	Explanation
Develop key performance metrics on tax costs	Display key performance metrics on tax costs for a corporation on a digital dashboard over a five-year period, including income before tax, effective tax rate, taxes paid, deferred taxes, and impact of losses carried forward.

TABLE 5.8 Tax Accounting and Data Analysis

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Tax Accounting (Why did it happen?)

Diagnostic: Why did it happen?

Data Analytics	Explanation
Create a trend analysis for sales taxes paid in different states to identify and investigate anomalies	An online company sells goods in all 50 states. Sales tax penalties for underpayment can be severe.

TABLE 5.8 Tax Accounting and Data Analysis

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Tax Accounting (What is likely to happen?)

Predictive: What is likely to happen?

Data Analytics	Explanation
Advise a wealthy couple on the tax consequences of residing part time in certain Countries	Prince Harry and Meghan Markle consult a tax partner to provide scenarios of future tax liabilities in the U.S. and Canada before deciding where to live.

TABLE 5.8 Tax Accounting and Data Analysis

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Tax Accounting (How should we act?)

Prescriptive: How should we act?

Data Analytics	Explanation
Develop an online Q&A for tax clients	Accountants use artificial intelligence to answer online tax questions from clients.

TABLE 5.8 Tax Accounting and Data Analysis

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Let’s Chat! (4 of 4)

Describe the four primary types of data analytics and include the question that each type is best suited to answer. Provide an example of how accounting professionals could use each type of analysis.

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Solutions (Ch5-DQ7): Descriptive analytics tell us what has already happened by looking at historical data and condensing it into smaller, more meaningful bits of information. It is easy to access to this data and visualize in software tools like Tableau. It addresses questions like: “What happened?” “How many times did this happen?” “When did it happen?” Example: A report comparing the amount invoiced by vendors for last year and this year, by quarter.

Diagnostic analytics provide insight into why something happened. These procedures drill down into data on a granular level and often consist of looking at the data sets in a variety of ways to identify trends or investigate causes. Diagnostic analysis uses historical data. Accountants use it to troubleshoot issues and investigate them by drilling down to the root cause. It addresses questions like: “Why did this happen?” “Where did this come from?” “How can we avoid this problem in future?” Example: Analysis of the computer login data for employees to see who is logging into their computer outside of the expected work week hours.

Predictive analytics use statistical modeling and algorithms to predict what is likely to happen. These require assumptions and provide possible outcomes based on those assumptions. Predictive analytics cannot tell you what will happen in the future; it can only forecast what might happen using probabilities by logically predicting outcomes using statistical modeling and predictive algorithms. This analysis uses historical and live data. It addresses questions like: “What is likely to happen?” “Should we make this decision?” Example: Connect a dashboard to a live database to show risks in real-time to predict logins that may result in fraudulent activity or external access to the network.

Prescriptive analytics answer the question of what we should do. It is the most advanced and sophisticated form of analytics and requires cutting-edge technology and advanced programming to design. It is costly to implement. The analyst develops the decision-making protocol by using historical data to train the program on what to do in a real-time situation. It uses the three previous analytics types for insights and uses live, historical, and external data. It answers the question: “What should we do? Examples will vary by student: Create a program that uses advanced algorithms to test the potential outcomes of every new login into the system to determine whether there is a high risk of a malicious login. If it determines a high-risk login, the login is blocked, and the program sends a message to the IT team notifying them of the potential threat.

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ACC-315 Accounting Information Systems

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