P1 SEC F - Technology and Analytics

Topics covered in this section include information systems, data governance, technology-enabled finance transformation, data analytics, business intelligence, data mining, analytical tools, and visualization.

Primary role of AIS in the value chain

To provide reliable and timely information to decision makers both inside and outside of the organization—in the form of official financial statements or as performance reports for internal users.

Accounting Information System (AIS)

Adds value by providing necessary information that is used for analysis, evaluation, regulation, and strategic decision making.

Closest to zero

To identify the weakest correlation, we need to determine the coefficient of correlation that is:

Revenue to Cash Cycle

Refers to the process of taking orders, shipping products or delivering services, billing customers, and collecting cash from sales.

Relevant records—Revenue to Cash Cycle

Customer purchase orders, sales orders, picking tickets, shipping documents, invoices, and cash receipts.

Expenditure Cycle

The process of placing orders, receiving shipment of products or delivery of services, approving invoices, and making cash payments.

Relevant records—Expenditure Cycle

Purchase requisitions, purchase orders, receiving reports, and invoices.

Production Cycle

The process by which materials are converted into finished goods.

Relevant records—Production Cycle

Cost accounting reports, bills of materials, customer orders, production schedules, production orders, material requisitions, move tickets, operations reports, job-time tickets, and cost of goods manufactured reports.

Human Resources and Payroll Cycle

The process of recruiting, interviewing, and hiring personnel, paying employees for their work, promoting employees, and finalizing employees’ status from retirements, firings, or voluntary terminations.

Relevant records—Human Resources and Payroll Cycle

Master payroll files, time reports, hiring, promotion, transfer, and firing records, tax and insurance rate records, and individual employment records with data such as withholdings and deductions.

Financing Cycle

The process of obtaining funding, through debt or equity, to run an organizations’ activities and to purchase PPE, servicing the financing, and ultimate repayment of financial obligations.

Relevant records—Financing Cycle

Cash budgets, debt instrument records, equity holding records, and repayment schedules.

Property, Plant, and Equipment Cycle

The process of acquiring resources (e.g., land, buildings, and machinery) needed to enable an organizations’ business activities.

Relevant records—Property, Plant, and Equipment Cycle

Acquisition records, depreciation schedules, and disposal reports.

General Ledger and Reporting System

The process of recording, classifying, and categorizing an organization's economic transactions and producing summary financial reports.

Relevant records—General Ledger and Reporting System

General and subsidiary ledgers, financial statements, and managerial reports.

Challenges of having separate financial and nonfinancial systems.

Primary challenge: data maintenance—that is, making sure the data is accurately linked in both systems.

• When the two systems are separate, the data must be reconciled to make sure they are measuring the same thing.

• If the data the systems draw upon is not located in the same place or database, extensive controls must be created and maintained in order to avoid costly errors and inconsistencies.

Enterprise Resource Planning (ERP)

The integrated management of core business processes. Brings together business functions such as inventory management, operations, accounting, finance, human resources, and supply chain management.

Advantages of ERP

Include the availability of real-time data, wide distribution of information, single system learning, and lower operational costs.

Disadvantages of ERP

Include high initial monetary, implementation, and training costs.

How ERP helps overcome the challenges of separate financial & nonfinancial systems

ERP enables a single information system to provide both financial and nonfinancial information to users. This reduces the errors that can arise when different systems draw on different information sources.

Ex. An ERP can link the CRM system to AIS to reduce errors and increase information usefulness.

Customer relationship management (CRM)

A nonfinancial system that can draw data from the same underlying information system as an AIS system that processes financial data about sales activity.

Relational database

Formally described set of data tables that recognizes relationships among data items. Each row of table has a primary key, or a unique identifier, that can be used to link tables together.

Database Management System (DBMS)

The interface or program between a company's database and the application programs that access the database. It defines, reads, manipulates, updates, and deletes data in a database. It optimizes how data in databases are stored and retrieved, and facilitates an organization's administrative operations.

Data warehouse

A storage system used to aggregate data from multiple sources into a central integrated data repository. Used to facilitate analysis of business activities and operations.

Enterprise Performance Management (EPM)

AKA. corporate performance management (CPM) or business performance management (BPM).

A process that facilitates the linking of an organization's strategies to specific plans and actions. The overall process can be broken down into subprocesses such as planning, budgeting, and forecasting; performance reporting; and profitability and cost analysis.

How EPM can facilitate business planning and performance management

• Improves efficiencies in planning, budgeting, and reporting processes by relying on a centralized database and workflow.

• Reduces/eliminates the need for spreadsheet-based business activities by acting as a central repository for performance data.

• Provides a more holistic view of an organization's performance by linking its financial and operational data and metrics. This helps facilitate the analysis and reporting of the organization's activities.

Data Governance

Refers to the overall management of data within an organization. It comprises the procedures, policies, rules, and processes that oversee the following data attributes: managing the availability, usability, integrity, and security of data.

Availability

The ability to make data accessible when and where it is needed.

Usability

The delivery of data to end users in formats and structures that allow for its successful use.

Integrity

The accuracy and consistency of data.

Security

The protection of data from unauthorized access and possible corruption.

Data Governance frameworks

Help organizations design and manage the structure of data governance systems. 

• Committee of Sponsoring Organization's (COSO) Internal Control – Integrated Framework

• Information Systems Audit and Control Association's (ISACA) Control Objectives for Information and Related Technologies (COBIT).

COSO's 5 Components of Internal Control [CRCIM]

1. Control environment

2. Risk assessment

3. Control activities

4. Information and communication

5. Monitoring activities

These components should be considered for internal control over operations, reporting, and compliance activities, and can be designed from the entity level down to the level of individual business functions.

COBIT Framework [PADM]

Primarily focuses on internal controls related to information technology. The framework divides IT into 4 major parts:

1. Plan and Organize

2. Acquire and Implement

3. Deliver and Support

4. Monitor and Evaluate

Also provides best practices for IT management in the form of resources, technical guides, and training.

Stages of Data Life Cycle [CMSUAPAP]

AKA. Effective Data Management Process

1. Data Capture

2. Data Maintenance

3. Data Synthesis

4. Data Usage

5. Data Analytics

6. Data Publication

7. Data Archival

8. Data Purging

Data Capture

The recording or securing of data. Data can be captured by entering by hand, scanned by computers, or acquired by sensors.

Data Maintenance

The process of creating usable data, which may include cleansing, scrubbing, and processing through an extract – transform – load (ETL) methodology.

Data Synthesis

The use of statistical methods to obtain a better overall estimate or answer to the questions for which data are used. Sometimes called data modeling.

Data Usage

Action taken with data to support the mission of the business such as processing invoices, contacting customers, sending purchase orders to vendors, etc.

Data Analytics

The use of data analysis methodologies to answer questions and make decisions.

Data Publication

The act of sending data outside the organization. This typically means sending data to business partners such as sending a statement to a customer.

Data Archival

The process of removing data from active use to be stored for potential future use.

Data Purging

Deleting data that is no longer useful or needed

Data Preprocessing

One of the most important steps in extracting information from data before it gets used in analytics models. Necessary because big data (which usually comes from different sources) is typically not standardized or commonly formatted.

Data Preprocessing—4 Processes

1. Data consolidation

2. Data cleaning (cleansing)

3. Data transformation

4. Data reduction

Data Consolidation

The process of collecting and bringing together data from multiple sources. Consists of cycling through defined data sources, connecting to each one, reading data from the source, and storing newly collected data in a central location.

Only new data is imported

Data redundancy can be avoided by ensuring that ________.

Data cleaning (cleansing)

The process of ensuring data matches the requirements for analysis. Criteria include validity, accuracy, completeness, consistency, and uniformity. Typically the most time-consuming and effortful of the data preprocessing steps.

Data transformation

The process of applying algorithms to convert data from its raw form into an output form that meets analytical requirements.

Ex. Conversion of temperature data into a common scale or financial values into a common currency.

Data reduction

The process of aggregating or otherwise decreasing the volume of raw data so that it can be handled efficiently.

Ex. Reducing granular data into metrics that provide more actionable data for analytical models.

Documented record retention policy

An important control to ensure data is secure. Records must be kept and maintained for internal use as long as they are needed by users to research, analyze, and document past events and decisions. Records must be preserved to meet legal and regulatory requirements.

Cyberattack

An attempt by an individual or organization to gain access to the information system or computer of another individual or organization.

Ex. malware, phishing, and denial-of-service attacks.

Controls and tools to detect and thwart cyberattacks

1. Vulnerability testing

2. Biometrics

3. Firewalls

4. Access controls

Vulnerability testing

Actions taken to identify existing vulnerabilities. It does not attempt to assess if and how the vulnerability could be exploited.

Penetration testing

Undertaken to actively exploit potential weaknesses in a system and identify potential resulting damages. Opposite to Vulnerability testing.

Biometrics

The use of physical features and measurements for identify verification. Include using fingerprints, facial recognition, and even stride pattern to verify individuals. Help ensure that only authorized personnel are allowed to physically be in certain locations, to access and alter data, and/or perform specified business functions.

Firewalls

Security rules and monitors of incoming and outgoing traffic used in computer networks to prevent unauthorized users from gaining access. Can take the form of hardware, software, or a combination of both.

Access controls

Limits on who can access a place or a resource.

• Physical access controls restrict who can enter into geographic areas, buildings, and/or rooms.

• Logical access controls restrict which individuals can connect to computer networks, system files, and data.

• Access control can take the form of passwords, personal identification numbers (PINs), credentials, or other authorization and authentication forms.

Systems Development Life Cycle (SDLC)

A structured road map for designing and implementing a new information system. It follows the following five steps:

1. Systems analysis

2. Conceptual design

3. Physical design

4. Implementation and conversion

5. Operations and maintenance

Systems analysis

Identifying the needs of the organization and assembling the information regarding modifications to the current system and/or the purchase and development of a new system.

Conceptual design

Creating a plan for meeting the needs of the organization. Design alternatives are prepared and detailed specifications are created for the desired system.

Physical design

Creating detailed specifications for creating the system based on its conceptual design. The design would include specifications for computer code, inputs, outputs, data files and databases, processes and procedures, and proper controls.

Implementation and conversion

The installation of the new system, including hardware and software. The new system is tested and users are trained. New standards, procedures, and controls are instituted.

Operations and maintenance

The execution of the system, including checking performance, making adjustments as necessary, and maintaining the system. Improvements are made and fixes are put in place until it is determined that the cost of maintaining old systems exceeds its benefits, and the cycle starts again.

Role of Business Process Analysis in improving system performance

It represents a systematic method of examining a company's business process to determine how they can be improved in either effectiveness, efficiency, or both.

Common approaches: clear establishment of process objectives, diagramming or flowcharting current and optimal process flow, and the identification and elimination of nonvalue adding activities.

Robotic Process Automation (RPA)

The use of software to complete routine, repetitive tasks, typically in settings with high volumes of routinized actions. Rather than employing human labor to perform these functions, they can manipulate data, record transactions, process information, and perform many other business and IT processes.

Benefits of Robotic Process Automation (RPA)

• Provides greater consistency and speed in work performed.

• Allows computers to execute routine tasks rather than having an individual perform the work in front of a computer.

• Allows organizations to scale processes faster than by hiring and training workers to perform identical tasks.

Artificial Intelligence (AI)

Involves computers performing tasks requiring critical analysis and pattern recognition. Where technologies can improve efficiency and effectiveness of processing accounting data and information.

Benefits of AI in processing accounting data

• Process information more quickly and in larger quantities than the human mind can

• Does not suffer computational fatigue; improve efficiency and effectiveness of accounting processes

• Adaptive—allows computers to learn from prior information processing experiences and revise and update future processing.

Real-life examples of using AI in processing accounting data

• AI can be used to recognize speech or textual patterns, and to analyze various inputs and provide recommendations.

• AI can be used to classify or categorize transactions into appropriate accounts or to identify potential errors or irregularities in accounting data, which could be used to improve financial reporting or by auditors to detect misstatements and/or fraudulent activities.

• AI can also analyze cost data and create reports on cost behaviors and patterns.

Cloud computing

A shared resource setup using a network of remote servers that are connected by the Internet. The remote servers are used to store, manage, and process data. Can provide access to larger data storage, faster processing speeds, and numerous software applications.

How cloud computing can improve efficiency

It can help avoid data loss due to localized hardware failures and malfunctions because of networked backups and redundancies. The “cloud” or network of servers provides a safeguard by storing information on multiple servers at multiple geographic locations.

Software as a service (SaaS)

A software distribution model in which a third-party provider hosts applications and makes them available to customers over the Internet.

Advantages and Disadvantages of Software as a service (SaaS)

Advantages:

• Lower IT costs for customers in the form of reduced need for local installation and equipment.

• Reduced responsibility for maintenance and troubleshooting.

Disadvantages:

• Potential limitations on functionality and customization.

Blockchain

Refers to a distributed, digital ledger of economic transactions within a peer-to-peer network. T

Benefits of Blockchain

Transaction data is not stored in a single location, but across thousands of computers and servers simultaneously. This improves data validity because the database cannot be hacked or corrupted.

Potential applications of blockchain, distributed ledger, and smart contracts

• Blockchain allows for cryptocurrencies such as Bitcoin to function because it facilitates economic exchange based on a public, digital ledger for the recording and verification of transactions.

• Blockchain also facilitates smart contracts that can be completed, verified, and carried out without involving third parties because computerized protocols are used to execute and enforce contract terms.

Big Data

Refers to datasets that are extremely large and/or complex, usually requiring special software and computational power to be processed and analyzed.

4 Vs of Big Data

1. Volume: quantity or scale of the data.

2. Velocity: speed with which big data is generated and analyzed.

3. Variety: different types of data that may be involved (e.g., numerical, textual, images, audio, video, etc.).

4. Veracity: accuracy or quality of the data.

Structured data

Easily searchable because it has fixed fields and unique identifiers, such as data organized in a spreadsheet with column or row identifiers.

Semi-structured data

Lacks neat, organized fields but may still have organizing features such as tags or markers.

Ex. Extensible Markup Language (XML) and email.

Unstructured data

Unorganized and not easy to search or categorize.

Ex. X or Twitter and text messages, photos, and videos.

Progression of data, from data to information to knowledge to insight to action

1. When structures or organizations are used, data is transformed into information.

2. Information is different from data because information carries meaning and understanding. Based on information, knowledge can be defined and created.

3. Knowledge is what we know and how we understand that things are. Knowledge is transformed through critical analysis and logical thinking to understand situations and context in order to take action.

Opportunities of managing data analytics

• Processes data into information by organizing data and using analysis techniques to identify and understand relationships, patterns, trends, and causes.

• Help individuals develop and refine information into knowledge, which requires human understanding.

Challenges to data analytics

• The compilation of disparate data sources into a unified structure

• Costly procedures and processes for data validation and verification

• The need for specialized training and frequent updating of expertise

Why data and data science capability are strategic assets

• Data analytics can help identify new opportunities and evaluate the efficacy of organizational practices and philosophies.

• Data analytics can help increase value by improving the understanding of operations and providing information for the evaluation of performance and strategic options.

Business Intelligence (BI)

Refers to the applications, tools, and best practices that transform data into actionable information.

Data Mining

Refers to the use of statistical methods, computer learning, artificial intelligence, and large-scale computing power to analyze large amounts of data in order to extract useful information about relationships, trends, patterns, and anomalies.

Challenges of Data Mining

1. Data quality: errors or missing values can limit the ability to conduct rigorous data mining.

2. Multiple sources: combining data from multiple sources can make matching observations and data transformation difficult.

3. Data volume: analysis of large datasets can require a challenging level of computation power.

4. Output volume: data mining techniques can produce enormous amounts of output that can require significant time and effort to navigate.

Why data mining is an iterative process and both an art and a science

• Iterative: datasets often need to be simplified and statistical tools and queries need to be refined repeatedly to focus results and provide actionable findings.

• Science: statistical tools and analyses need to be used with precision in order to produce reliable insights.

• Art: patterns and trends can often be seen only by looking at the data in different ways, and by relying on both experience and creativity to transform data into knowledge and productive actions.

Query tools

Such as Structured Query Language (SQL) are used to manipulate and extract information from a database. They are the primary mechanisms by which we can communicate with a database.

How an analyst would mine large data sets to reveal patterns and provide insights

An analyst could use data mining techniques to identify sales trends by products, regions, customer segments, or other categories.

Data mining could also be used to better understand cost behavior and identify cost drivers. These insights could result from techniques such as data clustering, longitudinal analysis, and regression, among others.

Challenge of fitting an analytic model to the data

• Part of the challenge when analyzing data is developing expectations (or models) of how different variables are connected.

• Another key consideration is whether the data meet the assumptions underlying the statistical analyses being conducted.

• Incomplete or misspecified models can lead to inaccurate or erroneous conclusions or estimates of effect sizes.

4 Types of Data Analytics

1. Descriptive

2. Diagnostic

3. Predictive

4. Prescriptive

Descriptive analytics

Observational analysis designed to report the characteristics of historical data. It describes statistical properties such as the mean, median, range, or standard deviation.

Diagnostic analytics

Analysis designed to examine uncover and understand why certain outcomes take place. It focuses on correlations and the size and strength of statistical associations.

Predictive analytics

Analysis designed to build upon descriptive and diagnostic analytics to make predictions about future events. Predictive analysis considers risk assessments, usually as outcome likelihoods and uncertainties.

Prescriptive analytics

Analysis that draws upon the other forms of analytics to infer or recommend the best course of action. Can take the form of optimization or simulation analyses to identify and prescribe optimal actions.