- Information systems
a. Accounting information systems
b. Enterprise resource planning systems
c. Enterprise performance management systems - Data governance
a. Data policies and procedures
b. Life cycle of data
c. Data management
d. Controls against security breaches - Technology-enabled finance transformation
a. System development life cycle
b. Process automation
c. Innovative applications - Data analytics
a. Business intelligence
b. Data mining
c. Types of data analytics
d. Data visualization
Part 1 – Section F.1. Information systems
The candidate should be able to:
- a. identify the role of the accounting information system (AIS) in the value chain
- b. demonstrate an understanding of the accounting information system cycles, including revenue to cash; expenditures; production; human resources and payroll; financing; and property, plant, and equipment, as well as the general ledger and reporting system
- c. identify and explain the challenges of having separate financial and nonfinancial systems
- d. define ERP and identify and explain the advantages and disadvantages of ERP
- e. explain how ERP helps overcome the challenges of separate financial and nonfinancial systems, integrating all aspects of an organization’s activities
- f. define relational database and demonstrate an understanding of a database management system
- g. define data warehouse and data mart
- h. define enterprise performance management (EPM) (also known as corporate performance management (CPM) or business performance management (BPM))
- i. discuss how EPM can facilitate business planning and performance management
Part 1 – Section F.2. Data governance
The candidate should be able to:
- a. define data governance and data management
- b. demonstrate a general understanding of data governance frameworks, including COSO’s Internal Control—Integrated framework
- c. identify the stages of the data life cycle, i.e., data capture, data maintenance, data synthesis, data usage, data analytics, data publication, data archival, and data purging
- d. demonstrate an understanding of data preprocessing and the steps to convert data for further analysis, including data consolidation, data cleaning (cleansing), data transformation, and data reduction
- e. discuss the importance of having a documented record retention (or records management) policy
- f. identify and explain controls and tools to detect and thwart cyberattacks, such as penetration and vulnerability testing, biometrics, advanced firewalls, and access controls
Part 1 – Section F.3. Technology-enabled finance transformation
The candidate should be able to:
- a. define the system development life cycle, including systems analysis, conceptual design, physical design, implementation and conversion, and operations and maintenance
- b. explain the role of business process analysis in improving system performance
- c. define robotic process automation (RPA) and its benefits
- d. evaluate where technologies can improve efficiency and effectiveness of processing accounting data and information (e.g., artificial intelligence (AI))
- e. define cloud computing and describe how it can improve efficiency
- f. define software-as-a-service (SaaS) and explain its advantages and disadvantages
- g. recognize potential applications of blockchain, distributed ledger, and smart contracts
Part 1 – Section F.4. Data analytics
The candidate should be able to:
Business intelligence
- a. define Big Data and explain the volume, velocity, variety, and veracity of Big Data; and describe the opportunities and challenges of leveraging insight from this data
- b. explain how structured, semi-structured, and unstructured data is used by a business enterprise
- c. describe the progression of data, from data to information to knowledge to insight to action
- d. describe the opportunities and challenges of managing data analytics
- e. explain why data and data science capability are strategic assets
- f. define business intelligence (BI) (i.e., the collection of applications, tools, and best practices that transform data into actionable information in order to make better decisions and optimize performance)
Data mining
- g. define data mining
- h. describe the challenges of data mining
- i. explain why data mining is an iterative process and both an art and a science
- j. explain the purpose of Structured Query Language (SQL)
- k. describe how an analyst would mine large data sets to reveal patterns and provide insights
Type of data analytics
- l. explain the challenge of fitting an analytics model to the data
- m. define the different types of data analytics, including descriptive, diagnostic, predictive, and prescriptive
- n. define clustering and classification, and determine when each of these analytic techniques would be the appropriate tool to use
- o. demonstrate an understanding of multiple regression and logistic regression and recognize when these techniques are appropriate
- p. calculate the result of multiple regression equations as applied to a specific situation
- q. demonstrate an understanding of the coefficient of determination (R squared) and the correlation coefficient (R)
- r. demonstrate an understanding of time series analyses, including trend, cyclical, seasonal, and irregular patterns
- s. identify and explain the benefits and limitations of regression analysis and time series analysis
- t. define standard error of the estimate, goodness of fit, and confidence interval
- u. explain how to use predictive analytics techniques to draw insights and make recommendations
- v. describe exploratory data analysis and how it is used to reveal patterns and discover insights
- w. define sensitivity analysis and identify when it would be the appropriate tool to use
- x. demonstrate an understanding of the uses of simulation models, including the Monte Carlo technique
- y. identify the benefits and limitations of sensitivity analysis and simulation models
- z. demonstrate an understanding of what-if (or goal-seeking) analysis
- aa. identify and explain the limitations of data analytics
Visualization
- bb. utilize table and graph design best practices to avoid distortion in the communication of complex information
- cc. evaluate data visualization options and select the best presentation approach (e.g., histograms, box plots, scatterplots, dot plots, tables, dashboards, bar charts, pie charts, line charts, bubble charts) for a given scenario
- dd. understand the benefits and limitations of visualization techniques
- ee. communicate results, conclusions, and recommendations in an impactful manner using effective visualization techniques