Starting Ch2 review Ch1 HW (AIS)

Value Chain Activities

  • Definition of Value Chain Activities

    • Activities that contribute to a company’s ability to provide value to customers.

    • Includes functions like operations, logistics, marketing, human resources, technology, and services.

  • Examples of Value Chain Activities Related to Business Functions

    • Call Center

    • Related Value Chain Activity: Service

    • Call centers address customer service inquiries, responding to customer needs directly.

    • Supply Schedules

    • Related Value Chain Activity: Procurement and Inbound Logistics

    • Procurement involves acquiring goods and services, while inbound logistics pertains to receiving and storing the supplies.

    • Order Taking

    • Related Value Chain Activity: Marketing and Sales

    • Order taking is directly linked to generating sales and marketing efforts.

    • Accounting Department

    • Related Value Chain Activity: Infrastructure

    • The accounting department supports various activities through financial oversight.

    • Staff Training

    • Related Value Chain Activity: Human Resource

    • HR is responsible for employee training and development.

    • Research and Development

    • Related Value Chain Activity: Technology

    • R&D often integrates technology to innovate products and services.

    • Verifying Quality of Raw Materials

    • Related Value Chain Activities: Inbound Logistics and Procurement

    • Ensuring quality is vital for the procurement phase, where raw materials are sourced for production.

    • Distribution Center

    • Related Value Chain Activity: Outbound Logistics

    • It involves the distribution and delivery of goods to customers.

    • Manufacturing

    • Related Value Chain Activity: Operations

    • Directly refers to the processes involved in producing goods.

  • Understanding Value Chain Activities

    • Familiarize with each activity to enhance operational efficiency and understand the organization's workflow better.

Firm Information Systems and Their Applications

  • Purposes of Information Systems

    • Facilitate efficient communication, improve decision-making, and assist in value generation through data management.

  • Examples of Firm Information Systems:

    • Customer Relationship Management (CRM)

    • Financial Reporting Systems

    • Human Resource Management Systems

    • Supply Chain Management Systems

  • Addressing Information Needs:

    • Fixed Assets Value Inquiry

    • Answering System: Financial Reporting System

    • Provides information on fixed assets listed on the balance sheet.

    • Debt Inquiry (Airbus to General Electric)

    • Potentially Related Systems: Supply Chain Management and Customer Relations

    • Customer relations deal with amounts owed to businesses.

    • Arrival of Shampoo Products

    • Answering System: Supply Chain Management

    • Systems track products and their logistics to maintain inventory flow.

    • Poor Credit Customers

    • Answering System: Customer Relations

    • Identifies customers based on credit history and payments.

    • Salary Inquiry for Web Designer at Netflix

    • Answering System: Human Resource Management

    • Specific salary information is recorded in HR systems.

The Importance of Data Analytics in Accounting

  • Data Analytics:

    • Defined as the science of examining raw data, removing excess noise, and organizing data to inform decision-making.

    • Involves technologies, systems, practices, and methodologies used to analyze diverse business data to aide in decision-making.

  • Impact of Data Analytics:

    • Assists in identifying patterns, forecasting future behaviors, and enhancing business process efficiency.

    • Enhances auditing processes by allowing comprehensive data testing over random sampling.

    • Helps companies track risks and opportunities better, increasing productivity and enabling growth.

The AMPS Model of Data Analytics

  • AMPS Model:

    • Stands for Ask, Master, Perform, Share.

    • Stages:

    1. Ask the Question: Formulate appropriate questions that guide inquiry and inform analysis.

    2. Master the Data: Organize and clean data to ensure reliability and relevance.

    3. Perform the Analysis: Analyze data to derive insights.

    4. Share the Story: Communicate findings to stakeholders effectively.

  • Data Quality Considerations:

    • Volume: The size of datasets being analyzed.

    • Velocity: The speed data is generated and processed.

    • Variety: The different forms of data (structured and unstructured) collected.

    • Veracity: The accuracy and trustworthiness of data.

Data Reliability and While Cleaning

  • Data Integrity:

    • Ensures the trustworthiness and reliability of data used in analytics.

    • Elements of Clean Data:

    • No missing values: A complete dataset.

    • No duplicates: Each entry should be unique.

    • No outliers: Defined as data that significantly deviates from other observations in the dataset.

    • Consistency: Formatting should be standardized across the dataset to allow for accurate analysis.

  • Data Ethics:

    • Emphasizes the moral considerations tied to the acquisition and protection of data.

    • Ensures data is collected in a responsible way and safeguarded appropriately against unauthorized access.

Conclusion

  • Significance of Asking the Right Questions:

    • Formulating solid questions is essential for effective and actionable results in data analysis.

    • Understanding the relationship between data collected and business strategies is crucial for accurate decision-making processes.

  • Emphasis on continuous learning about technologies, trends, and best practices in data analytics for improved business efficiencies and compliance with regulations.