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DCA801-1
1 Week Training Program
Data Analytics & Artificial Intelligence AI for Transforming the Accounting Unit
Building Skills in
Accounting Decisions using Data Analytics, Business Intelligence & Artificial Intelligence
Understanding Domain Process Knowledge & Data Analysis, Business Intelligence & Artificial Intelligence Opportunities
Proactive Facts Based Accounting Management Decisions
Facts Based Understanding of Your Accounting Processes
Improving Customer Experience & Satisfaction
Enable Timely & Proactive Accounting Management Decisions
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Program Objectives
Provide Skills for:
Provide Skills for:
Leverage Data Analytics and AI for Better Decision-Making in Accounting: Equip participants with the knowledge to utilize data analytics, business intelligence, and AI to make better, faster, and more accurate accounting decisions.
Domain Process Knowledge and Opportunity Identification: Help participants understand accounting domain processes and identify opportunities to apply data analytics, business intelligence, and AI for efficiency and innovation.
Proactive, Fact-Based Accounting Decisions: Enable participants to adopt a proactive approach to accounting management by leveraging real-time and data-driven insights.
Improve Customer Experience and Satisfaction: Demonstrate how data analytics can be applied to enhance customer experience, exceed customer expectations, and improve satisfaction levels.
Enable Timely and Real-Time Decision-Making: Train participants to utilize tools and methods that facilitate timely, proactive, and real-time decision-making in accounting processes.
Automated and AI-Powered Decision Support: Introduce participants to the potential for automated decision-making powered by AI, streamlining repetitive accounting tasks and improving operational efficiency.
Leverage Data Analytics and AI for Better Decision-Making in Accounting: Equip participants with the knowledge to utilize data analytics, business intelligence, and AI to make better, faster, and more accurate accounting decisions.
Domain Process Knowledge and Opportunity Identification: Help participants understand accounting domain processes and identify opportunities to apply data analytics, business intelligence, and AI for efficiency and innovation.
Proactive, Fact-Based Accounting Decisions: Enable participants to adopt a proactive approach to accounting management by leveraging real-time and data-driven insights.
Improve Customer Experience and Satisfaction: Demonstrate how data analytics can be applied to enhance customer experience, exceed customer expectations, and improve satisfaction levels.
Enable Timely and Real-Time Decision-Making: Train participants to utilize tools and methods that facilitate timely, proactive, and real-time decision-making in accounting processes.
Automated and AI-Powered Decision Support: Introduce participants to the potential for automated decision-making powered by AI, streamlining repetitive accounting tasks and improving operational efficiency.
Who should Attend?
This program is designed for Accounting Management Professionals. Accounting and Other Professionals interested in Understanding how their Accounting Unit can use Big Data and Data Analytics, Specify the Requirements, Providing Informed Guidance to Data Analytic System Developers (Remember you know your domain knowledge more than anyone else outside your Unit/Department/Section), Participate in Your Data Analytic System Design Team Processes, Managing Data Analytic Solution Implementation Contractors, Apply the Data Analytics to Accounting Decision Making, Help Build your Accounting Decision Models, Help Collect and Suggest Accounting Data and Data Types and generally become a productive participant in Data Analysis and Improvement Teams.
This program is also intended for Data Scientists and Management Analysts with responsibility in this field. These Data Scientists & Analysts will gain Valuable Domain Process and Other Knowledge which will help them better appreciate stakeholder needs and implement an Effective Data Analytic/BI/AI solution.
This program can be used to train Accounting Implementation & Improvement Teams - when a new Data Analytics project or initiative is being started. Teams can also attend when the team is required to become familiar with Facts Based Emerging Opportunities for Improvement, Product or Service Development so they can contribute and support such solutions.
This program is designed for Accounting Management Professionals. Accounting and Other Professionals interested in Understanding how their Accounting Unit can use Big Data and Data Analytics, Specify the Requirements, Providing Informed Guidance to Data Analytic System Developers (Remember you know your domain knowledge more than anyone else outside your Unit/Department/Section), Participate in Your Data Analytic System Design Team Processes, Managing Data Analytic Solution Implementation Contractors, Apply the Data Analytics to Accounting Decision Making, Help Build your Accounting Decision Models, Help Collect and Suggest Accounting Data and Data Types and generally become a productive participant in Data Analysis and Improvement Teams.
This program is also intended for Data Scientists and Management Analysts with responsibility in this field. These Data Scientists & Analysts will gain Valuable Domain Process and Other Knowledge which will help them better appreciate stakeholder needs and implement an Effective Data Analytic/BI/AI solution.
This program can be used to train Accounting Implementation & Improvement Teams - when a new Data Analytics project or initiative is being started. Teams can also attend when the team is required to become familiar with Facts Based Emerging Opportunities for Improvement, Product or Service Development so they can contribute and support such solutions.
Program Content & Modules
Data Analytics & Artificial Intelligence AI for Transforming the Accounting Unit
Module 1: Introduction to Data Analytics & AI in Accounting
The role of data analytics and AI in modern accounting.
Key concepts: Big data, business intelligence, and machine learning.
Benefits of integrating analytics into accounting processes: efficiency, accuracy, and speed.
Module 2: Understanding Domain Processes and Data Opportunities
Overview of accounting processes and workflows.
Identifying data sources within accounting: transactional, operational, and communications data.
Recognizing opportunities for process improvements and cost reductions using analytics.
Real-world examples of successful analytics adoption in accounting.
Module 3: Foundations of Data-Driven Decision Making
Transitioning from intuition-based to fact-based accounting decisions.
Framework for fact-based management: data collection, analysis, and interpretation.
Key performance indicators (KPIs) and metrics for accounting units.
Tools for real-time decision-making in accounting.
Module 4: Internal and External Data Utilization
Internal Data:
Operational data mining (e.g., invoices, expense records, payroll data).
Communication data mining for trend analysis (emails, logs, memos).
External Data:
Market and industry trend data for benchmarking.
Customer behavior and satisfaction data.
Competitive intelligence and third-party data sources.
Techniques for combining internal and external data for actionable insights.
Module 5: AI-Powered Tools and Techniques for Accounting
Overview of AI applications in accounting:
Automating repetitive tasks (e.g., data entry, invoice processing).
Fraud detection using AI algorithms.
Predictive analytics for financial forecasting.
Introduction to commonly used tools and platforms (e.g., Power BI, Tableau, Excel, Google Data Studio).
Using AI for proactive decision-making and risk management.
Module 6: Practical Applications of Data Analytics in Accounting
Use Case 1: Enhancing financial reporting with real-time dashboards.
Use Case 2: Fraud detection and anomaly detection using AI.
Use Case 3: Predicting cash flow and budget planning with machine learning.
Use Case 4: Customer experience improvement through personalized financial services.
Use Case 5: Cost reduction strategies through operational data analysis.
Module 7: Globally Benchmarked Data Analytics Methods
Overview of globally recognized analytics practices.
Implementing Six Sigma, Lean Accounting, and predictive models.
Reducing uncertainty and risk through statistical models.
Best practices for data governance, compliance, and ethical considerations.
Module 8: Early Identification of Emerging Opportunities
Methods to mine operational data for product and service improvements.
Identifying new revenue streams through market and customer data analysis.
Real-time alerts for emerging opportunities: case studies and tools.
How to gain first-mover advantages with timely decisions.
Module 9: Tools, Software, and Resources for Analytics Professionals
Overview of statistical tools and methods (e.g., regression, clustering, and classification).
No-code and low-code platforms for analytics (e.g., Power BI, Alteryx).
Demonstrations of key software and integrated development environments (IDEs).
Leveraging cloud-based data analytics solutions (AWS, Azure, Google Cloud).
Module 10: Future Trends in Data Analytics & AI for Accounting
Predictive and prescriptive analytics in financial decision-making.
AI-driven conversational agents for customer interactions in accounting.
Blockchain and its implications for secure data handling in finance.
The convergence of IoT, big data, and AI in transforming accounting.
Program Recommendations & Action Plan
Module 1: Introduction to Data Analytics & AI in Accounting
The role of data analytics and AI in modern accounting.
Key concepts: Big data, business intelligence, and machine learning.
Benefits of integrating analytics into accounting processes: efficiency, accuracy, and speed.
Module 2: Understanding Domain Processes and Data Opportunities
Overview of accounting processes and workflows.
Identifying data sources within accounting: transactional, operational, and communications data.
Recognizing opportunities for process improvements and cost reductions using analytics.
Real-world examples of successful analytics adoption in accounting.
Module 3: Foundations of Data-Driven Decision Making
Transitioning from intuition-based to fact-based accounting decisions.
Framework for fact-based management: data collection, analysis, and interpretation.
Key performance indicators (KPIs) and metrics for accounting units.
Tools for real-time decision-making in accounting.
Module 4: Internal and External Data Utilization
Internal Data:
Operational data mining (e.g., invoices, expense records, payroll data).
Communication data mining for trend analysis (emails, logs, memos).
External Data:
Market and industry trend data for benchmarking.
Customer behavior and satisfaction data.
Competitive intelligence and third-party data sources.
Techniques for combining internal and external data for actionable insights.
Module 5: AI-Powered Tools and Techniques for Accounting
Overview of AI applications in accounting:
Automating repetitive tasks (e.g., data entry, invoice processing).
Fraud detection using AI algorithms.
Predictive analytics for financial forecasting.
Introduction to commonly used tools and platforms (e.g., Power BI, Tableau, Excel, Google Data Studio).
Using AI for proactive decision-making and risk management.
Module 6: Practical Applications of Data Analytics in Accounting
Use Case 1: Enhancing financial reporting with real-time dashboards.
Use Case 2: Fraud detection and anomaly detection using AI.
Use Case 3: Predicting cash flow and budget planning with machine learning.
Use Case 4: Customer experience improvement through personalized financial services.
Use Case 5: Cost reduction strategies through operational data analysis.
Module 7: Globally Benchmarked Data Analytics Methods
Overview of globally recognized analytics practices.
Implementing Six Sigma, Lean Accounting, and predictive models.
Reducing uncertainty and risk through statistical models.
Best practices for data governance, compliance, and ethical considerations.
Module 8: Early Identification of Emerging Opportunities
Methods to mine operational data for product and service improvements.
Identifying new revenue streams through market and customer data analysis.
Real-time alerts for emerging opportunities: case studies and tools.
How to gain first-mover advantages with timely decisions.
Module 9: Tools, Software, and Resources for Analytics Professionals
Overview of statistical tools and methods (e.g., regression, clustering, and classification).
No-code and low-code platforms for analytics (e.g., Power BI, Alteryx).
Demonstrations of key software and integrated development environments (IDEs).
Leveraging cloud-based data analytics solutions (AWS, Azure, Google Cloud).
Module 10: Future Trends in Data Analytics & AI for Accounting
Predictive and prescriptive analytics in financial decision-making.
AI-driven conversational agents for customer interactions in accounting.
Blockchain and its implications for secure data handling in finance.
The convergence of IoT, big data, and AI in transforming accounting.
Program Recommendations & Action Plan
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