Data Analytics In Accounting PDF

8/5/24 DATA ANALYTICS IN ACCOUNTING Dr Zubir Azhar CamEd 1 Big Data 2 1  8/5/24 The Four V’s of Big Data u Big data is the term companies use to describe the...

8/5/24 DATA ANALYTICS IN ACCOUNTING Dr Zubir Azhar CamEd 1 Big Data 2 1  8/5/24 The Four V’s of Big Data u Big data is the term companies use to describe the massive amounts of data they now capture, store, and analyze. uData volume refers to the amount of data created and stored by an organization. uData velocity refers to the pace at which data is created and stored. uData variety refers to the different forms data can take. uData veracity refers to the quality or trustworthiness of data. 3 The Four V’s of Big Data 4 2  8/5/24 An Analytics Mindset u A mindset is a mental attitude, a way of thinking, or a frame of mind. uAn analytics mindset is a way of thinking that centers on the correct use of data and analysis for decision making. u According to EY, an analytics mindset is the ability to uAsk the right questions. uExtract, transform, and load relevant data. uApply appropriate data analytic techniques. uInterpret and share the results with stakeholders. 5 Ask the Right Questions u A good data analytic question is uSpecific: needs to be direct and focused to produce a meaningful answer. uMeasurable: must be amenable to data analysis and thus the inputs to answering the question must be measurable with data. uAchievable: should be able to be answered and the answer should cause a decision maker to take an action. uRelevant: should relate to the objectives of the organization or the situation under consideration. uTimely: must have a defined time horizon for answering. 6 3  8/5/24 Extract, Transform, and Load Relevant Data u The process of extracting, transforming, and loading data is often abbreviated as the ETL process. uThe ETL process is often the most time-consuming part of the analytics mindset process. uRepetitive ETL processes can be fully automated so the extracting, transforming, and loading data is done entirely by a computer program in what appears to be a single, unified step. 7 Extracting Data u There are 3 steps in the data extraction process 1. Understand data needs and the data availability. 2. Perform the data extraction. 3. Verify the data extraction quality and document what you have done. 8 4  8/5/24 Enterprise Data Warehouse Components 9 Transforming Data u There are 4 steps in the data transformation process 1. Understand the data and the desired outcome. 2. Standardize, structure, and clean the data. 3. Validate data quality and verify data meet data requirements. 4. Document the transformation process. 10 5  8/5/24 Loading Data u There are a few important considerations when loading data. 1. The transformed data must be stored in a format and structure acceptable to the receiving software. 2. Programs may treat some data formats differently than expected. It is important to understand how the new program will interpret data formats. u Once the data is successfully loaded into the new program, it is important to update or create a new data dictionary. 11 Apply Appropriate Data Analytic Techniques u There are 4 categories of data analytics u Descriptive analytics are information that results from the examination of data to understand the past answers to the question “what happened?” u Diagnostic analytics build on descriptive analytics and try to answer the question “why did this happen?” u Predictive analytics are information that results from analyses that focus on predicting the future—they address the question “what might happen in the future?” u Prescriptive analytics are Information that results from analyses to provide a recommendation of what should happen—answers the question “what should be done?” 12 6  8/5/24 Ernst & Young Foundation Recommended Data Analytics Skills 13 Interpreting Results u Interpreting results can be complicated. u One common way people interpret results incorrectly relates to correlation and causation. u Correlation tells if two things happen at the same time. u Causation tells that the occurrence of one thing will cause the occurrence of a second thing. u A second common misinterpretation of results is noted in psychology research. u Psychology research provides evidence of systematic biases in the way people interpret results. 14 7  8/5/24 Sharing Results u Data storytelling is the process of translating often complex data analyses into more easy-to- understand terms to enable better decision-making. u To tell a successful data story, you will need to: u remember the question that initiated the analytics process. u consider the audience. u use data visualizations. 15 Data Visualization u Data visualization is the use of a graphical representation of data to convey meaning. u Good principles of visualization design include: u Choosing the right type of visualization. u Simplifying the presentation of data. u Emphasizing what is important. u Representing the data ethically. 16 8  8/5/24 Automation u Automation is the application of machines to automatically perform a task once performed by humans. u Robotic process automation (RPA) is computer software that can be programmed to automatically perform tasks across applications just as human workers do. u Companies are using RPA and other automation software to automate tasks within their analytics processes. u RPA is one tool that can be used to automate ETL tasks. 17 Data Analytics is not Always the Right Tool u Data analytics is not always the correct tool to reach the best outcome. u Reliable data does not exist for aspects of many questions. u Human judgment or intuition may be able to account for sentiment factors that cannot be reliably measured. u Data can help us make better decisions, but we need to remember the importance of u intuition, expertise, ethics, and other sources of knowledge that are not easy to quantify but that can have a significant impact on performance. 18 9  8/5/24 Real-Time Analytics/Decision Requirement Product Recommendations Learning why Customers Influence that are Relevant Behavior Switch to competitors & Compelling and their offers; in time to Counter Friend Invitations Improving the Customer to join a Marketing Game or Activity Effectiveness of a that expands business Promotion while it is still in Play Preventing Fraud as it is Occurring & preventing more proactively 19 Key Terms u Big data u Dark data u Data volume u Data swamps u Data velocity u Metadata u Data variety u Data owner u Data veracity u Flat file u Mindset u Delimiter u Analytics mindset u Text qualifier u E T L process u Descriptive analytics u Structured data u Diagnostic analytics u Unstructured data u Predictive analytics u Semi-structured data u Prescriptive analytics u Data marts u Data storytelling u Data lake’ u Data visualization u Data dashboard u RPA u Automation u Bot 20 10