Data analytic overall view

CHAPTER 6

CHAPTER 7

CHAPTER 5

Chapter 5

Introduction to Data Analytics in Accounting

SMART Questions

Automated tools

Big data

Data Visuali-zation

ETL Process

Analytics types

RESTART

01 SMART QUESTIONS FOR SMART ANSWER

Analytic Questions

• Specific: needs to be direct and focused to produce a meaningful answer.
• Measurable: must be amenable to data analysis and thus the inputs to answering the question must be measurable with data.
• Achievable: should be able to be answered and the answer should cause a decision maker to take an action.
• Relevant: should relate to the objectives of the organization or the situation under consideration.
• Timely: must have a defined time horizon for answering.

02 Extract, Transform and Load Process

Extracting Data

Transforming Data

Loading Data

1.1

01

Understand data needs and the data available.

Processes in data Extraction

02

Perform the data extraction.

Back

Verify the data extraction quality and document what you have done.

03

1.2

Three Alternative Structures: Data Warehouse, Data Mart, and Data Lake

Back

2.1

Four steps in the data transformation process

Understand the data and the desired outcome.

Validate data quality and verify data meets data requirements.

Standardize, structure, and clean the data.

Document the transformation process.

Back

Data Analytic Techniques

Predictive analytics

Descriptive analytics

Information that results from analyses that focus on predicting the future—they address the question “what might happen in the future?”

Information that results from the examination of data to understand the past answers to the question “what happened?”

Prescriptive analytics

Diagnostic analytics

Descriptive analytics and try to answer the question “why did this happen?”

Information that results from analyses to provide a recommendation of what should happen—answers the question “what should be done?”

Data visualization

InterpretingResult

Sharing Result

5.0 Additional Data Analytic Consideration

Automation

Data Analytics is not Always the Right Tool

• Automation is the application of machines to automatically perform a task once performed by humans.
• Robotic process automation (R P A) is computer software that can be programmed to automatically perform tasks across applications just as human workers do.
• Companies are using R P A and other automation software to automate tasks within their analytics processes.
• R P A is one tool that can be used to automate E T L tasks.

• Data analytics is not always the correct tool to reach the best outcome.
• Reliable data does not exist for aspects of many questions.
• Human judgment or intuition may be able to account for sentiment factors that cannot be reliably measured.
• Data can help us make better decisions, but we need to remember the importance of
• intuition, expertise, ethics, and other sources of knowledge that are not easy to quantify but that can have a significant impact on performance.