Accounting Analytics Chapter 1

Data

Raw figures and facts

Information

The knowledge gained from data

Data analytics

Process of analyzing raw data to answer questions or provide insights

Self-service business intelligence software

Provides extended data processing capabilities for preparing, analyzing, and reporting data analysis results

Is easy to use

Auditing anayltics

Audits have expanded beyond sample-based testing to include analyses of entire populations of relevant data

Auditors can review entire data sets to identify all exceptions, anomalies, and outliers

Data driven audits reduce the time the client spends gathering information and allows more time for the analysis, making it a better experience for all involved

Financial accounting analytics

Performs routine financial accounting function

Create financial dashboards

Manegerial accounting analytics

Identify and manage risks

Improve budgeting and forecasts

Automate internal reporting

Identify operational improvements

Create KPI dashboards

Tax analytics

Tax compliance

Speeds up process

Tax dashboards moniter real time tax positions

Stages of data analysis process

Plan

Analyze

Report

Plan

Understand motivation

Determine objective

Design data and analysis strategy

Motivation

Why analysis is being performed

Internal or external

Determine the objective

Clear objective narrows focus

Specific questions can be developed

Design the data and analysis strategy

Determine data necessary to answer questions

Decide what type of analysis is appropriate considering the data and those questions

Descriptive data

Investigates what is happening currently or has happened in the past

First analytics performed to help understand data

Sum, count, average, median, SD, and proportions

Diagnostic data

Helps understand why something happened

Inform decision-making about actions in the future

Anomaly and outlier detection, trend analysis, and pattern recognition

Predictive data

Forecasts what might happen in the future

Uses data, statistical algorithms, and machine learning to identify the likelihood of future outcomes based on historical data

Forecasting, regression, and time-series analysis

Prescriptive data

Helps understand what should happen to meet goals and objectives

Optimizations and what-if analyses

Analyze

Prepare data

Build information models

Explore data

Data preparation: ETL

Data is extracted from a source, transformed by cleaning, restructuring, or integrating with other data prior to analysis, loading is the process of uploading transformed data into analysis software

Build information model

Creation of information needed for analysis purposes, starting from data collected

Explore data

Identify patterns, trends, or unusual observations

Lets us discover, question, and investigate data relationships to successfully execute data analysis objectives

Report

Interpret results

Communicate results

Interpret results

Process of reviewing analyses to make sure the make sense based on the project’s objective and that the results are valid and reliable

Communicate results

Can be done orally, with visuals, or in writing

Often include data visualizations

Can be a dashboard

Data analytics mindset

Professional habit of critically thinking through the planning, analysis, and reporting of data analysis results before making and communicating a choice or decision

Are inquisitive, ask why, open to learning new technologies, and evaluate their own thinking

Develop skills such as critical thinking, data literacy, technological agility, and communication skills

Critical thinking

Disciplined reasoning used to investigate, understand, and evaluate an event, oppurtunity, or an issue

Reasoning

The human process of logically forming conclusions, judgements, or inferences from facts

Data literacy

Ability to understand and communicate data

Technological agility

Awareness of latest technological developments and a willingness to try new things

Communicating data analysis requires specific skills:

Writing clear and effective memos and reports

Preparing successful presentations

Creating meaningful data visualizations

Telling compelling data stories

Six elements of critical thinking

Stakeholders

Purpose

Alternatives

Risks

Knowledge

Self-reflection

Data risks

Choosing inappropriate, incomplete, or incorrect data

Analysis risks

Choosing an inappropriate or incorrectly applying method

Assumptions risk

Not understanding or evaluating assumptions about data or the results

Bias risks

Mental shortcuts can affect decisions