Introduction
Growth of dataOrganizations are accumulating data at an exponentially growing speed. Just a decade ago, the volume of data transferred in a year globally was estimated to be 5 zettabytes (that is approximately equivalent to 5,000,000,000,000,000 megabytes). In 2021, the volume of data transferred globally is estimated to be 79 zettabytes (Source: Statista).
But how are any of the 79,000,000,000,000,000 megabytes of data relevant to you? If you're looking to open a new retail branch as a business owner, data can provide insights on whether the new location has a high chance of success. If you're running a digital advertising campaign, you can swiftly adjust the campaign based on real-time feedback. As an auditor, you can perform control tests using structured transaction data. For every scenario we just discussed, the end goal remains the same. You want to make better decisions using data.
What is Data Analytics?Data analytics is the science of deriving insights from raw data. It combines multiple disciplines from statistics, mathematics, programming, and even art! 🎨 Data analytics is further categorized into four different types of analytics.
- Descriptive Analytics: Analyze historical and current data to describe what happened (or is happening)
- Diagnostic Analytics: Use data to understand why something happened in the past
- Predictive Analytics: Predicts what is likely to happen in the future 🔮
- Prescriptive Analytics: Test potential outcomes of each decision using advanced algorithms and recommends the best course of action
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Steps in working with data
Finding or gathering data is the starting point of any analytical processes. Both numerical and categorical data can be collected.
This step involves a repetitive process of loading, transforming, and digging through the data. Your goal is to find latent patterns that can be used to derive insights.
This step is often referred as a storytelling phase. You communicate your findings and suggest actions like you're telling a story. The most common approach is to graphically represent data.
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Data Analytics in Accounting
ManagerialManagerial accountants can use analytics to gain advantage over competitors and execute strategic improvements. Despite these advantages, many organizations are still not utilizing data to make critical business decisions.
TaxGlobalization, regulatory changes, shorter business cycles, and increased scrutiny mean that tax accountants need to become more agile to respond to market changes swiftly. Organizations can only make informed business decisions if they are able to locate, validate, and analyze tax data.
AuditAuditors can use data analytics to identify risks, assess risks, and perform testing on a larger scale. This leads to a higher audit quality by improving auditors' knowledge about transactions.