What is meant by Data Analytics?
According to Domo’s Data Never Sleeps 6.0 report, it was estimated that each individual would create 1.7 MB of data every second of the day. Isn’t it faster than a blink of an eye? However, these raw data are meaningless unless they are converted into valuable insights.
In its most literal sense, ‘data analytics’ simply means the science of analysing or evaluating raw data. It is a process of obtaining meaningful insights, trends from raw data and drawing conclusions about the information contained in the raw data. This data analysing process can be used to pull out valuable financial insights from accounting data as well. Most Accounting professionals use data analytics to make informed decisions.
Types of Data Analytics
Data Analytics is a broad and growing field that can be classified into four main categories. Those are:
1. Descriptive Analysis
Descriptive analytics is the process of analyzing past (historical) raw data, to get a comprehensive understanding of things that have already happened in an organization. This is the simplest form of analytics, which can be used for comparisons, identifying patterns and relationships.
2. Diagnostic analytics
Diagnostic analytics is the process of evaluating data to identify the underlying causes of the already occurred changes or things. Descriptive analytics acts as the base for diagnostic analytics as it provides the reasons for the findings of descriptive analytics.
3. Predictive analytics
Predictive analytics focuses on the likelihood of past and current events and predicts possible future behaviors. (by examining current and past datasets) This is widely used to enhance customer services by predicting customers’ future behaviors.
4. Prescriptive analytics
Prescriptive analytics consider all the available data and suggest specific actions or ways to proceed. It is a complex and expensive analytical procedure, which requires a high level of technological interventions like Programming, Artificial Intelligence, (Machine Learning) etc. This can be identified as the next phase of predictive analytics as it uses possible future behaviors, current and past data sets in its analyzing process.
These are four types of data analysis that are in use across all industries. While we separate these into categories, they are all linked together and build upon each other. As you begin moving from the simplest type of analytics to more complex, the degree of difficulty and resources required increases. At the same time, the level of added insight and value also increases.
Importance of Data Analytics for Accounting
Today, the data analytics process has made a huge impact in the field of accounting. The need and the importance of data analytics are increasing day by day. The insights obtained by analyzing the information contained in the data can be used to improve the efficiency of operating processes, reduce risks and avoid unnecessary costs of the organizations.
1. Enhanced the accuracy of Financial Reporting
Financial reporting is a complex process that requires a lot of time and expertise. Maintaining a high accuracy level with effective financial reporting is a difficult task for every organization. Delays and errors in the recording process, having multiple operating units in different locations (different standards, tax laws, currency conversion issues), and using outdated technology (manual processing) can be identified as some of the major challenges in the reporting process. Financial reporting can get the help of data analytics to increase the quality and the accuracy as it can be used for continuous monitoring analysis, non-financial data analysis, peer group metrics, benchmarking analysis (Profit Margin, Return
on assets – ROA, Inventory turnover, Inventory composition).
2. Improve the efficiency in operations
Data analytics can be considered as one of the most important area in the field of accounting as it increases the amount of value that can be added to an organization’s operations. Based on the past data used in financial accounting, it is possible to find out whether the company is moving towards the expected targets which were set at the beginning of the year. Data analytics can identify the unnecessary expenses, purchases and obstacles that could hinder the efficiency in performance levels. Further, it will identify the factors that may affect long-term survival and will respond to them promptly.
3. Mitigate Risk
There is a famous quote by William Faulkner: “You cannot swim for new horizons until you have courage to lose sight of the shore.” This saying is well-suited to the business world because if an organization wants to achieve success, that company needs to take risks day by day to identify opportunities. Today, the role of the accountant is not limited to bookkeeping and financial reporting, it extends to the areas like risk management and mitigation. Data analytics can be used for risk identification, reduction, and control.
Accountants need to act as risk experts to foresee uncertainties and to suggest suitable internal controls.
4. Improving customer satisfaction to generate high profits
Every business is based on its customers. Increasing customer satisfaction levels or improving customer experience equals to increasing profit or the growth of the company. It is easy to attract new customers and retain existing customers within the organization by utilizing the insights obtained through the data analytics process.
5. Industry Knowledge
The conclusions drawn from the data analytics processes are consist of a vast amount of knowledge. This process also can enhance the understanding and awareness as it pulls out valuable insights about future trends, current and past changes, relationships between accounting items. Further, the knowledge gained from predictive and prescriptive analytics will have a very high level of accuracy with its technological interventions.