Data Analytics : How Descriptive, Predictive & Perspective Data Helps In The Finance Industry

In recent years, the topic of big data has gotten a lot of attention. The subject is both broad and deep, and it touches on a variety of other issues, such as accounting and data analytics.

Let’s look at what is it and how financial business intelligence software can be used to impact business and industry, as well as the accounting profession.

What Exactly Is Big Data?

It is a collection of large or complex data sets that can’t be analyzed using traditional databases or tools like spreadsheets. Consider the data that a car could collect: time, the speed at different times, GPS location, dashboard settings, pressure applied to the gas and brake pedals, tire pressure, and so on. The same goes for managing your finance for your business either big or small. It would be difficult for you to analyze this data if they had to do this for then a million vehicles. Every minute of the day brings a slew of new information. The magnitude of the situation is incomprehensible. This is a lot of information!

Big data is information that has multiplied in one or more of the following ways:

  • The volume of data is often measured in terabytes or more, and a typical server’s processing power cannot handle it.
  • a wide range of options Data from a variety of sources, including mobile devices, social media, sensors, and sometimes unstructured formats such as open text
  • Velocity is the rate at which data is collected for analysis, which is often measured in fractions of a second.
  • Data that is inconsistent, incomplete, and occasionally inaccurate.

These data are typically divided into four categories:

  • Product sales, operational metrics, marketing activities, and financial performance are all examples of company data.
  • Ethnicity, gender, social security numbers, purchases, online behavior, and so on are examples of consumer data.
  • Sensor data, also known as the internet of things, or IoT, is a newer category of data that is typically used to track people or things.

Syndicated Data: Information obtained from third parties, such as survey results.

  • Categorical data (i.e., data that is divided into categories such as US regions, yes/no questions, etc.)
  • Ordinal data includes information that can be ranked, such as excellent, good, average, and so on.
  • Interval Data: Similar to ordinal data, but the distance between data points is significant, as in test scores, temperature, or golf handicap.
  • Time, height, and weight are examples of ratio data, which have all of the properties of interval data but have a natural zero.

What Is Big Data Analytics In Accounting?

It has an impact on almost all aspects of accounting. Data in auditing can result in more data-driven audits, which will benefit both the client and the auditor. It can also provide more useful information. It can help identify questions, monitor and improve business performance, and create analytical models that support a variety of product or operational improvements in advisory services. Big data in tax provides the ability to more easily analyze efficiencies, bi tools identify tax-related opportunities for improvement and aid in the evaluation of global opportunities. Finally, big data and bi intelligence can aid risk identification and management in managerial accounting.

Accounting Data Analytics: Descriptive, Predictive, And Prescriptive

The most common use of Data in business is to provide descriptive analytics. This includes categorizing and classifying data in order to make it useful. It can assist in answering questions like which region sold the most product.

It can be used for “predictive analytics” if you dig a little deeper. This allows businesses to forecast the future using historical data. How will a $1 million increase in marketing, for example, affect sales?

Prescriptive analytics is a more advanced approach to accounting data analytics. This method employs optimization to determine the best option or course of action. For instance, “what is the ideal marketing budget?”

Models For Making Decisions From Data Analytics

Decision models are one of the most common uses of data and analytics. These are mathematical models of a problem or a business situation that are used to analyze and make decisions. Some controlled data, such as the number of salespeople, is usually included in decision models. They also frequently include uncontrollable data, such as interest rates or unemployment rates.

Visualization Of Data

Another application of analytics in business and industry is data visualization. Simple graphs are one example, but in recent years, an entire field has emerged to assist analysts in better visualizing data and information. Dashboards are a great way to quickly visualize a variety of metrics, with drill-down capabilities to delve deeper into concerns or questions. Watch How.

Wrapping Up

Along with the growth of the big data industry, a whole category of software dedicated to the visualization of data has emerged. You can get more insights on data analytics and financial data analysis tools by reaching out to one of our consultants.

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