Question

1. How is big data analytics applied in accounting? You must

explain in detail how big data analytics applied are used. Give and elaborate three (3)

examples. (3 Different accounting examples.) [600 words]

2. Why is big data analytics applied in those examples? What are the benefits to the company or

organisation? [100 words]

* Please give in-depth answers with supporting references (journal articles, company reports etc.), appropriate with the word limits.*

Answer 1

1)Accountants use data analytics to help businesses uncover valuable insights within their financials, identify process improvements that can increase efficiency, and better manage risk. “Accountants will be increasingly expected to add value to the business decision making within their organizations and for their clients,” comments Associate Professor Wendell Gilland, who teaches Data Analytics for Accountants at UNC Kenan-Flagler Business School. “A strong facility with data analytics gives them the toolset to help strengthen their partnership with business leaders.”

Here are a few examples:

(i)Auditors, both those working internally and externally, can shift from a sample-based model to employ continuous monitoring where much larger data sets are analyzed and verified. The result: less margin of error resulting in more precise recommendations.

(ii)Tax accountants use data science to quickly analyze complex taxation questions related to investment scenarios. In turn, investment decisions can be expedited, which allows companies to respond faster to opportunities to beat their competition — and the market — to the punch.

(iii)Accountants who assist, or act as, investment advisors use big data to find behavioral patterns in consumers and the market. These patterns can help businesses build analytic models that, in turn, help them identify investment opportunities and generate higher profit margins.

Four types of data analytics

To get a better handle on big data, it’s important to understand four key types of data analytics.

1. Descriptive analytics = “What is happening?”
This is used most often and includes the categorization and classification of information. Accountants report on the flow of money through their organizations: revenue and expenses, inventory counts, sales tax collected. Accurate reporting is a hallmark of solid accounting practices. Compiling and verifying large amounts of data is important to this accurate reporting.

2. Diagnostic analytics = “Why did it happen?”
Diagnostics are used to monitor changes in data. Accountants regularly analyze variances and calculate historical performance. Because historical precedent is often an excellent indicator of future performance, these calculations are critical to build reasonable forecasts.

3. Predictive analytics = “What’s going to happen?”
Here, data is used to assess the likelihood of future outcomes. Accountants are instrumental in building forecasts and identifying patterns that shape those forecasts. When accountants act as trusted advisors and build forecasts, business leaders grow increasingly confident in following them.

4. Prescriptive analytics = “What should happen?”
Tangible actions — and critical business decisions — arise from prescriptive analytics. Accountants use the forecasts they create to make recommendations for future growth opportunities or, in some cases, raise an alert on poor choices. This insight is an example of the significant impact that accountants make in the business world.

Why accountants make excellent data scientists

Accountants have outstanding technical skills. Gilland notes, “Accountants are used to aggregating information to create a picture of an organization that summarizes the details contained in each transaction. Working with descriptive analytics, predictive analytics, and prescriptive analytics comes more easily to people who already possess excellent quantitative skills.”

Accountants are natural-born problem solvers. The jump from descriptive and diagnostic analytics to predictive and prescriptive analytics requires that one shift from an organizational mindset to an inquisitive mindset; a shift from stacking and sorting information to figuring out how to use that information to make key business decisions. Accountants are experts at making this jump.

Accountants see the larger context and business implications. The true value of data analysis comes not at the point when the data is compiled, but rather when decisions are made using insights derived from the data. To uncover these insights, a data scientist must first understand the business context. Not only do accountants understand this context, they live it.

2) Benefits of Big Data Analytics applied in thos examples which will benefit to the company

Big Data Management solutions provide companies the ability to add a variety of data from hundreds of different sources in real time. This means that you can increase the client’s commitment since you can have more effective interactions with them and better marketing proposals, which ultimately lead the company to achieve a longer and more profitable relationship with the client.

Big Data Analytics, made with advanced Big Data Analytics solutions, provides organizations with complete customers’ profiles, which allows for more personalized customer experiences at each point where contact is made throughout the entire journey of the company.

Big Data Management solutions eliminate data niches, so that organizations can obtain a unique view of the customers that include countless descriptive, calculated and industry-specific metrics that allow for the construction of a detailed record of the behavior of each client. These profiles provide organizations with a global understanding of their clients through in-depth knowledge of the client and its operations.

Big Data can be intimidating, but with a good Big Data Management solution, your organization can address the data it needs to obtain actionable information and increase the value of the entire relationship with the client. Apps developed by a smartphone app development company can be used to sustain a good relationship with your clients.

The reality is that the as data volumes continue to increase, its promise for companies also appears to exponentially grow. This allows companies to convert raw data into relevant projections, predictions, and trends with accuracy. A list of some of the benefits of Big Data Analytics could include:

• It can provide ideas from huge amounts of data from multiple sources that include those that come from external third-party sources, the internet, social networks, those already stored in company databases etc.
• Real-time forecasting and monitoring of occasions that may affect the performance or operations of the businesses.
• Capability to locate, get, extract, change, analyze, and blend data with different tools.
• Identification of important information that can improve the quality of decision making.
• Ability to mitigate risks by optimizing complex decisions about unplanned events more quickly.
• Identification of the causes of failures and problems in real time.
• Full understanding of the potential of data-driven marketing.
• Generation of offers to customers based on their purchasing habits.
• Improvement of the commitment of the client and increase of his loyalty.
• Reevaluation of the risk portfolio quickly.
• Customization of the customer experience.
• Adding value to the interactions with online and offline customers.

Another way to see the benefits of Big Data is through these eight qualities required by companies and that are present in the Big Data:

• Big Data is Timely: 60 percent of each work day, knowledge workers spend it trying to find and manage the data. Big Data can provide timely reports immediately.
• Big Data is Accessible: Half of senior executives indicate that access to correct data is often difficult.
• Big Data is Holistic: The information is currently stored in silos within many organizations. Marketing data, for example, can be found in web analytics, mobile analysis, social analysis, CRM systems, A/B testing tools, email marketing systems, and many other sites each with its focus on its silo.
• Big Data is Reliable: Things as simple as securing the correct contact data of customers through the review of multiple systems can save thousands of Dollars in incorrectly sent communications.
• Big Data is Relevant: 43 percent of companies are not satisfied with the ability of their tools to filter irrelevant data.
• Big Data is Safe: A breach of data security costs hundreds of dollars per customer.
• Big Data is Precise: Businesses have difficulty with multiple versions of the fact based on the supply of their info. Combining multiple reliable sources, businesses can create accurate correct of intelligence.
• Big Data is Usable: Many companies make bad decisions due to obsolete or bad data. Big Data can ensure that the data is usable without fear of mistakes.