Being an accountant is not a straightforward job with the digitization of the world. Almost all solutions come to a point where data support is needed to get the outcome.
Learning data science is not a solution, especially for accountants who had already spent decades of years working. However, not getting informed about the data matrix will make their job strenuous. With this, it may happen that their expertise is less important or outdated. This conveys an important message to accountants for having knowledge of data science.
Accountants use data for
a. Strengthening decision-making process
b. Internal audit
c. Financial management
d. Risk management
The task is to use commercial sense and training to make data an effective tool for decision-making and compliance. The slicing and dicing of data is the most technical part to make it usable. Data value-add to the business decision-making improves processes to increase efficiency and gives valuable insight.
Effective use of data creates a high level of curiosity among users. It always gives a unique insight whenever the data bank is used in various ways and formats. Imagination, curiosity, agility help to create new ways and patterns beneficial for organisations based on their structure, needs, and resources.
My take is that not everyone can become an expert in every stream of their profession, but the important is to understand the concepts emerging around. By doing so, a professional can blend the new emerging concepts in his expertise field.
The popular concepts used are; Data Lake, Data Warehouse, and Data Munging. The most important work is more related to data munging, converting one in ‘raw’ form into another format or preparing data for a specific purpose. By doing this, the data becomes useable. It is also called cleaning of data that is ready for analysis.
We divide the uses based on the functionality, that is Descriptive, Diagnostic, Predictive, and Prescriptive.
Most business units where the prior available data is used to find out the reasoning behind the present position use Descriptive analysis. The review of performances is the correct example of it. However, it is used to set trends for the future.
The next step is Diagnostic analysis, where the reason for any occurring is analysed. For example, if a business unit has seen a sudden surge in profit, then it is essential to find out the reason. Repetition of similar trend need identifying the reasons. So that the success repeats with enhancement.
Then comes another step of Predictive analysis, where we predict the futile outcome of the business unit based on prior data patterns. The decisions and strategic plans are based on predictive analysis and thus vital for revenue managers and finance directors. The success in a plan, actions are more likely based on how well the predictive analysis is used.
The last and fascinating step is Prescriptive analysis, which extends to Predictive analysis. It shows the outcome of the plan, actions, strategies. These four steps are to be used in a sequence. First, using prior available data, then climbing into the forecast for the future course of action.
We can successfully complete this process and make it effective only if the data is enough that can be mugged carefully toward the desired outcome. Here, 'enough' means 'excess', where the data collection points must be multiple.
Decision-making process;
a. Define the problem by co-relating it with the prior data, descriptive analysis
b. Developing a viable idea, having a diagnostic analysis
c. Analysis strategies create framework and mapping, the combination of predictive and next step of prescriptive analysis.
The entire process is completed in the steps, and the outcome gives a solution to the problem.
Accountants need to develop the ability to analysis prior data and reach a conclusive finding of happening. Then, predicting the outcome of the future based on the plan, actions, and strategies in place.
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