1. Use of Analytics in Accounting
Understanding data is seen as important in the areas of strategizing and decision-making for finance and business in general. This is developed in accounting using the field of analytics accounting. According to Cao, Roman and Stewart (425), accounting analytics describes the manner in which financial statements and non-financial metrics are attached to interpret financial and accounting performance of a business. The interest that this will be having is one of forecasting that the accounting analytics will be presenting for the future performance of the company or the business. Notably, Chan and Kogan (13) presents the use of accounting analytics under the position of influence, as attached to the needs that business health and will, therefore, be requiring a detailed understanding. In fact, the basic treatment goes back to the use of mathematics in assessing the financial performance and setting metrics that determine the options that the company will be having in accounting detail. Decision-making, in this case, comes as informed, and has a point of reference.
Identifying with the advantages of accounting analytics, the assurance is developed with the awareness that the company will be needing in developing their understanding of business movements. Accounting analytics will be inclusive of measures such as variance analysis, the ratios of business performances and company performance betas. Some of these are financial and accounting functions whereas others are developed as statistical manipulation of data. The main purposes that are again served will be in breaking down the complex analysis results into data that can be understood by average users of accounting statements.
2. Data Structures to Be Used On Analytics
Accounting analytics is implored more and dependent on the data used in the analysis. Therefore, looking at the definition of data structures, as developed by Chan and Kogan (11), the main interest is the arrangement of data. The organization and the storing of data, in this case, comes in handy. This is assumed from the point of view that the business may be interest in different forms of data. The most common types of the data structures used in this case, the data structures are not different from those used in research and statistics in nature. The linear data structures, the composite data structures, and the data trees are normally very common when it comes to accounting analytics. The difference that these data structures have is the relationship and arrangements that they present and not necessarily the data that they represent empirically. Linear data can be used in trend observations. On the other hand, the composite and data tree structure can be used in interdependence relationship analysis where data on different ratios can be used to analyze a particular of the different financial phenomenon.
• Sources for Data Collection
There are a number of data spruces that are available free or also for a premium rate of subscription. For a personal level of business data, the company may choose to hold their records in detail on their own. However, sources such as yahoo finance, Google finance are some of the most pubic and free sources from which liners and composite data can be obtained. On the other end, there are a number of much more detailed data sources such as Bloomberg and Morningstar, which provide their own detailed analysis of accounting informing depending on the industry or the company of interest. Looking at the interest that is served in this case, emphasis is placed on the ease of data access that these sources provide.
• Different Types of Data Formats
Data formats are developed with the intentions and the nature of the data analytics. Identifying with complex analytics structures, the development of data annalistic is presented under the valued assessment of a much more detailed complex data formats. Under accounting analytic, the most common data formats are time series data and panel data (Michael and Wahlen 590). These are described as data that characterize the development of relationships in this case relationships on financial performance. For example, panel data will be majority interested with the longitudinal and the cross-sectional analysis of time series data. In such data formats, the assessment is on the behavior of the data, the characteristics of the data over a period of ties to predict on future expectations.
Other than series data, the other common types of data structures are the broadcast data, which is used in grid compositions, the billing interval data used on market signaling and to some effect the concern of pricing. Also accounting annalistic will be interest with the measures of aggregate statistical data, which are the most basic data set used in analysis based on the detail that it contained and multivariate characteristics.
3. Slicing and dicing
Suggestions from the mathematical school of thoughts bring the interest that is served to slicing and dicing to the topics of topology and set theory. These are basic to the established methodologies of dividing and grouping of data. Therefore, slicing refers to the filtering that is done on data such that focus is presented on a particular phenomenon. In fact, with slicing of data, the interest is in the selection criteria that the data and accounting analyst will be doing. Data selection develops the better understanding of the phenomenon of interest making the inferred decisions better placed based on the detailed selection process performed.
On the other hand, dicing is more of the zooming into the selected area of preference (Michael and Wahlen 590). Looking at the chronology effects, one will note that with an example of financial analysis, a check on the effects that debt capital has on the company performance will be performed using the slicing and dicing approach. Slicing will detail to the selection of the debt ratios and credit ratios. With dicing, the interest will be to zoom into one of these ratios that seem more affected by the index measure on business performance by debt capital.
• Benefits and Limitation of Slicing and Dicing
The separation of data provides a position that is influential in serving specific objectives. This is detailed to the interest that the data analysis will be having. Looking at the preservation of data, the merit is that there is an ease of understanding the interrelation for slicing and dicing of data. Second is that with slicing and dicing of data, less time is used in the preparation of data results. The initial stages may be time-consuming but in general the selection and the zooming in if data dicing create more time saving in concentrating on one area.
The demerits, on the other hand, are attached to the position of inconclusiveness. With slicing and dicing, the end results may not be dependable, the use set of data fails in reliability, therefore, calling for a repeat of the slicing and the dicing of data all together again. Second, is that the use of slicing and dicing will be subject to data selection criterions which are formed on assumptions in the initial stages. Therefore, the effect may be lost again when considering the failure of the zooming and failing to get conceptual and detail results.
Works Cited
Cao, Min, Roman Chychyla, and Trevor Stewart. "Big Data analytics in financial statement audits." Accounting Horizons 29.2 (2015): 423-429.
Chan, David Y., and Alexander Kogan. "Data Analytics: Introduction to Using Analytics in Auditing." Journal of Emerging Technologies in Accounting (2016).
Crawley, Michael, and James Wahlen. "Analytics in empirical/archival financial accounting research." Business Horizons 57.5 (2014): 583-593.
