Business Report And Data Mining: Analysis And Methods

What is a business report? What are the main characteristics of a good business report?

Reports serve several purposes and are used to communicate information to people who are not involved in research or a certain set of findings. Similarly, a business report is a set of evaluation statements of certain issues or even circumstances that are affiliated with the financial operations as well as activities that generally affect business performance (Meintjes 2015). Furthermore, these reports are written as responses to current or expected events that promote senior management to engage the lower management to make changes that will outline solutions to those events. For instance, a senior management in a given company may notice the wastage of resources and request the lower management to outline the reasons for this occurrence. The finding, as well as the proposed solutions, are usually outlined in a writing known as a business report (Humber 2015).

Writing business reports is a necessary skill needed by any business minded individual as they define the solutions adopted by an organisation with reference to the prevailing business environment. Nevertheless, when writing a business report it’s important that the writer focuses on the issues at hand (Woods 2017). Moreover, the main subject must be clearly defined to convey the general outline. Furthermore, the information must be accurate and simplified to give the reader a deeper understanding. To meet these objectives the following features/attributes must be satisfied.

1.    Clearly outlined purpose/objective – At the start of the report, the reader should be able to know the general purpose of the report e.g. to increase a company’s revenue. Moreover, this purpose should also be seen in the title that then follows an in-depth yet summarised introduction. Finally, the purpose should also state whether past data or future prediction are used (Orauariki 2017).
2.    Accuracy – Since reports are based on factual assessments/evaluations, accurate data should always be quoted. For instance, an analysis of a business performance in the past year will outline the quarterly numbers of the enterprise. Therefore, for integrity purposes and also to maintain the accuracy of the conclusions, the actual and true values must be used.
3.    Simple language – As stated before, a detailed report is needed however, the details must be conveyed in a simplified way for all professionals to understand regardless of their background. A simple language is a good start to ensure all readers understand the message. Simplicity means the report should avoid many technical terms related to a given field and if used, they must be elaborated or defined (Suttle 2017).
4.    Completeness – A major attribute of a good business report, where all the information needed is conveyed to the reader. To ensure a report is complete, the writer must cover all the objective set at the start of the assessment. Furthermore, reports should have a consistent structure for instance, an introduction, body and conclusion. These sections must be clearly defined and fit the normal standards.
5.    Concise outline and presentation – Adding too many variables and information may distract the reader, therefore a concise tone and structure must be evident (Woods 2017). A quick and brief executive summary can be used to outline the issues at hand in a quick and elaborate way. Moreover, the writer must also use a minimal word count to engage the reader from the start to the end.

According to Piatetsky & Frawley (1991), data mining a defined as a process used to discover knowledge or information that in one way or another holds a consistent pattern, change, association, anomaly and has a pre-defined structure. Moreover, this information is sourced from large volumes of data that can either be a database, or a warehouse or even a much bigger data respiratory. Nevertheless, despite the source, the objective of data mining is usually the same, that of obtaining conclusive results from a wide variety of data that is readily available to the user.

Data mining can also be defined as the evolutionary process of analysing and understanding information as a result of data functionalities such as data collection, creation and management. Furthermore, this process is a result of a prolonged and natural evolution of information as well as technology which in the past used simple techniques to analyse and understand data. Therefore, the past techniques of data collection and evaluations serve as a pre-requisite for today’s data development which includes data storage, retrieval, transaction and query processing (Han & Kamber 2000).

Definitions of data mining

Finally, data mining is also seen as a technique that is used to uncover interesting data facts beneath hidden data patterns that occur in large volumes of data sets (Maurizio 2011). Now, these techniques are popular today because of the availability of information that truly exceeds the imminent and future needs. Moreover, the available data tends to a have many unnecessary details that lower its accuracy and quality. Therefore, by using data mining techniques users are able to interoperate the available data to form accurate conclusions that are helpful in business and other operations.  

In all the definitions outlined above, several things stand out, one big data i.e. large volumes of data that are currently available. Secondly, quality and accuracy of information a key component of organisation development be it a profit making business or otherwise. Therefore, a quick summary of these definition sees data mining as the process of identifying and sorting data to extract valuable information that can be used for various analytical purposes (Pro-global 2014).

Now, data mining is quickly becoming popular today because of the critical role it plays in auditing processes that evaluate and assess businesses. In essence, from the available data, valuable information is extracted and is used for decision-making processes that generally outline the direction taken by an enterprise. Decision making is a crucial process as it encapsulates all factors of business into one wholesome concept that defines how operational activities are conducted. Information will help executive and management make accurate decisions that are based on facts and not intuition. Moreover, with modern data analytics data mining offers a quick fix to business problems such as developing profitable business ventures without affecting the existing operations. Furthermore, information technology has increased the popularity of data mining by offering sophisticated software that analyses data effectively and accurately (Alexander 2016).

In addition to this, consider business activities in past where auditing processes such as data analysis took time to accomplish. Furthermore, users were limited by the available technologies that could only support few records. However, with today’s database systems enterprises have the ability to look at many records that in most cases will even supersede the existing business needs. Nevertheless, if data mining concepts and application were non-existence, cumbersome processes would be used to evaluate data which again would limit its use. Moreover, these database systems or data warehouse would have to limit their practicality to the source of information i.e. one analysis method for one source of data.

Why is Data Mining becoming so popular these days?

Data mining solve the problem above by using innovative Softwares that analyse data from different sources using the renowned business concepts (of course at revolutionary speeds). Therefore, today’s auditors and evaluators are more focused as they have a wider reach which is supported by many and accurate business tests. In fact, during assessments, an auditor can analyse a given data set using a number of evaluation techniques before performing the final auditing test. Moreover, these processes are simple and cost effective which lowers the overall expenditure cost which outlines the current popularity of data mining processes on transactional auditing and business (Pro-global 2014).

Finally, consider the many functionalities and applications of data mining processes despite the fact that it’s still in its infancy stages. For one, it can be applied in all industries from retail to transportation and even healthcare. Business activities such as market identification and segmentation can be determined using data mining concepts where the behaviours and purchase patterns of customers are outlined. Moreover, it enhances the interaction between businesses and customers by developing solutions that are based on the immediate needs of both of the parties through trend analysis. Finally, it enhances risk management practices by providing accurate assessment that can be used for forecasting purposes (Alexander 2016).

Data mining methods or techniques have been in existence for many years however, their popularity grew after big data started to dominate the market. In essence, big data caused a huge explosion of information that is more extensive in all business contexts. These contexts include size, nature and availability among many other. Therefore, with the data seen today, users must develop intricate processes that provide detailed analysis other than simple statistics. For instance, it’s common to have millions of records that outline general information of customers such as their names, contact info and addresses. This information is further complicated by the fact that these companies will want to outline the behaviour exhibited by their customers, hence the importance of the data mining methods.

1.    Association– a simple and straight forward technique used to analyse data where correlations are made between two sets of data. It’s the most commonly used method as it outline the relationship between different data variables. Therefore, two factors or variables are considered and then a prevailing pattern is identified. An example of this method; a franchise retail store may identify that customers usually bus the store’s cream when buying strawberries. In essence, this relationship is identified by a data mining process that evaluates the behaviours of the customers with relation to the products. Therefore, in the next business quarter, the retail store can suggest to the customers that they buy cream when buying strawberries (Brown 2012).
2.    Clustering– data can be grouped based on the characteristics possessed by the different variables in question i.e. objects that form classes. Clustering is a data mining method that groups or forms useful categories (clusters) based on the characteristics exhibited by the data variables. Moreover, this grouping is done based on conditions or classes defined when developing the evaluation technique. Example; consider a library with many different books, when reader desires a certain book they may be forced to look at all the books available. However, through a clustering technique books are grouped based on a given similarity factor e.g. subject. This outcome narrows the search as the reader now moves to a certain section of the library to find their book (Zentut 2017).

A set of calculations and heuristic processes that create a meaningful model of data. Algorithms create these models by analysing data and specifying the patterns or even trends exhibited. Moreover, these patterns are only developed after repetitive processes or iterations that define optimal factors that generally outline the mining method.

1.    C4.5

A common analysis algorithm used to impose classification rules/patterns from a given set of data in the form of an elaborate decision tree. When provided with a data set, the algorithm will assess the instances available based on their attributes to classify them using new and unseen patterns. Example; consider a dataset with patient records from age, pulse, blood pressure and family history etc. Using these attributes, doctors can develop two different classes (patterns) of patients who are likely to get a certain disease such as cancer (Kumar 2009).

1.    k-means

Another simple algorithm that uses an iterative process to group and categorises data based a given variable or cluster (k) as seen before. Therefore, this algorithm will use the concepts of cluster analysis where data patterns are defined based on the members involved who either have similarities or not. Example; using the same example as before of patient records, a user can define the number clusters needed (number of k). Therefore, a given data set can be optimised to have various conditions or variables which are then used for other analysis (Kumar 2009).

References

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Brown. M, 2012, Data mining as a process, IBM developer. Available at: https://www.ibm.com/developerworks/library/ba-data-mining-techniques/ [Accessed 08 May, 2017]

Han. J & Kamber. M, 2000, Data mining: concepts and techniques, Simon Fraser University. Available at: https://cs.wmich.edu/~yang/teach/cs595/han/ch01.pdf [Accessed 08 May, 2017]

Humber, 2015, Business report. Liberal arts & science. Available at: https://liberalarts.humber.ca/assets/files/new-pdfs/BUSINESS%20REPORTS.pdf [Accessed 08 May, 2017]

Kumar. V, 2009, The top ten algorithms in data mining. Chapman & Hall/CRC Data mining and knowledge discovery series. Available at: https://gforge.create-net.org/gf/project/pottahole/scmsvn/?action=browse&path=%2F*checkout*%2Fmaterial%2FData%2520Mining%2FWu_Kumar_The_Top_Ten_Algorithms_in_Data_Mining.pdf [Accessed 08 May, 2017]

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