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Accounting Information System Tools

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Accounting information systems (AIS) are tool integrated in the information systems designed to help in the control and management of the organizations especially in the financial sector. The tool are necessary in tracking and monitoring the organization financial plans and budget. AIS tools are the components which are put together to collect raw data and process them into financial data for the shareholders to understand the progress of the company. The raw data is the information gathered like reports and the current trend of the company. For an organization to succeed they must have Accounting information tool for the strategic planning. Due to the changing economy and environment the AIS tool are required to leverage a strong flexible culture for success. Investing in the tools they will increase the performance of the company, reduce financial obstacles and help the company access the capital market. The AIS design tool combines the controls, methodologies and accounting techniques with the information technology to track transactions, financial statements, provide an internal and external reporting data and analyses the trending factors in the environment. These will help the organization to increase its performance (Chuck, 2009).

Data analysis are mainly done by the auditors who need to know more about their rivals and clients. The data analysis tool have helped the organizations gain deeper knowledge about their clientele. The data analytic have advanced in the internal auditing than the external auditing. The organization use continuous monitoring and auditing of data to identify risk and any irregularities which act as a form of internal control (Kuiper, 2012).

The data analytics could be used to test the whole set of data instead of the samples in the external financial statement. The Data mining and analysis also helps in identifying risk or carrying out risk assessment through identification of irregularities and trends of the current environment. The tools can also be used to compare the company performance, data and important information that the auditors need to investigate. The tool are also used in providing audit evidence by providing the comprehensive analysis of the organization general ledger systems.

Data mining is the process of analyzing raw data into a summarized and more perspective based information Michell (2009).The information is useful in increasing revenue and reducing costs. Data mining is an Account information system tool used in extracting and analyzing data. The software provide the users to interpret information from different Angles and dimensions. The information is then classified, categorized and then summarized. The importance of data mining is to provide patterns and relationships of the raw material in the databases (Clipping, 2012).

Data mining has been used by large corporation to scan the volumes of data and analyses the market. The process is done though the mining software’s in computers. The revolution of technology in computers in terms of increased storages, processing power accurate software’s has made data mining easy in terms of accuracy and fats results. The use of the software’s has reduced cost in production and labor. The extracted can also be used to identify the buying habits and patterns of consumer’s .The Information can be used by companies with widespread retail store to control and manage the inventory as well as identify new market gaps and opportunities in the market.

The data mining tool can be useful in live recording of games. They can be used to analyze and monitor the movement of the players to help the referees to manage the game fairly. They can also help the coaches to formulate new strategies and game plans. 


Process of Data Mining and the Benefits

Raw data is data in form of numbers, texts and facts that the computer processes. The organizations accumulate volumes of unprocessed data in different format and in different databases. The type of raw data in an organization include the Operational and transactional data, nonoperational data and Meta data.

The operation data are the data from internal activities of the company such as the cost, inventory, sales and payroll. The non-operation data are the record of the industry sales, economic analysis and research. The Meta data is the dictionary or the database of all the other data. It also store information like logical database and designs. The main work of the data mining tool is to extracting the important information for each department and arranging this information for records. The data analyses software then evaluates, processes and interpret the data into information (Hirsch, 2011).

The data mining tools are used by cooperation with a strong focus to their consumers in terms of communication, finance and accounting, retail and marketing. The tool help the company to balance between the internal and external factors .The internal factor here been the labor skills, prices and product promotion while the external factors include; customer behavior patterns, competition from existing and upcoming companies and economic forecast and future trends. The tool help the company to measure the impact on profits, sales and customers satisfaction. The software helps the company to conclude the finding and summarize the information outcome for the shareholders understand (Goodman, 2010).

The tools can be used to send customized promotions for the consumers based on the individual purchase history. The data can be mined from the point of sales records of the customers who purchased the items. The data can also be mined from the warranty records and the customer’s feedback. The information could be used by the organization to develop custom products and promotion to appeal certain customer or they could use them to target certain group or class of customer in the market.

The extracted can also be used to identify the buying habits and patterns of consumer’s .The Information can be used by companies with widespread retail store to control and manage the inventory as well as identify new market gaps and opportunities in the market.

The data mining tool can be useful in live recording of games. They can be used to analyze and monitor the movement of the players to help the referees to manage the game fairly. They can also help the coaches to formulate new strategies and game plans.

The data mining and analyses software are used in football matches to replay live goals and display it in multiple screen for the fan to watch their favorite moment.

The gathering, storing and using the customer’s information can cause a lot of unethical issue to the company. Most of the data collected from the customer is at times done without their knowledge. An example is when a customer shops in the supermarket an pays for the good using the point of sale system. The customer leaves some information like the product they bought and the amount spent. The company can use that information to their advantage by knowing their customer purchasing patterns. Acquiring the information without the consent of the customer is unethical .The company should inform the customer but most companies cannot do that.

When the customer comment or purchase an item online the company can track their location. The information is important to the company but at times the if the information falls into the wrong hand the customer can end up suffering. The information can be important to the company by for studying their market share but it can lead to problem to the customer if the information gets to the wrong hands. Most of the suppliers use cookies in their website to collect information from the customer without their consent.

Marketers collect big data from the consumers through the internet. The marketers can ask the users questions directly through Email, or they can obtain information from the customer's visit on their website. The buyers are requested to fill their personal information on the site. The information can be used by the company marketers to evaluate the consumer needs and provide a solution to the consumers marketing goals. Some of this information provided can be personal and can be used to hurt the clients. The consumer can provide his or her location and if this information fell into the wrong hands can be used to track the users and maybe steal their property. Some markers use marketing tool on the websites such cookies to collect information from the consumers without their knowledge. The consumer can be innocently shopping online while the marketers can be collecting personal information and habits from the consumers (Kurtz, 2012).

The marketers can use the information to stay ahead of their competitors by understanding the consumer and customizing their product to suit the consumer. Theses information can be used to expose the bad habits of consumers whereby maybe the consumer visit some private website or get information on the net which should stay private. The marketers can use this information to blackmail the consumer (Gerber, 2015). The marketer can use this information to blackmail the consumer in exchange for something. Information provided by the consumer should be utilized for market analysis only.

Marketers should use the information they get to give the consumer a better marketing experience, while they observe their work ethics and respect the consumer privacy 



Kuiper, S., & Clippinger, D. A. (2012). Contemporary data mining report writing. Mason, Ohio, South-Western.

Goodman, M. B., & Hirsch, P. B. (2010). Data analysis and mining: strategic adaptation for global practice. New York, Peter Lang.

Lehman, C. M., & Dufrene, D. D. (2009). Data mining. Instructor's manual. Cincinnati, Ohio, South-Western.

Noor Al-Deen, H. S., & Hendricks, J. A. (2012). Data mining: usage and impact. Lanham, Md, Lexington Books.

Guffey, M. E. (2017). Business Communication:  data process & product. Cengage Learning. 

Wales, T. (2014). Business school libraries in the 21st century.

Lewis, P. S. (2007). Management: challenges for tomorrow's technology Mason, OH, Thomson/South-Western.

Gambetti, R. (2012). Data mining tools of analysis. Palgrave Macmillan.

Guffey, M. E., & Almonte, R. (2010). Essentials of data mining.

Guffey, M. E. (2017). Business Communication: data process & product mining. , CENGAGE LEARNING.

Goodman, M. B., & Hirsch, P. B. (2010). Data mining: strategic adaptation for global practice. New York, Peter Lang. 

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