Data Handling And Business Intelligence Assignment Sample

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Introduction of Data Handling And Business Intelligence Assignment

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Data handling is the process of storing research data securely, or archiving the research data, or disposing the data off in a safe manner during or after the research work is finished. This includes formation of policies to safely store electronic or non- electronic research data. Data handling requires knowledge of statistics.

 Business intelligence is the collection of business data, analysis of the collected data and presentation of the results obtained from processing the data. It includes the fields of “data mining”,” descriptive analytics”,” performance benchmarking” and other such fields. This is a growing field of study for businesses and can increase the profit made by organizations and cut costs. Business intelligence should be considered a serious prospect for businesses and it will go a long way to improve performance of business organizations.

In a very competitive business environment, companies try to go through information regarding their company to make informed decisions about the future of their companies. 

Literature review

Business intelligence is a very important field of study for businesses. It enables businesses to make decisions regarding the future performance of the company. Business intelligence enables to access “big data” from organizations. The data is then analysed for making the useful decisions for the company. “Business intelligence” includes “data-integration”, “analytical capabilities”, “content quality in business processes”, “decision making”.

Researchers have stated that Business intelligence consists of the process of collection data, processing and treatment of data, then obtaining meaning from the data and the decrease of uncertainties during decision making. Students of the topic define “business intelligence” as a ” business management term used to depict applications and technologies which are used to gather, provide access to analyse data and information about an enterprise, in order to help them make better informed business decisions”.

Zeng categorized BI technology “based on the method of information delivery; reporting, statistical analysis, ad-hoc analysis and predictive analysis”. The idea of “BI” has been defined as the application of tools and processes such as “J2EE”, “DOTNET”, “Web Services”, “XML”, “data warehouse”, “OLAP”, Data mining, “representation technologies” and other such technologies to improve the performance of an enterprise and help in decision making for having an advantage in competition with other enterprises. BI is an integrated solution for organizations to address important business issues to help improve performance (Hu and Yin, 2022).

Knowledge and Understanding of Subject

Business intelligence is a field of study where the performance of businesses can be improved with the application of techniques like “data mining”, “data warehousing”, “data analytics” and other such tools. It helps to get meaning out of the large mass of data of an organization and make informed decisions (Lopes et al. 2021).

Data handling is the technique of storing the data in a secure manner or for it to be archived. It helps to maintain the data and then use it for future purposes. Data handling experts need the knowledge of statistics. There are various components of Business Intelligence. Some of them have been described below.

On-line analytical processing is one of the components of Business Intelligence. It lets the business leaders get an overview of the business data and it can be used for analysis, modelling and planning to improve business performance. It is used to find trends in the data. The results are informed to the management to keep them informed about the current state of the business. It includes data mining, data warehousing, information visualization, dash boarding and other such techniques (Nithya and Kiruthika, 2021).

Advanced analytics makes use of statistical techniques to provide measure on the data. Data warehousing is also considered one of the components of BI. It allows spreading of data by using the company data records for “integration”, “cleansing”, “aggregation” and “query tasks”. It consists of “live data”. It contains data that is dynamic in nature and gets updated regularly for the purpose of making decisions regarding the working of the company. The data can be retrieved from databases or obtained from historical sources, market data. The data can also be in structured form like tables, graphs, etc. or unstructured. “Data mart” can contain data which helps in making decisions. Each individual department like Finance, marketing and Sales can have their own data marts. The departments have their own hardware, software, data and programs which all build up their data marts. Each department has an idea of how their data mart can look like. Data marts let business experts study their data and strategize to improve the performance of the company. Business Intelligence has the potential for providing decision support, processing of data, statistical analysis of data and data mining (Phan et al. 2021).


The analysis of the various kinds of data requires statistical techniques. Data warehousing is one of the ways in which analysis of data is carried out. It involves collection of data, cleansing of data, and compilation of the data and query tasks of getting specific information from the whole data set. Collection of data should be done from reliable sources. The cleansing of the data is done to remove any errors in the data. The cleansed data is then analysed to find patterns in the data. Then proper conclusions are drawn. The results obtained from analysing the data help the business organizations to make decisions regarding the strategies to be employed for the improvement of the company in the future.

 Data warehouses contain live data and has minimal data history. The data can be obtained from various sources like databases, historical data stored in archives, market data and other such sources. Data from the warehouse that has been stored earlier can also be a source of data required to perform analysis. During analysis of data various statistical tools and techniques are used. They help in obtaining the results required by the companies to make decisions. Data handling is thus relevant here (Pustokhina et al. 2021).

Data marts are also used to help in the analysis of data. Data marts help to form decisions by the company. In a company each individual department like Finance, marketing and sales have their own data marts. Each department has their own set of software, hardware, data and programs that help in forming data marts. Data marts for the different departments can be unique and they help companies to strategize against competitors and for the future. Data marts contain specific information about the company. 

Practical application and Deployment

Business intelligence is usually applicable for various companies and business organizations. Some of the applications of business intelligence are given below.

Sales Intelligence: The sales department of companies are involved with customer relations. It includes the skill to convince the customer to purchase the products of the company or avail the services of the company. This is one of the areas where Business Intelligence is becoming useful. Business information collects information on topics such as “customer demographics”, “conversion rates”, “sales metrics”, etc. Then “BI” transforms the data into graphs, pie charts, etc. The company can view trends from the data provided in these figures which will provide understandings into customer behaviour and whether the company meets their demands. BI helps to understand the needs of the customers and to get to know them better (Tavera Romero et al. 2021).

Visualization: BI tools let companies to manage data according to the company requirements. The data , presented in a graphical way helps to monitor “logistics”, “sales”, ”productivity” and more. When the data are presented in easy to understand models it lets employees with the minimum amount of knowledge to understand the data that is being conveyed through the diagrams. The different graphical forms of the data include tables, graphs, scatterplots, charts and many more such forms (Velu, 2021).

Reporting: BI application includes reporting the data. The data in its unstructured form is studied and analysed to form reports for the company. Various kinds of reports are required by the company. These reports might include “staffing”, “expenses”, “sales”, “customer services” and other such processes. Reporting is similar to “data analysis” but varies in aims, performance and quality. The process of reporting consists of making a gist of the analysis results so that the company can make decisions on their basis. Reporting turns data into information (Yiu et al. 2021).

Skills for Professional Practice

Business Intelligence is a must have tool for modern business organizations to improve their performance. BI improves customer connect to improve the relationship with the customers of the company. BI is also used for the purpose of forming visualizations on the data that has been collected. The visualizations help the company employees to understand the meaning or implications of data that is collected. The data needs to be collected from reliable sources. The errors in the data are then removed. Then certain statistical techniques are applied on the data to obtain meaning and insights into the data. This process is the analysis phase. Finally the results of the analysis are conveyed using visualizations which are easy to understand by any common company employee. BI includes a combination of different tools like data mining, “data analytics”, “data warehousing” and “predictive analytics” among others. Thus “Business Intelligence” is considered a useful tool for any business organization for it to function properly and grow further in the future. It should be applied wisely for the benefit of the companies.


Hu, H. and Yin, M., 2022. Evolution of business intelligence: an analysis from the perspective of social network. Tehni?ki vjesnik, 29(2), pp.497-503.

Lopes, A.B. and Boscarioli, C., 2021. Business intelligence and analytics to support management in construction: a systematic literature review. Revista Brasileira de Computação Aplicada, 13(1), pp.27-41.

Nithya, N. and Kiruthika, R., 2021. Impact of Business Intelligence Adoption on performance of banks: a conceptual framework. Journal of Ambient Intelligence and Humanized Computing, 12(2), pp.3139-3150.

Phan, L.L., Pham, P.H., Nguyen, K.T.T., Nguyen, T.T., Huynh, S.K., Nguyen, L.T., Van Huynh, T. and Van Nguyen, K., 2021. Sa2sl: From aspect-based sentiment analysis to social listening system for business intelligence. arXiv preprint arXiv:2105.15079.

Pustokhina, I.V., Pustokhin, D.A., Aswathy, R.H., Jayasankar, T., Jeyalakshmi, C., Díaz, V.G. and Shankar, K., 2021. Dynamic customer churn prediction strategy for business intelligence using text analytics with evolutionary optimization algorithms. Information Processing & Management, 58(6), p.102706.

Tavera Romero, C.A., Ortiz, J.H., Khalaf, O.I. and Ríos Prado, A., 2021. Business intelligence: business evolution after industry 4.0. Sustainability13(18), p.10026.

Velu, A., 2021. Influence of business intelligence and analytics on business value. International Engineering Journal For Research & Development, 6(1), pp.9-19.

Yiu, L.D., Yeung, A.C. and Cheng, T.E., 2021. The impact of business intelligence systems on profitability and risks of firms. International Journal of Production Research, 59(13), pp.3951-3974.


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