Data Mining as a Business Intelligence Tool: Applications, Pros and Cons

Published by: Iloka Benneth Chiemelie
Published on: 8th October 2016

Introduction

Studies indicate that there have been a tremendous increase in the business technology landscape and this shift has been very fast that it is now possible for one to take a little step out, just to come back and witness a different thing. Mainly, this shift have been influenced by the increasing volume of innovation and creativity, which is further aided by the increase in advanced technologies across the globe, which has made consumers demand more complex and easily customizable (Computer Weekly, n.d.). As a result of that, businesses are doing whatever is necessary enhance their market shares by understanding the individual needs of consumers and customizing products that meet these needs.

What is business intelligence?

Business intelligence is very board when viewed as a business process and it encompasses all features of understanding the needs of consumers and utilizing such understanding to manufacture products and services that excite them. Business intelligence is considered a decision making process that is data driven and it covers all aspects of analyzing, aggregating and visualizing the data that has been gathered in order to create information and aid the business management process and also enhance overall strategic decision making  (Expert Systems, 2016). From a generalized view, BI is used to reference some feature of information that are being gathered and compressed, but that is not all as it also involves what the business does with the information that have been gathered – as it reflects in their decision making process.

The importance of business intelligence in the present economic setting is that businesses are internationalizing, and the global economy is increasing industrializing – leading to a higher level of competition. Thus, the only way for business to ensure sustainability is to understand the independent needs of consumers as a group and service these needs individually (customized). Thus, gathering information and data from the consumers as well as compressing them to generate real meaning for decision making is a necessity in modern businesses. This is based on the understanding that it allows companies to create products and services that better serve the needs of the consumers – increasing their loyalty – and enhancing the performance of the company in the process. In essence, business intelligence is correlated with the performance of a company.

Data mining as a business intelligence tool

Data mining involves finding answers to the business process that were formerly unknown. Due to the vast volume of information that can be obtained through such analysis, it has been noted that managers are never certain as to whether or not they are overlooking vital information in their business management process. As a process, data mining is the practice of siting through all generate data in course understanding patterns that were previously unrecognizable. This has become so important in modern business that some companies are now hiring data scientists, statistical analyst, and computer scientist that are vastly equipped with the necessary competence for finding the hidden signs within a sounded business process. (Junk, 2015) To a greater extent, data mining does fit well within analytics category, but majority of the analysis are based on data from system that has been set up to tract known KPIs – thus, it can be considered more of measuring than mining. 

Application of data mining in business

As the importance of data analytics grow continually, corporations are finding new ways to apply data mining and business intelligence. There are numerous ways it can be applied but some of the major ways are as discussed below.

Service provider: the first example is form services providers in the utilities and mobile phone industries. They utilize data mining for the purpose of predicting churn, a terms used when a company departs a given company for a competitor in search of service offered by the company. The service providers collate billing information, interactions with customer services, visits to websites and other metrics to attach probability score to each customers, and then target offers and incentives to the customers which they consider to be highest level of risk churning (Matillion, n.d.).

Retail: the retail industry is another example of corporations that use data mining for business intelligence. They separate the consumers based on Recency, Frequency, and Monetary (RFM) groups and target marketing related promotions to these different groups. A consumers that spend less time is normally be handled in a different way from those that spend more time with the company. A higher volume of loyalty, upsell and cross-sell offers will be accorded to those that send less time with the company, while those that spend more time with the company will be given more of a win-back deal (Matillion, n.d.).

E-commerce: one of the most commonly known and utilized form of business intelligence in the case of data mining is in e-commerce sales. Many of the e-commerce companies use data mining and business intelligence to provide cross-sells and up-sells via their corporate website. Some of the most famous companies in the area include Amazon and eBay that utilize sophisticated data mining approaches to drive enhance the product availability of people who viewed and liked their products. For instance, if a consumers wants to make purchase, other related products will be shown to the person for comparison and more purchase in the process (Matillion, n.d.).

Supermarkets: another good example of business process that involves data mining is the supermarket. The famous supermarket loyalty card programmes are usually driven (if not solely) by the decisions of the supermarket to gather details information about their consumers which will be later used for data mining. A good and recent example is the Target store, which recently developed rules that the company will use to predict if their consumers are more likely to be pregnant by taking a look into the content of their consumers’ shopping baskets and spot those that they are more likely to be expecting babies and initiate targeted marketing to these consumers. The programme was so popular that target actually started sending out promotional coupons to families that did not know or are yet to realize that they could be pregnan (Matillion, n.d.)t.

Crime agencies: the sue of data mining and business intelligence is not something that is solely reserved for the business world as they are increasingly being applied in the crime and prevention agencies where data analytics and mining are used to spot new trends. They utilize data mining to determine where to deploy police manpower and to search at bother crossing in order to determine recent activities (Matillion, n.d.).

Overall, data mining is increasingly becoming an integral aspect of the business process based on the understanding that it can easily be used to predict changes occurring in the business world. However, data mining is not all about the flare as it does come with a number of issues. The major issue when it comes to data mining is the issue of data quality. Normally, the mining process are based on vast combination and process of big data warehouse (which can normally contain outdated or incomplete data) (Rouse, n.d.). In essence, processing these data might not necessary guarantee success with the outcome based on the fact that findings might not be a clear reflection of the present needs.

Secondary, the issue of validity and reliability has also been stressed with data mining based on the understanding that it can sometimes contain information that are coned for the purpose of delivering set objectives (Rouse, n.d.). In unethical research process it is possible for data analysts to just feed huge volume of data into the data warehouse in order deliver their desired outcome – making the finding invalid and unreliable. As such, it is important to consider the issues with data mining and adopt necessary measures to eliminate these issues in order to ensure that the process is effective and efficient, and capable of delivering quality data.

Conclusion

In the present business setting, it is increasingly hard to find any business that does not use data in decision making. Every aspect of a company’s decision making process is based on a varied volume of data gathered, analyzed and used to generate important information. For instance, data mining is sued to generate new understanding about consumer trends in relation to specific products, and the factors pushing or limiting these trends. These understanding are then used to target certain products to the segmented units of consumers.

Secondly, data mining is used to target specific promotions to certain consumers by offering them products that are related to the ones they presently use. Outside of the business world, data mining is also used in security agencies for the purpose of detecting trends in crime and where to shift manpower. In essence, data mining is an integral and important aspects of the daily business process. Essentially, understanding the trends and application of data mining is increasingly becoming important in the present world.

In any case, this research does show that data mining is not all about the flares are there numerous issues that need to be considered in the course of performing successful data mining. They include quality, reliability and validity of data being mined. However, under ceteris paribus¸ data mining is an important business process that can be used to deliver products that offer better value to the consumers. 

References

Computer Weekly. (n.d.). Master data management gains ground in UK public sector. Retrieved from Computer Weekly: http://www.computerweekly.com/feature/Master-data-management-gains-ground-in-UK-public-sector

Expert Systems. (2016, 9 15). Text Mining vs Data Mining: Which came first? Retrieved from Expert Systems: http://www.expertsystem.com/text-mining-vs-data-mining-differences/

Junk, D. (2015, 4 15). Business Intelligence vs Analytics vs Big Data vs Data Mining. Retrieved from Aptera Blog: http://blog.apterainc.com/business-intelligence/business-intelligence-vs-analytics-vs-big-data-vs-data-mining

Matillion. (n.d.). 5 real life applications of Data Mining and Business Intelligence. Retrieved from Matillion: https://www.matillion.com/insights/5-real-life-applications-of-data-mining-and-business-intelligence/

Rouse, M. (n.d.). business intelligence (BI). Retrieved from Tech Target: http://searchdatamanagement.techtarget.com/definition/business-intelligence