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Importance of Data Mining and Effects of Bad Data in a Business
Bad data is any information that has been collected by an organization but does not provide actionable intelligence. It is a broad spectrum term that shows accuracy, verifiability, usability, and value of information at hand. It can be inaccurate or fraudulent. It can also have some controversial qualities like being old, subjective or just too much. Bad data is often caused by organizations collecting big data where they mainly focus on collecting large volumes of data instead of quality information that can help generate solutions to specific business issues. Lack of clear structures in an organization, dishonesty in an organization and lack of technology are the major causes for bad data. The consequences of relying on information that is not clean are disastrous. Organizations lose a lot of revenue as a result of decisions made from bad data. It takes time before an enterprise can go back on its feet once it suffers the consequences of relying on bad data. The statement that there is no bad data, just bad management is true.
The most common type of bad data is the case of starting with the wrong data set as a result of false records or incorrect information. This is a case of bad input by a human being which if relied upon definitely leads to bad decisions. Poor interpretation is the other most common type of bad data (Turney, 2013). Data interpretation is as a result of human element of intuition and control. It can therefore be concluded that as much as bad data comes from multiple sources, it is always not the data’s fault. From these two examples it can be concluded that the lifecycle of a piece of data has two interesting moments; the moment of creation and the moment of use. Quality, the extent to which the data is useful is determined at the moment of use after the data has been created. Since creation and use of data are purely human elements, human error is solely to be blamed for bad data.
To avoid paying the heavy price of bad data, companies need to put up stringent measures to avoid it in the first place. First, it is important to do the data janitor work of cleaning up data before it can be analyzed. Though tedious, data cleansing exercise removes the human errors cited as the chief reason for data inaccuracy. In addition, business departments need to communicate clearly and share common information, so their data sets are complete. Data centralization is paramount in ensuring its accuracy (Reid, 2014). Finally putting in place efficient data strategies can be a long –lasting solution to ensuring clean data is always acquired. For example minimizing the role of employees by adopting technology can be an acceptable solution to human error.
Data mining is technology driven process of combing through and analyzing massive sets of data and then extracting the meaning of the data. The statistical tools used in data mining forecast behaviors and future trends, assisting businesses make informative decisions. One of the situations where data mining is very useful is in the marketing department. First, it assists in market basket analysis which helps understand which products are commonly bought together (Rajkumar, 2014). The results of the analysis can aid in drafting new marketing campaigns that incorporate co-promotion activities. The patterns obtained from data mining are useful in planning of direct marketing activities. It helps in drafting the list of the specific customers to be reached. Traditional market research can help segment customers but data mining digs deeper and increases market effectiveness. It aids in aligning the customers into a specific segment and helps make tailor made solutions for each customer. Market is always about retaining the customers. Data mining allows segmentation of customers based on vulnerability so that the business could offer them special offers and enhance satisfaction.
Big data however appealing can be the genesis of bad data for an organization if precautionary measures are not taken. Organizations must address data quality head-on, implementing policies, and advancing cultures such that data creators do so correctly the first time (Redman, 2014). The data customers should also communicate their specific needs to data creators in good time and they also have an obligation to provide feedback. Bad data, when relied upon, leads to improper decisions which can result in a permanent scar on the reputation and revenue of the business. Clean data is nevertheless not the miracle switch with all the solutions to business dilemmas. Data itself – no matter how accurate – has little value unless it can drive quicker, more effective decisions. Good data interpretation using the data mining tools is therefore imperative for any business.
References.
Rajkumar P. (2014) 14 Useful Applications of Data Mining. Big Data made Simple. http://bigdata-madesimple.com. Accessed 2/11/17.
Redman T.C (May, 30,2012) Break the Bad Data Habit. Harvard Business Review. https://hbr.org.Accessed 2/11/17.
Reid. A (2014).3 Tips for Protecting your Organization from Bad Data. Pitney Bowes. http://blogs.pitneybowes.co.uk/ Accessed 2/11/17.
Turney D. (2013) How Big Data can Result in Bad Data. The Sydiney Morning Herald. http://www.smh.com.au Accessed 2/11/17
