Data Mining – The Cost Factor
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Data mining offers significant advantages for companies but comes with substantial costs involving software, data management, and skilled personnel. Many organizations struggle with the complexity and expense, leading to gaps between them and more adept competitors like Coca-Cola and Ion Geophysical. While data mining can lead to better decision-making, its benefits are not immediate, requiring time and investment to realize effective outcomes.
Data Mining – The Cost Factor
- 1. Data Mining – The Cost Factor Even though data mining can change the way companies target customers and implement projects, it involves huge investment in new tools and resources. Businesses that cannot invest in these are getting left behind. In a Deloitte survey, 53 percent of top-level executives said that technology enablers and disrupters posed the greatest threat to their business models, though they did not see it as affecting their results. Businesses said that data mining and analytics constituted 44% of the five major threats they face, the others being social media, mobile applications, cyber attacks and computing. Many companies have got ahead by harnessing the benefits of mining big data. A technology-focused seismic solutions company like ION Geophysical Corporation (ION) is currently ahead of many others in the industry. It was only recently that ION began mining its big data to present a more organized image and to understand its potential customers better. This is helping the company to make wiser budgeting and capital-spending decisions. Coca-Cola focuses significantly on data mining and analytics and is planning to use big data to manage and monitor information. Phil Maxwell, Coca Cola’s Director of Enterprise Risk Management, told Deloitte that one of the key activities in risk management is to prioritize where a company should invest its resources. They can better understand their potential risks by harnessing and looking at the right types of data. The big question, he says, is how to sift through the data to find something that’s actually meaningful and relevant for the organization. Costs Involved in Data Mining
- 2. Many organizations consider data mining complex and expensive. The costs involved include: Costs of cleaning the data from different sources Costs of integrating the data Costs of additional preprocessing and altering the integrated data set into a usable data set for data mining Costs of building and running the data mining model Additional costs to implement data mining would include Cost of data mining software Maintenance costs Backups for data Implementing new data collecting strategies Cleaning and transforming the data for appropriate use ── may include document conversion from one format to another Cost of resources── data mining does not work by itself once the software and data is loaded onto a computer. The services of analysts with experience in handling data mining technology would be needed to implement the process Intelligent Culling of Data to Make Wise Investment Decisions Some companies make better choices by making greater use of its data to inform decisions and provide context. Companies such as Facebook, Amazon, Target and Google utilize data mining techniques to create detailed profiles of their customers. They mine and extract valuable information out of status updates, interests, page likes, and other user activities. The extracted information is helping them promote and sell their products and services Other companies collect data from multiple sources which can make the mining process more efficient. ION, for instance, utilizes a customer-relationshipmanagement system, earnings-call transcripts and analyst reports to collect information about its customers and prospects. This helps the company to know about prospective clients even before the interaction begins. Data Mining Takes Time to Bear Fruit The results of investment in data mining cannot be seen immediately as it is a complex long term process. Building a data mining team or implementing data mining software add to the time and expense. Training employees in using new software and hiring consultants to bridge platforms also add to cost. Merging new systems with older, legacy systems is also complicated and expensive. Moreover, if the company does not know how to utilize the data, the whole exercise would be futile. That brings the relevance of professional data mining services to the forefront. An established data mining company would have all the resources to help its clients achieve their big data mining goals efficiently and affordably.