Apart from security and privacy problems, cost is a major factor determining decisions related to data mining.

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.