What is data mining and what exactly do organizations hope to gain from mining their data? Is data mining about efficient use of technology, or making business decisions which add a lot of value? Collecting data is only the first step. It is of no use to anyone if they collect the data and dump it in an obscure corner and never look at it. Data could have been buried for so long that no one currently making decisions in the organization has any clue to its existence. On the other hand, how many companies intuitively know how to mine their raw data and derive insights which automatically make sense? How many expend a lot of energy on doing so and get disheartened and decide to drop it as a waste of time, in a huff, not to bother with it anymore? If they do choose to press ahead with their efforts, will they definitely reap the benefits touted? Is there any legal necessity which binds their actions as they engage in data mining? So many questions and very few answers! Let us see if we can throw any light on at least some of them.
Data mining provides insights by identifying anomalies, patterns and correlations between different (very large) data sets and helps to predict possible outcomes through the correlation. Done right, it could provide insights which reduce costs, improve revenues, control attrition, reduce risks, enable business decisions across multiple locations and improve regulatory compliance.
Some critical steps in data mining:
Before initiating the necessary efforts to make your business data-driven, do ensure that some critical initiative assessment is undertaken to decide what your needs will be:
Now is the time to strike a note of caution! These preliminary strategy meetings must also recognize the benefits and risks which come with data mining and data driven decision making. It all depends on where you plan to use big data. Is it to hire and manage your workforces, or is it in marketing, or in fraud prevention? Are you sure of the quality of data, in terms of its representativeness, accuracy, robustness, reliability and completeness?
At all times, employers need to ensure that their data set is representative and that their data model eliminates possible biases as well as ethical and fairness concerns. They need to also stay alert to the accuracy (or lack thereof) of any predictions made by their big data mining efforts and revise them continually until they prove reliable.
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