Data mining

In data mining, you are looking for hidden information but without any idea about what type of information you want to find and what you plan to use it for once you find it. As and when you dig into data and discover interesting information you start thinking how to make use of it to improve business.

Example - A data miner starts digging into call records of a mobile network operator without any specific targets from his boss. Boss probably gives him a quantitative target to find at least 2 new patterns in a month. As he starts digging into the data he finds a pattern that there are less international calls on Tuesday (remember it is an example) compared to all other days. Now he shares this information with management and they come up with a plan to reduce international call rates on Tuesdays and start a campaign. Call rates go high, customers are happy with low call rates, more customers sign up, company makes more money as utilization % has increased.

Watch out for these

• Uncertainty of the results of data mining - You assume that there is some information to be discovered from the data but you don’t know how much information is hidden in the data.

• Uncertainty of the usefulness of the results of data mining - You think that you have found an interesting pattern and share with management but management says they knew this already or they think it cannot be used for business improvements.

• Time lost during the process of data mining - This time and effort could have been useful somewhere else.

• Uncertainty of the task completion - Its difficult to say if the data mining process is completed. You can go through the data today and find nothing but go through it again 1 year later and you probably find some interesting information. So this is more or less a continuous process.