Diamond mining is the act of digging into large amounts of unrefined ore to discover precious gems or nuggets. Similarly, data mining is the act of digging into large amounts of raw data to discover unique nontrivial useful patterns. Data is cleaned up, and then special tools and techniques can be applied to search for patterns. Diving into clean and nicely organized data from the right perspectives can increase the chances of making the right discoveries. A skilled diamond miner knows what a diamond looks like. Similarly, a skilled data miner should know what kinds of patterns to look for. The patterns are essentially about what hangs together and what is separate. Therefore, knowing the business domain well is very important. It takes knowledge and skill to discover the patterns. It is like finding a needle in a haystack. Sometimes the pattern may be hiding in plain sight. At other times, it may take a lot of work, and looking far and wide, to find surprising useful patterns. Thus, a systematic approach to mining data is necessary to efficiently reveal valuable insights. For instance, the attitude of employees toward their employer may be hypothesized to be determined by a large number of factors, such as level of education, income, tenure in the company, and gender. It may be surprising if the data reveals that the attitudes are determined first and foremost by their age bracket. Such a simple insight could be powerful in designing organizations effectively. The data miner has to be open to any and all possibilities. When used in clever ways, data mining can lead to interesting in sights and be a source of new ideas and initiatives. One can predict the traffic pattern on highways from the movement of cell phone (in the car) locations on the highway. If the locations of cell phones on a highway or roadway are not moving fast enough, it may be a sign of traffic congestion. Telecom companies can thus provide real-time traffic information to the drivers on their cell phones, or on their GPS devices, without the need of any video cameras or traffic reporters. Similarly, organizations can find out an employee’s arrival time at the office by when their cell phone shows up in the parking lot. Observing the record of the swipe of the parking permit card in the company parking garage can inform the organization whether an employee is in the office building or out of the office at any moment in time. (Big Data and Business Analytics by Mark Ferguson) question: Write a short conclusion to the article above.
Diamond mining is the act of digging into large amounts of unrefined ore to discover precious gems or nuggets. Similarly, data mining is the act of digging into large amounts of raw data to discover unique nontrivial useful patterns. Data is cleaned up, and then special tools and techniques can be applied to search for patterns. Diving into clean and nicely organized data from the right perspectives can increase the chances of making the right discoveries.
A skilled diamond miner knows what a diamond looks like. Similarly, a skilled data miner should know what kinds of patterns to look for. The patterns are essentially about what hangs together and what is separate. Therefore, knowing the business domain well is very important. It takes knowledge and skill to discover the patterns. It is like finding a needle in a haystack. Sometimes the pattern may be hiding in plain sight. At other times, it may take a lot of work, and looking far and wide, to find surprising useful patterns. Thus, a systematic approach to mining data is necessary to efficiently reveal valuable insights.
For instance, the attitude of employees toward their employer may be hypothesized to be determined by a large number of factors, such as level of education, income, tenure in the company, and gender. It may be surprising if the data reveals that the attitudes are determined first and foremost by their age bracket. Such a simple insight could be powerful in designing organizations effectively. The data miner has to be open to any and all possibilities.
When used in clever ways, data mining can lead to interesting in sights and be a source of new ideas and initiatives. One can predict the traffic pattern on highways from the movement of cell phone (in the car) locations on the highway. If the locations of cell phones on a highway or roadway are not moving fast enough, it may be a sign of traffic congestion. Telecom companies can thus provide real-time traffic information to the drivers on their cell phones, or on their GPS devices, without the need of any video cameras or traffic reporters.
Similarly, organizations can find out an employee’s arrival time at the office by when their cell phone shows up in the parking lot. Observing the record of the swipe of the parking permit card in the company parking garage can inform the organization whether an employee is in the office building or out of the office at any moment in time.
(Big Data and Business Analytics by Mark Ferguson)
question: Write a short conclusion to the article above.
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