Correctly understanding four large data myths

It seems that every software manufacturer, every consulting firm, and every thought leader is trying to define "big data" exactly. Although there is no such definition, breaking the myth about big data will help us to understand the big data correctly.

1: You can get all the data

In many ways, we are living in an unprecedented era. We've never been able to get as much data as we do now. The megabytes, bytes and bytes of data that have been overlooked by people have now emerged. In today's industrialized society, the average person consumes more information per day than the amount of information consumed by people who live in the 15th century.

No one or company has been able to store and retrieve all the data on a particular subject, not to mention all the data, including Google. Google indexes only the information in the surface network, not the information in the deep web. Experts estimate that the latter is 25 times times the size of the former. So, as we search, the amount of information we get is only the 4%~6% of the amount of information in the Internet.

2: You need all the data

There is no doubt that the more data you help, the more it does not mean that you need all the data when making a business decision. Companies that are making efficient use of large data have realized that they don't need to get all the relevant information.

A large number of new data sources are emerging almost every day, but not all of the data is valuable. For example, e-mail messages often provide us with valuable insights into the state of the business. Savvy companies are digging up personal information to assess employee sentiment and who might resign. But that's not to say that all emails have the same value. Because it doesn't make sense to analyze junk e-mail. You don't need all the data. The more the data, of course, the better, but please don't waste time trying to do this impossible thing.

3: Big data will give us a clear answer

We often hear the business motto "deal with the data you can handle and get more information from it." "We encounter a lot of problems in making business decisions using the information we have." In fact, we simply cannot use this information to accurately predict the company's mergers and acquisitions, product launches, new ventures, and employee entry.

But this is not to say that there is uncertainty and that big data will not help us. Please do not confuse reduction of uncertainty with elimination of uncertainty. The day that big data can help us to eliminate uncertainty is not here yet, and it may never come. Analyzing massive unstructured data may help companies better understand their customers ' moods. But don't be mistaken for large data to exclude all possibilities for us. The impermanence of life and the ups and downs of our business will destroy the perfect plan we have worked out.

4: Big data is just a flash in the pan

Nate Silver can be said to be a representative of the Big data field, at least before he left the New York Times. In the 2012 presidential election, although many predicted that Mr Obama and Mr. Romney would be a match for the vote, silver, a statistician, predicted that Obama would win the 2012 presidential election with a 90% electoral vote. Because Silver's prediction model is so accurate that many people now come to him for help when they encounter something.

While the definition of large data and data science will remain uncertain for years to come, it is certain that the amount of data consumed in 2013 exceeds the amount of data consumed in the year 2012. Many companies have recognized the importance of big data, and rejecting big data could lead to a company being eliminated from competition.