Do not book as much as the Quweicunzhen data analysis to be

The real value of large data quweicunzhen

The big data industry development speed is astonishing, the big data analysis has brought the huge value for the enterprise, has become the enterprise decision new assistant. But there is an old saying in China, "no book as much as the letter", in fact, large data analysis is not as perfect as you think. This is mainly because the large data is not all the data on the value of users, some spam even the value of the data to bring deep harm, how to filter the data collected, Quweicunzhen, is the key to the real value of large data.

What is junk information?

What is spam, in short, is the useless information that is mixed in a lot of useful information, harmful information, and information that affects the results of large data analysis.

But spam is not absolute, it may be useless to user A, it is harmful to the results of no analysis, but for User B, may be useful information. Therefore, users of the same industry should learn to distinguish their own data in the information which is junk information, is useless.

Common Junk information:

In our daily life, all kinds of rubbish information can be said to be everywhere. For example, the network now has a large number of network navy, these people produced a lot of junk information, the results of large data analysis has a bad impact. In the last release of the "X feast", the propaganda party hired a large number of the navy to brush points, resulting in a high score, but after the view of the bad reputation, finally the organizers have to apologize to calm down. The data of these network navy is rubbish information.

Network Navy (photo from Xinmin)

Of course, a lot of similar examples, Taobao opened a few years ago, there are special for sellers brush drilling tools, such a result of many stores although the level of drilling is very high, but products and after-sales service is not. Weibo user brush powder is prevalent for a while, a lot of network Big V Real fans are few, forum brush reply Paste, create a forum very fire situation; The electric Commerce website Promotion Period brush trade singular. And so on, these spam messages are deeply damaging to the value of the data.

Large data Market Current situation:

There is no denying that large data has great value, but in the present situation, large data is more like the moon in the water in the mirror, it seems beautiful, but it is exaggerated. Discovering data that is valuable to users in a myriad of data centers is like looking for gems in the desert, like picking up treasures in a dump.

So how do you get valuable information from a large number of data centers? Let's take a look at how people are getting rid of junk information and looking for data value today.

How to remove junk information from data

Eliminate the role of spam information in the data?

Why do you want to get rid of this junk? One of the things that we have mentioned earlier in this paper is that the rubbish information affects the results of our data analysis and makes the data value difficult to embody, which is just a hazard of spam. At the same time, too much rubbish information will cause the bottleneck of the customer infrastructure, bring the burden to the system, and add the cost of storage, host and other equipment, and greatly increase the cost of operation and maintenance of enterprise users. So how do you get rid of all this junk information?

Enterprise Storage Architecture

How do I get rid of junk information in my data?

The important difference between large data and traditional data is the appearance of unstructured data, which makes it less useful to have traditional rules and parameters that eliminate junk information. In the large data age, new methods of eliminating spam information are needed. But now that big data is being developed, major IT vendors are racing to launch big data solutions, but there are few ways to eliminate spam.

The author thinks that it is advisable to start with the following two aspects:

Manpower: Today's lack of large data analysis can only, so many solutions can not really achieve intelligent analysis, and the artificial will bear the intelligence part. Some data analysis issues are sent to the Commissioners responsible for the problem, and the relevant large data analysis professionals provide solutions to the problem.

Lack of large data talent

But today, there is a dearth of professionals for big data analysis. According to the McKinsey survey, by 2018, the U.S. market will be nearly 200,000 in-depth data analysis of professionals, 1.5 million can be data interpretation of the job gap of professional managers. Large data professionals not only need many years of mathematical knowledge accumulation, but also need to have programming, business knowledge and other comprehensive capabilities, is a scarce compound talents, and for the employment of enterprises, it is difficult to have the right position suitable for such talents.

It vendors: In addition to increasing the strength in manpower, training professional personnel. More large data vendors are needed to provide smarter solutions, and manpower is clearly not enough.

Faced with such a large pile of rubbish in the future, this will be an important challenge for big data makers. Manufacturers need to establish new data standards, to help users more in-depth analysis of data, the ability to intelligently identify the level of data, automatic elimination of duplicate, the same IP address or malicious interference data, which will greatly accelerate the speed of data analysis. From scratch, sometimes may be a little bit, and then need a little time, gradually accumulated, this is a large data manufacturers long-term test.

So is it all right to have big data?

Data quweicunzhen is to enhance the effectiveness of data, but also pay attention to the timeliness of data, the timeliness of data also determines the results of large data analysis. We need to be based on different needs to confirm the timeliness of the data, outdated things we do not even analyze the impact of our decisions.

Large data analysis needs attention to timeliness

For example, for the investment industry, this timeliness is critical, the investment industry needs to be based on market data rapid analysis of the results, the faster the analysis results, the user benefits may be greater, the contrary may even cause the loss of funds.

People-oriented large data should be supplemented primarily

Large data analysis can provide us with the most valuable information, can help us to release the most beneficial to the company's development. But it is not feasible for users to rely entirely on large data analysis. After all, we analyze the data is already happening, large data analysis results can only provide us with reference, but in the former change million flower market, also need to have a shrewd decision-makers, rely on large data, final, determine the development of enterprises.

Large data analysis is not God, it can only provide us with a reference, help me analyze the past occurrence of the laws of our future forecasts, but today's large data analysis is still not intelligent, many factors large data analysis can not be taken into account. For example, when Nokia and Motorola dominated the handset market, the information we collected was all about the two brands, but who could predict the speed of the rise of Android phones and iphones.

To sum up, in the enterprise, large data in the enterprise decision-making is more to play a supporting role, rather than the final decision makers. The data in large data is all-encompassing, and is not the most advantageous result of simple data analysis. In the fierce market competition, large data analysis will become more and more important, but it is still a human helper.