On four misunderstandings of large data

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In recent years, with the rapid development of cloud computing and large data, "Big Data" and "cloud computing" have become the most fashionable words in the present. From the IT industry to the financial sector, to the logistics sector, marketing, and even the medical sector, education ... Almost everyone outside the boundary has quickly formed the mantra of "pro cloud" and "speech must be the data".

But if you do get a "seriously" and ask this question-what is the big data? What is the value of big data? How can I obtain large data value?...... Is that yellow hadoop little elephant? is the xxxbit of a large amount of data? or is it tens other user information? That's probably a lot of people who keep up with big data that might be ambiguous.

So how do you look at Big data? or listen to an expert's point of view. According to the domestic emerging application delivery manufacturers too one star Morning product director Feng Xiaojie said, large data sheet from the literal meaning does not seem to be difficult to understand, can be considered to be a massive level of data, but in this mass-level data exactly what it means, this is in many industries inside and outside the concept of people are still pure in some misunderstanding.

Big Data misunderstanding one: As long as the big is good

When the technical brother in the conference room just said this sentence, secretary mm happened to push the door, slightly leng a blush red to quit.

Nowadays, many people bring up big data, if do not mention a few mouth "day processing data quantity xxgb, upload picture xxgb, concurrent number XXX" "Hadoop cluster has XXXX node, total storage Xxpb" ... Such technical language, are afraid of others feel that they are not professional. But, is really only the data is big, can reach the peak state of big data? Can several people achieve the goal of unification in Oneness?

Feng Xiaojie says, if the data is only big it is not very useful! Just like the meaning of money is how to use turnover, the data is big, but do not use, let it alone Pianan room corner, it is not big data, but a little "black sheep" meaning.

For example, a lot of traditional portal sites, basically in the "sitting in the Golden Hill but no blessing consumption" situation. Hundreds of millions of users a day, but only a simple advertising presentation, not through the analysis of the data to produce more value.

Big Data misunderstanding two: only technology Daniel can understand big data

Although many people can not be separated from the big data, but really ask him how much, some people may say: "I just understand the fur, the real technical level of large data I do not understand, you still ask those technology Daniel go, they really understand." ”

In fact, this view is not entirely right, Feng Xiaojie said. For example, Zhuge Liang is very understanding of the art of war, he knows where to put the array, where to ambush ... However, he did not need to know how Guan Yu was playing broadsword, nor did he need to know whether Zhang Fei's eight-snake spear was stabbed or chopped during the war.

In fact, the application of large data is more a strategic ability, rather than the implementation of the details of the skills, this ability can help decision-makers from the endless data to see the business opportunities to see value, so as to bring higher profits for enterprises. And as decision-makers do not care too much in the technical details level, the big data exactly how technology generation, and how to straighten out the user experience.

Big Data misunderstanding three: It's a company that has big data.

On the gmic, Evernote's CEO, Phil Libin, made it clear that the business model of his product was to charge users and make them willing to pay for the product experience without the big data.

Feng Xiaojie said although the big data is a sweet pastry, but not everyone can digest, or not all have the necessary data, but to measure the status quo of the enterprise, see clear primary and secondary contradictions, or to consider good input-output rate of return, large data is not suitable for all the status quo of enterprises.

For example, for small and medium-sized sites, a blind pursuit of advanced "tall" technical framework, it is a bit of "kill a sledgehammer" meaning. For this kind of website, the first consideration is the business mode and promotion, only wait until the user volume soar, then consider the big things such as technology upgrade.

On the gmic, for example, Evernote's CEO, Phil Libin, made it clear that the business model for their products was to charge users for the product experience, without having to play with big data.

Feng Xiaojie, for example, is like a double choice: a. 1000 users of the day, the framework of the full reference to the United States Amazon never downtime; B. Daily landing users 100,000 people, daily because of high concurrency had to downtime three times. What would you choose?

Big Data misunderstanding four: I want massive data

Since the big data concept fire, many enterprises in the face of problems, always can not help but think "is not my data quantity?" Would it be better if you had a huge amount of data? In fact, this is falling into a misunderstanding.

This goes back to the analogy between big data values and money values. For example, use search engine search "deposit depreciation", so soon can find similar information: "50 years ago million to 13", "10,000 yuan to save a year to compensate 19 yuan" ... Obviously, the money that does not flow is more and more worthless, and the larger the base, the greater the loss that may result.

Money is so, so is big data. Only like Bitcoin players, keep using the data, and with unparalleled enthusiasm to dig the relationship and value behind the data, can be like a snowball, so that the relationship between the data richer and more perfect. Similarly, for the enterprise's large data, only the full use of large data, so that large data flow fully, and continuously realize the value-added effect, then there is a chance to release large data energy.

As a result, Feng Xiaojie points out, for business decision-makers, there must be a sober understanding of the big data, and before the head is ready to spend large sums of money, it has to be clear: "Do I really need big data?" Does big data really work for me?