Forget "Big Data", start with "medium data"

For many http://www.aliyun.com/zixun/aggregation/10242.html "> Market researchers, the" medium data "is the real goal of providing ROI value returns. The so-called "Big data" analysis, will show a diminishing ROI.

The industry's skepticism about the concept of "big data" has never stopped, and many people think it is just an overly hyped marketing bubble. Indeed, most companies do not have PB-level data, such as Google or Facebook, in terms of volume of data. So, does big data have any meaning? Data analysis expert Tom Anderson recently gave a concept called "medium data", according to his division, the dataset data volume below 100,000 of the so-called "small data", the dataset in more than 10 million of the so-called "large data", and between the two is called "Medium" data. Tom Anderson believes that the rate of return on investment in data analysis is the highest in the "Medium" data range. Here's a blog from the IT manager network compiling Tom Anderson:

After I took part in this week's first big Data seminar for the American Marketing Association, I became more convinced that I had been communicating with marketers of many Fortune 1000 companies over the years. That is:

Few companies are able to analyze the magnitude of the so-called "big" data, and they don't really need it. In fact, most companies should start thinking about how to start with "medium" data.

Big data, big data, big data, people talking about it everywhere, actually, I found that there are few researchers who really deal with "big" data. I think we should narrow down the concept of "big data". Introduce a new, more meaningful noun: "medium" data to describe our current big data boom.

To understand what "medium" data is, and then to understand big data, we need to know what "small" data is.

"Small Data"

The above diagram simply divides the "big" and "small" of the data according to the size of the data record or the size of the sample.

Small data can include an interview from a qualitative study to the results of thousands of questionnaires. In this scale, qualitative analysis and quantitative analysis can be combined technically. Neither can be called the "big Data" now defined. At present, the definition of large data varies with the level of the enterprise's data processing. The usual large data definition refers to the amount of data that is difficult to analyze with existing generic software.

And this definition is from the point of view of it or software provider. It describes the inability of enterprises to take advantage of existing capabilities and the need for a large number of hardware software upgrades for valuable data analysis.

(Responsible editor: The good of the Legacy)