Where are the opportunities for SME change in the Big Data era?

Yesterday Xiaobian listened to a period of "brainstorming" television program recording process, the theme of this show is brought about by the big data era of entrepreneurial opportunities for change, or quite interesting, and the recording process than the broadcast The effect is much more fun. Here Xiaobian share some information with you (some do not remember Kazakhstan, we will know it):

Attendees are:

Entrepreneurship Square: Do not remember the full name, just remember that they founded the company and the product is very interesting, is "king of food, today's headlines, aunt," and one did not remember ("Aunt" speech ignited Audience interaction orgasm, the host is a laughing stock)

Old business representatives: Jun Tang, Kuang Zhiping (Qiming Venture Partners, angel investors), there is a real estate developer, far away from seeing the table card

Two observers: one is Kong Fan Ren (marketing); one is what Lee Feng investment.

Topics for the day include:

1, these innovative enterprises are not big data era specialty?

Tang Jun that they are not, because "big data from the disorderly data to find an order, there must be enough data," and these companies do not model the era of big data can actually do, but Method will be stupid, so can not reach "innovation and change"

Kuang Zhiping that the first of these companies are "data-oriented enterprises," and "big data era does not have to have enough data, just have enough data correct and useful," so SMEs must ensure that your data Produce value

Other guests also mentioned that the complete big data model includes "Data Acquisition - Storage - Analysis - Screening - Forecast". The latter three are the most important. Many small and medium-sized enterprises just lack the ability to analyze and predict. Can not make the data produce value.

To sum up: "How to use the data" is generally believed that the current business where inadequate.

2, SMEs in the era of big data innovation, the biggest challenge in where?

The basic selection of the old enterprise square "access to data", they think the real big data in the hands of some core businesses and departments, such as the State Grid, Civil Aviation Administration, Internet Information Center, these data is huge to real Produce value; SMEs have access to the data fall into the trap of missing.

There are two ways to make up for this deficiency. One is to hug the thigh and work with agencies that have big data, such as "very accurate" and CAAC, but this is not a long-term plan because the thigh can be dumped at any time, for example later Civil Aviation Administration to do their own "flight butler" is more accurate than the standard; the second is to find unique data, those large institutions did not expect or do not want to touch the data. The honored guests are very much in favor of and optimistic about the data uniqueness and potential value of "Auntie". Everyone who is interested may wish to search and search, which is a bit complicated.

Kuang Zhiping mentioned from the application point of view, in the application of data collection, data rolling;

3, the opportunities for change brought by big data is more suitable for large enterprises or small and medium enterprises?

Tang Jun is still "big business flour", he believes that historical data has not been well tapped, this is only a large financial and technology companies such as EMC, IBM, SAP, ORACLE can do.

Some guests think that big companies make use of big data to make adjustments based on their own businesses. For example, in the past when EMC did storage and the big data era was still based on storage, the change was only a slight change (Xiaobian Note: Big Data from Storage From the beginning, EMC has already done a good job of analyzing big data.) But for innovative businesses, it is more likely to create truly disruptive changes.

4, under the era of big data, innovative business operating mode where?

To put it bluntly, how businesses make money, it seems that no one can say a conclusion, including the first point mentioned, without a strong ability to analyze and predict can not find a sustainable profit model.