Data and large data which is more practical

The author of this article and the island Hochang, he thought that although the concept of large data is very hot, but for many entities and traditional industries, how to make their business data more practical, more reliable.

Data is a good thing, it can reflect the user's past behavior trajectory, but also can predict the user's future behavior tendencies. With the increasing richness and diversification of data analysis tools and data mining channels, the data stock becomes more and more important. This directly spawned the concept of large data hot and popular, but for many entities and traditional industries, large data still appear unfamiliar, even do not know how to use large data.

I have also been thinking about how real enterprises and traditional industries to meet and based on the era of large data. In contact with some business owners, financial analysts including data management professionals, I think: at the current stage, rather than the big data, as a solid to complete the business of their own data (this is like E-commerce, the core or business electronic).

First, the enterprise ultimately wants the user, not the big data. Large data just for the enterprise better adhere to users, open up the market provides a decision support.

Second, the mass of data itself is not much value, it is a user of the existing behavior of quantification and accumulation. We take the data from a certain period of time to estimate a new period of user behavior, which is itself to be examined (of course, it cannot be said that he is completely worthless, this is like experience is important, but experience is often unreliable).

Third, more important than the existing large data, is to find out how the data are generated from which areas, what will affect the business behavior, and then pour out the data and enterprise Management (employee management), the intrinsic logic of user behavior.

Four, the data will not be accomplished overnight. It involves both the quantitative processing of the current business indicators, but also the quantitative assessment of the employees, it must be detailed and specific, and use data to speak, with data decision-making, is also a need to cultivate decision-making thinking.

Five, in the short term, large data can not be covered in all industries and enterprises, cloud computing and other concepts for most enterprises are still far away, or to focus on the enterprise's own business model.

So how do companies proceed with data? I mainly think of the following points.

First, business performance data.

This is best understood and most easily overlooked. With the improvement of the financial statement system of domestic enterprises, how to keep the Non-financial personnel (especially the middle-level managers) to maintain a data sensitivity to the overall operating performance of the enterprise, and to adjust the strategy according to the change of operating performance in different periods, becomes a new requirement.

As far as I know, at present, not a small number of enterprises in the financial statements are still a mere formality, management relies on the decision maker's own experience, which will inevitably increase the risk of business operations. Therefore, I suggest that the business owner should first pay attention to the overall operating performance of the data. Sub-departmental, phased (such as the Practice of the quarterly bulletin, can be turned into the internal monthly report), the focus on the overall operation of the enterprise data disclosure for decision makers to refer to, and foster the data awareness of decision-makers. and through more reasonable and convenient channels for business managers to understand real-time business performance and so on.

Especially with the further deepening of internationalization, the business performance will be affected by more external factors, how to establish an effective external data observation model, will become an important means to reduce business risk.

Second, the business model of data.

At present, many enterprises do not want to use large data, but their own business model is difficult to produce effective data, this is the enterprise's own business model of the data to put forward requirements.

In traditional retailing, although there are some member management, there is limited information to be collected. For example, when the customer came in the store at any time, when they browsed the product, what it felt like, even the expression, and so on, the data was difficult to collect in the traditional offline way, and it was difficult to compare with the information he visited every time. It's hard to quickly find the right thing to recommend to him the next time he comes in. There are a lot of retail businesses, and they have a lot of good ways to do it. So everyone in the store knows that the camera device is very common, by using these devices, the acquisition of images, the identification and analysis of the image, the facial expressions, the records and analysis of the products, the collection of information can help the staff in the store to analyze the whole composition of the customer. I found that a lot of physical stores in this area have a lot of ideas, I hope there is such an IT company to help him achieve such an IT support solution.

At the same time, physical retailing is undergoing a transformation--physical stores and online interactions--and physical stores simply become a showcase, propaganda platform, its actual sales behavior is a lot of online, also no longer need so many physical stores, and then can open the physical store in some more convenient for customers to reach the place. Customers in the physical store to see things, want to buy when you can buy online, but also door-to-door.

At present, similar to the retail industry this has a large number of users of the business model, will inevitably complete to the data.

Third, the user behavior data.

I think this point is the most important, but also the most widely used in the current period. As the previous article says, the enterprise ultimately wants the user. Then the scientific analysis of user behavior, nature is to understand the user, close to the user most effective way. However, compared to E-commerce and other online enterprises, the traditional industry and the entity Enterprise user behavior data is not optimistic, the operability is also relatively poor. Many enterprises ' user behavior analysis is a formality, and can not provide real help for marketing and product improvement. I give an example of a travel company.

A lot of people know that Zhong Kun group (Enterprise of Huang Nu Wave), do tourism real estate, one of them is Beijing's Mentougou project, China Kun cooperates with Mentougou District government, put the whole Mentougou's scenic spot as data, there are many places to put on the camera head. Because it is a tourist attraction, we pay more attention to the local customs, history, and even which little Hill has had some stories in history. The previous practice was to train a group of tour guides, and then to introduce visitors, so that tourists in the process to understand something, causing some thinking.

Later he found that the model was old-fashioned, and many tourists now want to travel by themselves, which is that I look for them on the mountain, so that these stories don't have a proper way to offer to tourists. So he just digitized all the sights, all the murals and all the landscapes, historical allusions can be done wirelessly-by installing a simple software on a visitor's smartphone, where visitors go, selectively viewing what's going on at the time, and tourists driving around the tourist attractions In different places, you can take pictures of tourists through the camera.

In this way, tourists and attractions of the stickiness stronger, the relationship pulled closer. Visitors to other friends to introduce these scenic spots, there are many stories can be said, slowly found this way, for visitors, is an approximate viral transmission, better than the general marketing effect, which is an example of the transformation of the tourism industry.

The data of employee management.

Any company's staff management, are concentrated in two aspects, one is how to promote staff growth, it can be said to be more understanding of the company's business model, and thus improve performance. Second, how to evaluate the growth of employees, that is, assessment. I think both of these can be achieved through a database that includes a variety of assessment and incentive elements. This is just a good point, I believe that our human resources management experts, can completely according to their own work needs, complete the data.

Of course, another key to the data is how to effectively integrate with the Internet and IoT. Because so far, the mobile Internet provides us with the best opportunity to stick with the user and fully tap the user's data. Whether mobile, social or localized, the release of a huge amount of data, there is a significant mining space.

At the same time, I would like to emphasize that large data is more to the judgment of existing behavior, the curing of known experience, in the prediction, the application of large data should be very cautious.

The above is the core point that I want to elaborate today, in 3-5 years, for the entity enterprise and the traditional industry, the data is more reliable and more urgent than the big data. The examples cited in the article are not necessarily appropriate, for reference only, but also welcome to add more.

(Responsible editor: The good of the Legacy)