What level of data operation needs several levels

More and more companies, individuals have the importance of the data , more than ever before momentum. The concept of big data is everywhere, as if everyone is talking about big data, met without greetings of big data, as if it was not in the data world. So, the data passed by the outside world so magical, in the end what can promote the development of business? Or, business data on what level of demand? Where can the data to help business?

Combined with years of work experience and extensive understanding of data and business, the business needs for data are summarized in four levels.

The first floor: know it

We can "know the difference" by establishing a data monitoring system to grasp what happened and to what extent.

Specifically, the cut-in point of the data mainly in these areas. The first is the "concept of day" to observe the overall trend of the industry, policy and environmental impact; then "know the land" to understand the performance of competitors; finally "introspection", how to do their own, how their data performance. In view of the cycle of the data, "View day" can be quarterly or longer period; "知 地" by week or month, special time points, except for special events; "Self-examination" data obtained Is the most comprehensive, need to look every day, specifically someone, someone research.

At this level, share two perspectives on data:

1 data is scattered, look at the data need to have a framework.

How to look at the data is very particular about. Pieces of data is difficult to play a real value, the data into an effective framework, in order to play the overall value. The so-called effective framework contains at least two roles:

(1) A lot of data, different people have different data needs, such as CEO, middle managers, the underlying data is usually not the same attention, the effective framework for different people to get what they need.

(2) An effective framework can quickly locate the problem. For example, we all care about the trading volume indicator. If the trading volume indicator drops by 20% on a certain day, then the business is very likely to be a problem, but where is the problem? If only a few highly abstract indicators , Such as conversion rate, the number of transactions, the customer price, is not positioning the problem. A good framework to support us to drill down, find the problem from the category, flow channels, etc., the board will hit the person in charge of the specific body. This is what we usually said, to see the data to be landed.

2. Data, there is a comparison of the truth.

I have 120 pounds, you say it is heavy or light? A lonely data is difficult to explain the problem. To determine the speed of an indicator of growth, you need to select the correct comparison of objects, the reference system, which is the baseline. This baseline can be a pre-set target, either on average with the industry or over the same period of history.

The second floor: I know why

See the problem through the data, go to this step is not enough. Data is only an image, is used to find and describe the problem, practical operation to solve the problem is more important. Data combined with business, to find the true reason behind the data representation, to solve it. The process of solving the problem will involve data, data processing, may also involve methods or tools such as data model, there is a high technical content, another space for introduction, here is not started.

In the second layer there are two points to share:

1. The data is objective, but the interpretation of the data may be very subjective.

The data itself is objective, but consumption data is subjectively motivated. We tend to bring subjective factors into our interpretation of the data: the same data may seem to be a good conclusion in A's opinion, but B may yield the opposite result. Not to say that such a situation is not good, the truth clearer and clearer. However, it is not advisable to find a problem through data rather than qualitatively and then selectively use the data to prove its point of view. In fact, we often happen around such things.

Understand business can really understand the data.

Vehicle goods teacher's blog, "Do not understand the business do not talk about the data," made a profound exposition of this point of view, do not start here. Just because of the importance of this point of view, I specifically come up to do some emphasis.

The third floor: find opportunities

Using data can help your business discover opportunities. For example: Taobao middle-aged clothing segment, there are large size women's market, these markets can be perceived by the surrounding environment, we know there are some middle-aged or older fat MM Taobao did not meet the needs of the above . So there are no other channels to find more market segments?

Data can be!

By comparing the keywords searched by the user with the data of the actual transaction, it is found that many demands have not been satisfactorily satisfied, reflecting strong demand but insufficient supply. If you find such a market segment, published to the industry second, published to the seller, is it right? Can we help to better serve consumers? This is what we are doing now "potential market segments found" project.

Speaking of this case, not to brag how good the data, but to tell everyone: the data is there, some people turn a blind eye, but some people can dig out the "baby" to come. What is the difference? Business sense. Just mentioned the search data, the transaction data, many people can see, but no one before the two data together look, which reflects the business sense behind.

The fourth floor: the establishment of data operations system

My understanding of data operations involves two meanings: data as indirect productivity and direct productivity.

1. Data as indirect productivity.

The so-called indirect productivity refers to data workers through the operation of the value of the data passed to consumers, that is, the so-called decision support, data workers output reports, analysis reports for business decision makers at all levels for reference. I call it Decision Support 1.0 mode. However, as business development and business people 's understanding of the importance of data increases, the demand for data will pop up. It is clear that it is clear that a small number of analysts can not simply rely on it. Teaching people is better than teaching people to fish, so operations, product classmates are able to carry out data analysis, is my mind decision support 2.0 model.

Decision Support 2.0 model has three key words: product, ability, willingness.

Let operations and PD master SQL such take the number of languages, master SAS, SPSS such analysis, it seems not realistic and necessary. Providing a low-threshold, user-experienceable data product is the foundation for implementing the Decision Support 2.0 model. Products mentioned here, not only operating set of functions, but also need to carry analytical thinking and the actual case.

However, the threshold of data analysis is always there. This poses new basic capabilities requirements for operations and PDs, namely basic mathematical ability, logical thinking ability and learning ability.

The last will, perhaps the most crucial, is possible only when there is a strong inner drive to do a good job of this matter.

2. Data as direct productivity.

The so-called direct productivity refers to the role of data workers in the value of the data directly through the front-end products on the consumer. Trendy point, called data realized. With the advent of the Big Data era, corporate management is paying increasing attention to this. Big data era has brought great opportunities, but it may also be a catastrophe. If you can not use the data to generate value, then it is a disaster - the more data you generate, the more space you have to store and the more resources you waste.

One application that is now better understood is related recommendation, after you buy a product, recommend one that you are most likely to buy. Personalization is the new wave of data as direct productivity, and this wave is getting closer and closer. Data workers, ready to meet it.