The data is equal to the raw material

The volume of electricity quotient is doubling every year, but does the knowledge of the accumulated data increase by one times? If the electricity dealers do not start collecting data and applications, they will find that the data used in the future are useless. Many people realize that the future of the electricity business is the battle of the data, so in the context of such large data, where does the dealer collect the data? How to use data quickly to make the right business decisions? The problem has plagued many electric companies.

The more data, the better?

Later in the United States encountered Patil, he thought the past data collection is very difficult, and now access to data resources become easier. But if the starting point for collecting data is not to solve the problem, what does it mean to collect more data?

However, many companies still have a question is that it is not difficult to collect data, the cost is not high, why not collect data first? When you need data to solve the problem, you can use it again. Patil's answer I also very agree, he advised us not to think so, with such a concept to design data applications will certainly fail. The data is not marginal, I have been suffering for a long time. such as collecting a person's birthday, can be accurate to a few seconds, but such a precise data what application, can produce what value?

In fact, the data is life-cycle, such as the Chinese ID number can be inferred from the gender, but in a few years if this rule changes, resulting in our data based on assumptions and decision based on the loss of meaning (data broken). What's more, it's not easy to save the data and the context in which it was collected. So, while collecting data, we have to know what the future can be used for, and we can't think of it today.

For example, today many electric business owners will ask what the repeat purchase rate is, so we collect data to calculate the repeat purchase rate, but rarely think of the need to repeat the purchase rate to decide what to do. It's like Kezhouqiujian's story, he tells us that things are changing and we can't just be mechanical methods or indicators. Just as there is a different definition of repeat purchase rate, different definition of duplicate purchase rate is required to make various decisions. If you look at the repeat purchase rate from an investment company, it wants to buy a company, and it will look at the whole company's health or user's quality from a repeat purchase rate. If the repeat purchase rate is viewed from the point of operation of Company A, then it is more concerned about the day, week level of repeat purchase rate trends, or the month of new customers how many people in three months after the repeat purchase, so that each month to measure the new and stock customer loyalty and quality, to find the room for improvement. Is it more reliable to choose what data to use after you know the background?

Data applications are small and beautiful

From the end of 2011, I began to think about how to change from "Using data" to "feed data" (that is, data operations turned into operational data), and this time I was particularly bothered by what data to collect (more data, much problem). Also, I once wanted to do a particularly large number of data applications for most people to use (false up), but later found that this in the initial stage of data application is almost impossible, one is to find a solution to most of the needs of the data application is not easy, the second is to pay the treasure of the data is very rich, there are many factors to consider, The connection between factors is complex.

So, I conclude, as data application, the data is equal to the raw material, when the raw material has been changing, the product is very easy to go wrong. After understanding the relationship between data and application, I finally decided to start small and apply it (a good sight).

"Small" here refers to the application of the goal is very specific. For example, for a data application, if my goal is to distinguish between two decisions who are better, where the difference is, is a very specific problem. But if my goal is to know how to make a company profitable, it's a vague goal.

Also note that "small" does not mean the amount of data. Many people are enjoying their ignorance when they do not get enough data and have no understanding of the data.

After some setbacks, but also in accordance with the idea of small angle design data applications, small angle into the design products can be specific and fast, but also to avoid the changes in raw materials caused by the problem.

Put the data in the box

In addition, we have to mention a topic, in the context of large data, must consider the relationship between the data. A single data is meaningless, and you can see the problem by putting the data in a "data frame".

In order to make the problem clear, here I take the former son of a home appliance business Company to discuss the problem for example. It's not easy to make public the name of the company.

A ask me, do you want to remove the ads to navigate the website? Because many of the old customers are suspected to visit the website from the navigation site, rather than direct access to the official website.

To put this question more bluntly, is to understand the navigation in advertising and a company's business relationship.

So, what's the next "data frame" to use to help make decisions?

The current input-output ratio of a company

1, the introduction of a clear navigation site to the new and old users accounted for?

2, the introduction of new and old users input-output ratio and conversion rate?

3, infer the removal of the navigation site, the loss of new and old users of the impact?

Second, the game with competitors

One problem that may be overlooked is that if you don't advertise your navigation site, your opponents will come in immediately. When doing the data frame, pay special attention to the framework is not static, but game, need to put the competitor factor in.

Iii. Consideration of time factors

Consider the time factor when creating a box:

1, with the present, the past and the future vision to look at the navigation site, to see the quality of navigation is not getting better

2, attention is the time delay, the introduction of traffic will have some delay, after two or three months to know the value of new users (Life time value).

In short, the "Data frame" is the soul of the business analyst, looking for the key factors and answers to the problem from the box. Different questions have different boxes that cannot be fully elaborated here.

How to make a decision with a framework?

In this, I summed up the four steps to go:

First, determine what the problem is, and collect data from the perspective of problem solving;

Second, organize the collected data into a "data framework" (a framework that helps decision makers). Let policymakers see the relationship between data and decision-making in a more explicit frame, such as a company's knowledge of competition, new and old customer ratios, and the relationships of many factors.

Third, look at the relationship between the framework and decision-making, such as a company and the navigation site has three options, completely do not cooperate, part of cooperation, all-round cooperation. Tell Company a how to make decisions based on the data frame. If you find that the data frame does not match the decision, you must return to step two.

Finally, action is taken according to the decision, and whether the action achieves the goal. If the action is found not to achieve the goal, we must review the whole chain to see where the problem is. Is there a problem with the data or is it because the frame is wrong or is the decision wrong?

So, back to the topic I always said before, do not understand business do not talk about data. The more complex the problems you want to solve, the more complex the framework is. And for the majority of the current companies have not started to do data applications, the first framework should not be too complex, must be a solution to a problem to start the framework, so that the relationship between the framework and decision-making is very clear. What your problem is, what your decison is, and in turn your frame. From a small angle, from the "little" start.