-01- Data management is a mindset, not about big data or small data, and it can be unrelated to the internet. 71 years ago, during the Liaoshen Campaign, the Battle of Hujiawopeng was originally an ordinary skirmish among countless encounters. But Lin Biao, through the captured lists and prisoners, noticed that there were many small vehicles captured and the prisoners were of high rank, so he concluded that Hujiawopeng was the headquarters of Liao Yaoxiang's Corps. Some say Lin Biao was the ancestor of big data application. In fact, Lin Biao merely had data awareness. Data awareness is more important than the data itself. Without data awareness, no amount of data is useful. -02- Let me tell a widely circulated story in Taiwan: the story of Wang Yongqing, the "Plastics King" of Taiwan, selling rice. Wang Yongqing initially ran a rice shop. While others waited for customers to come, he delivered rice to their homes. When delivering rice, he would ask a few questions. First, how many people were in the family; second, when they got paid; third, he would measure the rice jar. This is data management. It's just small data. Knowing how many people in the family, he could know when the rice would run out. He would deliver rice when it was about to run out but not yet empty. Otherwise, if he delivered too early, they wouldn't want it; if too late, they would have already bought rice. Knowing when they got paid: Taiwan was poor then, and rice delivery might be on credit. When to collect payment? He would collect at the first time after payday. Measuring the rice jar size told him how much rice to deliver at once. If he delivered too much, the jar couldn't hold it; if too little, it wouldn't be full. This is also data awareness. Wang Yongqing became the "God of Management" in Taiwan, and this shows the beginning. -03- Twenty years ago, I found a distributor with data awareness who did very well. This was a county-level liquor distributor in Jiangsu. The boss had grown big and no longer delivered goods personally. When the boss was hands-on, the data was all in his head. This is the practice of most individual business owners. Once employees delivered goods, many employees were not as "smart" as the boss. This distributor's practice was: paste several large white papers on the warehouse wall, with tables drawn, the horizontal axis being the dates of the month, and the vertical axis being the customer list. After delivering goods each day, the salesperson had to write down the customer's product names and quantities. I was curious and asked the boss what use it was. The boss said, by looking at this paper every day, he could know which customer had a problem. For minor issues, he would call; for major ones, he would visit personally. I asked again, why not have the finance department write a daily report? The boss said, looking at one day's data only tells you the sales volume, which is meaningless. It's meaningful only when you look continuously for a period. For example, by looking at several consecutive purchases, you can roughly know the sales progress and thus the approximate time of the next purchase. If the customer doesn't purchase at the expected time, it means the customer is abnormal and has a problem. It's not about looking at one day's data, nor one month's data, but discovering patterns through data. This customer did well, and there was a reason. -04- When I visit agents, I habitually look at two things first. One is the terminals; the other is the warehouse. Looking at terminals is to discover problems. When communicating with agents, if you don't understand the terminals, you might only solve the agent's problems but not the manufacturer's problems. Solving terminal problems solves both the agent's and the manufacturer's problems. Looking at the warehouse, what do I look for? First, the product structure; second, the product dates. Data is not just numbers, but also structure. Lin Biao found that officers and small vehicles accounted for a large proportion in the captured lists; that is structure. From the products, you can see the agent's product structure, which reveals influence and roughly guess the profit situation. Because profit is determined by sales volume, cost, and structure together. Looking at product dates, you can roughly guess the sales volume. Sometimes, agents may not tell you the true sales, but the purchase dates don't lie. This is data awareness. -05- Without data awareness, no amount of data is useful. In the IT era, companies already have too much data; do they care about more data in the DT era? Many manufacturers ask agents to use SaaS systems, but agents are unwilling. Because they find it troublesome and useless. It's not that data is useless, but that people don't know how to use it. Big data, being associated with the internet, confuses many people. Single data cannot show patterns. When data accumulates, patterns emerge. As long as you are good at discovering patterns, many problems are solved. -06- What is data useful for? There are many uses, but the most important is: discovering patterns and predicting the future. When will a consumer buy something online, and what might they buy? Big data can predict, and also push products based on predictions. When will a store stock up, and what will it stock? Big data can predict. Wang Yongqing used small data to predict when to deliver rice, how much to deliver, and when to collect payment. The small case about the agent can predict when stores will stock up and what they will stock. If the purchase time and data are inconsistent with predictions, there is a problem. The power of big data lies in its ability to predict the future. Predicting the future is not about having a magic plan, but about discovering patterns behind the data. Discovering patterns and predicting the future enables turning passivity into initiative. Since big data can predict the future, when the future has arrived and predictions are inconsistent with reality, problems can be discovered. For example, in the case of the liquor distributor above, when a customer's purchase volume in a certain period is inconsistent with predictions, the customer may have a problem. Source: Teacher Liu's New Marketing Once the tip is adopted, a reward of 400-2000 yuan will be paid.