In the past two years, despite the popularity of Netflix's American drama House of Cards based on behavioral analysis, big data has also become an important school of modern enterprise management. Whether it is consumption, finance, telecommunications, transportation, or even politics and charity, you can definitely see big data in all seminars. It seems that all the marketing and management problems of human organizations in history can be solved by data.
Of course it's not that simple. Like any new technology, big data is not everything. To make good use of it, we must start with the correct concept. Today, I want to talk to you about six misunderstandings about big data that I often hear.
1. Big data is a new gadget in the new era. In fact, data analysis is nothing new. As early as hundreds of years ago in the Enlightenment, scholars have begun to follow the scientific method and dismantle the reasons behind the formation of things step by step. Scientists first observe, obtain and analyze data, and then draw a hypothesis, and then gradually form a law through constant argumentation. So the big data we are talking about is at best the application of scientific methods. Compared with scientists in the past, modern big data relies more on machines to observe and obtain data in order to collect data more comprehensively and in real time. However, the subsequent inference and induction work still needs human judgment.
2. Exceeding 100 TB is the scale of big data. Actually, there is no clear boundary. More importantly, the size of the data is not necessarily meaningful. A large amount of data doesn't necessarily mean that you can make an accurate prediction-suppose you have all kinds of data on the name, gender, birthday, height, weight, skin color, vision and their online behavior of 7 billion people on the earth. If the topic is to predict their income distribution next year, I'm afraid this huge database can't help you. So there is not much data, and the focus is on the tasks to be completed, not the quantity stored.
3. The data is objective. The software and hardware of data collection are artificially designed, so it is impossible to be absolutely objective. Does the mobile phone stay in a certain picture, which means that you are enjoying the content? It's hard to say. Maybe you're just chatting with friends next to you. Like a post, does it mean that you really like this information? I don't know, maybe it's just someone who likes to post it, or someone slipped and accidentally pressed it. There are always uncertain links in the real world, so it is difficult for designers of data acquisition software to record users' behaviors absolutely and objectively, so it is difficult to produce completely objective data. For big data, what you should have is quite and relatively objective, but it can't be absolutely accurate.
4. Data can tell you things you don't know, just as it literally appears. Data can only tell you what you don't know. But what kind of insider it represents has to be interpreted by the inducer himself. For example, after analyzing your App user data, it is found that women aged 2 1-30 account for the largest proportion, which may mean that your App is the most attractive to this group of people, but it may also mean that the promotion team is targeting this group of people more when advertising. What is the truth? It often needs further comprehensive comparison and experimental analysis to approach.
5. Big data is a problem in the information sector. The collection and storage of big data can indeed be classified as the business of the information department. However, it is absolutely the responsibility of the business leadership department to clarify what to collect, how to collect it, and how to use it after collection. Asking the IT department to do big data well is just like asking the financial department to improve the company's profits, which is putting the cart before the horse.
6. Big data will change everything, and people who don't understand data will be eliminated. The focus of data is not data, but interpretation and prediction, that is, using data to verify human behavior patterns, so as to improve the design of products and services and communicate with potential and existing customers. Therefore, understanding data is not the key, but understanding talents. In the all-round networked world, data will become more and more rampant, and people who know data collection and management will become more and more common. But no matter how technology develops, people who know people will always be in the minority. People are emotional and easily influenced by the environment, so it is difficult to predict.
Therefore, big data is an important progress in social sciences, but if enterprises want to accurately grasp the future, managers should have better determination, or they should be based on the understanding and differences of different people, not just the use of scientific and technological tools. Big data is not everything, it is just a turbo accelerator. As for the steering wheel, you still have it.
These are the six misunderstandings about big data that Bian Xiao shared for you. For more information, you can pay attention to the global ivy and share more dry goods.