Recently, Nanjing University of Science and Technology went on a hot search. The reason is that Nanjing University of Science and Technology has a large proportion of poor students, but many poor students are unwilling to apply for poverty grants because of face reasons. So Nanjing University of Science and Technology uses big data analysis to quietly list students from 420 yuan who eat more than 60 meals a month and have insufficient total consumption as recipients.

These students eat two meals at school every day, but each meal costs no more than 7 yuan, which shows that the student is really in financial difficulties. These students don't have to go through the process of examination and publicity, and the school directly pays the subsidy to their meal cards. Big data allows Nanjing University of Science and Technology to show kindness in the context of human nature in a quiet way.

We inevitably live in a data world. Social development is inseparable from data, enterprise development is inseparable from data, and personal work and life are inseparable from data. Data is everywhere, data applications can be seen everywhere, and big data knows ourselves even better than ourselves.

Therefore, the way of solving problems with data thinking like Nanjing University of Science and Technology will appear more and more in our lives. Jordan Morrow, the "father of data cognitive literacy", wrote Data Thinking, telling us that data thinking should be "a data cognitive skill that everyone must have".

Jordan Moreau is the head of data, design and management skills at PLURALSIGHT and a global pioneer in data literacy. He believes that data thinking can help enterprises and individuals improve their competitiveness and promote the development of corporate culture and personal ability.

In the book Data Thinking, he expounded data thinking from three parts: the importance of data, data cognitive literacy and data processing skills. Among them, "data cognitive literacy" is the core of data thinking, so how can we have "data cognitive literacy"?

Literacy refers to a person's literacy, which we need to acquire through continuous learning. Data cognitive literacy is not human instinct, but it can be compensated and improved through education, study and training.

In data thinking, Jordan Morrow gave the concept of "data cognitive literacy": the ability to read data, work in data language, analyze data and communicate with data.

In this concept, there are four characteristics of "data cognitive literacy". Knowing these four characteristics, we will know what "data cognitive literacy" is.

Property 1: read data. Is to view and understand the data presented to us.

Feature 2: Processing data. Refers to the use of data to do something in an organization to achieve a certain result or purpose.

Feature 3: Analyze data. Data analysis can provide us with a way to identify and filter the massive data and information we face in our lives.

Feature 4: data communication. It means sharing or exchanging information, information or ideas.

Nanjing University of Science and Technology identifies and screens poor students through the data that each meal does not exceed 7 yuan, and takes this as the basis for funding, which is the full application of the four characteristics of data cognitive literacy.

Data cognitive literacy is not the change of personal ability, talent or vocational skills, but the improvement of personal ability in data. So how can we improve data cognitive literacy?

Jordan Moreau gave the answer in Data Thinking. This is the 3C of data cognitive literacy: curiosity, creativity and critical thinking.

The first C: Curiosity. If you are an educational administrator of Nanjing University of Science and Technology, do you have curiosity to ask one more question when you see students eating in the canteen, and each meal costs no more than 7 yuan?

Curiosity is the first step for us to start data cognitive literacy. In the process of reading and understanding information, our curiosity will make us unconsciously enter the state of dealing with data and want to explore more information and cognitive results, so that the four characteristics of data cognitive literacy begin to cycle repeatedly.

The second c: creativity. Only when curiosity is generated can interest be generated, and interest promotes the birth and change of creativity. Curiosity can bring creativity. When improving data cognitive literacy, if creative skills can be fully released, the world will be better.

The third C: critical thinking. When analyzing the data and information in front of us, we can think and make decisions from a more objective perspective, change preconceived ideas and change the overall thinking mode. Judge whether the analysis is reliable and comprehensive, and ensure the feasibility and scientificity of the decision.

3C of data cognitive literacy is very important to strengthen the role of data cognitive literacy. In our career and life, when we strive to achieve these goals, we can work towards smarter and more perfect decision-making.

Jordan Moreau said: "People are the essence of data cognitive literacy". The ultimate goal of understanding and improving our cognitive literacy should be to help individuals or organizations make wise data-based decisions and rely on a data-driven culture.

So how should we achieve such a goal? In data thinking, the author puts forward six steps to establish an informed decision-making framework for data:

Step 1: Ask a question. This step can be combined with 3C's curiosity to promote the development of data-centered thinking tendency.

Step 2: Get the data. It refers to obtaining useful data to help us specifically answer the questions raised in the first step.

Step 3: Analyze the data. It can be combined with the third feature of data cognitive literacy and 3C's creative and critical thinking, which runs through the whole process of data informed decision-making.

Step 4: Comprehensive analysis. According to personal experience, correctly integrate human factors, data and technology, find clear and perfect answers to questions, and make better decisions as much as possible.

Step 5: Make a decision. All the above steps are aimed at getting a crucial result, which is decision-making. No matter how perfect your strategy and plan are, without decision-making and implementation, everything is empty talk.

Step 6: Iteration. Decision is not the final result. Learning from known decisions and then repeating this process is more helpful for organizations to implement data-driven and data notification. In order to make more reasonable decisions, iteration is an essential link.

Data decision-making framework should be an integral part of our work with data cognitive literacy, and these six steps can guide us to make better decisions. The author said: This is an important process of intelligence and data-driven culture.

Writer Liang once explained what culture is. He said: culture is rooted in it? Cultivation; ? Need a reminder? Sense; Under the premise of constraints? By; Why not? Kindness of thinking.

How to use data to drive culture, Nanjing University of Science and Technology subsidizes poor students is a good example. When we improve our data cognitive literacy, learn to deal with data and always keep a positive attitude, we will certainly find that dealing with data will help us make wise decisions.