What Are the Uses of Data Science? At first glance, the data does look very simple. But, actually there are many things that businesses can enjoy by utilizing data. For example, here are some examples of the benefits of data science: By analyzing historical sales data, businesses can predict which products will sell in certain seasons. A manufacturing business can view failed product data to improve product quality in the future. Logistics businesses can check data related to traffic and routes to make the shipping process more efficient . To increase the level of employee satisfaction , the human resource division can evaluate data that comes from employee feedback. A securities company can look at data about financial market patterns to make more effective investment decisions . illustration of doing data science
There are many more uses of data that you can find. In essence, data can be used to do the following: Predictions – Examples such as predicting consumer behavior based on sales history. Detection – Examples such as detecting website security holes based on data obtained from security plugins. Decision making – For example, such as determining the value of accreditation based on Brazil phone number list data related to agency performance. Classification – Examples such as differentiating high quality products and low quality products based on data from the quality control team . Recommendations – Examples include product recommendations from online stores based on product pages visited by potential buyers. Well, now you know that there are tons of things you can do with data science. Are you curious about how it works?
Let's see in the next section! Also read: Must Know How Does Data Science Work? If explained as a whole, how data science works is actually quite complex. Therefore, the following is the flow of implementing data science if explained more simply: Planning – First of all, the data scientist needs to plan the projects they are going to undertake, as well as the results they want. Data mining – Then, the data search process begins. Either through qualitative or quantitative methods. Data management – After getting the desired data, the data scientist will store the data regularly so that it is easy to access. Identification of data – The data obtained will be identified. Usually by attaching a category or label to each data. Data analysis - In this phase, every data will be processed and analyzed so that the business can get the information it wants. Data visualization – Finally, the information obtained will be interpreted into a format that is easier to understand.