標題: Say you have a web lead form that collects [打印本頁] 作者: sharmin.akter6 時間: 2024-3-6 11:10 標題: Say you have a web lead form that collects In addition, you may need more than one transformation technique. Here are five data transformation techniques you can employ: Data smoothing Data aggregation Data normalization Data discretization Attribute construction Let’s go through each one. 1. Data smoothing Have you ever looked at a bunch of data points that don’t seem to tell you much? To highlight important features in your data set, you need data smoothing. Data smoothing is the process of removing noise from a data set using algorithms. It helps you see patterns more clearly as it removes out the data outliers. Data smoothing helps predict trends and can help you with sales or seasonal forecasting.
2. Data aggregation Data aggregation is a tactic that Brazil WhatsApp Number Data stores and presents data in a summary format. It is beneficial if you have more than one data source and must compile and analyze the data together. For example, let’s say you own multiple pet stores in different locations. You can aggregate the sales performance of all your stores, so you have a monthly sales analysis report on your overall revenue. 3. Data normalization If you want to segment and analyze your data easily, you can use data normalization. Data normalization is the process of organizing data to have a uniform and standard way of recording them.
As a result, it’s easier for you to sort, segment, and analyze your data. a user’s first and last names. Some users may type their name in capitalized format, while others submit theirs in lowercase. Data normalization can help you standardize the format of the first and last names when you store them in your database or customer relationship management (CRM) software. 4. Data discretization Data discretization is a data transformation tactic that converts continuous values into a set of intervals, so your data is easier to analyze and group together. This step is especially useful for customer segmentation.
