First, to recap physical views and logical models:
The data model exposes business data in a format which end-users will understand, and offers maximum flexibility for end-user self-service. It does this through two complementary components, the physical view and the logical model. The physical view transforms a generic database schema into a businessfriendly schema, capturing just the tables and join relationships that the business user needs.
The logical model then organizes the information of the physical view into logical entities that correspond to business-world objects, entities and attributes. This makes the data accessible and easy to use.
The following example will demonstrate how easy it is to set up a virtual OLAP model against a relational database.
Follow the steps below to add a hierarchical level to an existing dimension in the ‘Order Model’.
Creating a virtual overlay on top of your existing ER database schema is as simple as defining your own dimensions and measures. By simply using the Entities and Attributes that already exist in your data model, you can quickly define the levels to group and aggregate your data with the OLAP overlay.
Adding Auto Drill-down to a Query - Adding auto drill-down at the query level is very similar to adding auto drill-down at the model level. (See Example 1: Passing column values in a drill-down and Example 2: Passing query-based values in drill-down above.) Auto drill-downs can be added to new queries or existing queries. Query-Based Parameters Note that the 'Auto Drill Down' window for query-level auto drill down also offers the option of passing query-based parameters. See Example 2: Passing query-based values in drill downs for details of the procedure. Specifying a query in the 'Auto Drill Down' window, as you did in Step 9 of the example, is the same for auto drill downs on queries as for auto drill downs on models; the value of the column clicked by the end-user provides the input to the query specified in the 'Auto Drill Down' window, which then generates as output the parameters to be passed in the auto drill down...
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How Does Data Mining Help Increase Sales on Instagram? - Data mining is pivotal in helping businesses increase sales on Instagram by providing valuable insights and enabling data-driven decision-making. Let's break down how Instagram data mining boosts conversion rates. Audience Segmentation Data mining allows businesses to analyze demographic information such as age, gender, location, and language preferences of their Instagram followers. This information helps create targeted content and tailor marketing campaigns to specific audience segments. For example, a fashion brand may discover that many of its Instagram followers are young females residing in urban areas. Armed with this knowledge, they know what they need to bring to the table to create content that resonates with this particular segment, featuring trendy styles and urban fashion trends that appeal to their target audience...
How Is Artificial Intelligence Being Used in Integrated Marketing? - Artificial Intelligence (AI) is transforming integrated marketing by enhancing various aspects of marketing strategies and operations. Here are some key ways AI is being used in integrated marketing: 1. Personalization and Customer Segmentation Usage: AI algorithms analyze customer data to create detailed profiles and segments. These segments can be based on demographics, behavior, purchase history, and preferences. Impact: Enables personalized marketing messages and offers, improving customer engagement and conversion rates. Personalization helps in delivering the right message to the right customer at the right time. 2. Predictive Analytics Usage: AI uses historical data to predict future customer behavior and market trends. This includes forecasting sales, customer lifetime value, and churn rates. Impact: Allows marketers to make data-driven decisions...
Predict and Influence Customer Behavior - One of the best examples of companies using business intelligence tools to impact customer behavior is Starbucks. Data is key to the company's success, and job postings it publishes demonstrate how serious these folks are when it comes to data analysis. For example, this Starbucks' data scientist job posting from LinkedIn has a long and impressive list of analytics-related experience requirements, take a look. Machine Learning And Data Product Dev And Deployment Under direction of more senior data scientists, contribute to AI and Machine Learning models in batch, real-time Develop data pipelines and scalable Restful APIs to create and enable analytical applications Statistics And Model Development And Deployment Leverage the latest cloud technologies, existing and immerging statistical and...
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