Salesforce Wave Analytics

1. Data Integration: Connects to multiple data sources, including Salesforce CRM, ERP systems, and external databases.

2. Visualization: Provides interactive dashboards, charts, graphs, and maps for visualizing data trends and patterns.

3. Predictive Analytics: Utilizes AI and machine learning to forecast outcomes and recommend actions based on historical data.

4. Collaboration: Facilitates sharing of insights, dashboards, and datasets across teams for collaborative decision-making.

5. Mobile Access: Allows users to access analytics and insights on-the-go using the Salesforce mobile app.

Before diving into Salesforce Wave Analytics (now known as Salesforce Einstein Analytics), it's beneficial to have a strong foundation in the following areas:

1. Salesforce Platform Knowledge: Familiarity with Salesforce CRM and its various modules, such as Sales Cloud, Service Cloud, and Marketing Cloud, is essential as Wave Analytics is deeply integrated with Salesforce.

2. Data Analysis: Understanding of data analysis concepts, including data modeling, data visualization, and statistical analysis, will be helpful for interpreting and deriving insights from datasets.

3. Business Intelligence Tools: Experience with other business intelligence tools or platforms can provide a good foundation for learning Wave Analytics, as many concepts and techniques are transferrable across different tools.

4. Data Integration: Knowledge of data integration methods and tools, such as ETL (Extract, Transform, Load) processes, API integration, and data cleansing, is valuable for connecting and preparing data for analysis in Wave Analytics.

By learning Salesforce Wave Analytics, you gain the following skills:

1. Data Visualization: You'll become proficient in creating interactive and visually appealing dashboards and charts to present data insights effectively.

2. Data Exploration: You'll learn how to explore large datasets and uncover patterns, trends, and correlations to derive valuable insights.

3. Data Preparation: You'll gain skills in data preparation techniques, including data cleansing, transformation, and integration, to ensure data quality and consistency.

4. Analytics Modeling: You'll understand how to create and apply analytics models to predict outcomes, forecast trends, and identify opportunities for optimization.