Jan 30, 2024
Deeper dive in Einstein discovery – Exploring Einstein AI and Advanced Analytics
Salesforce: Advanced analytics capabilities
The following sections will dive into the advanced analytics capabilities within Salesforce and CRMA. This will teach you the options for working with advanced analytics within Salesforce prior to diving into the hands-on case.
Deeper dive in Einstein discovery
Einstein Discovery is a powerful tool within the context of advanced analytics. It can seamlessly incorporate Artificial Intelligence (AI) and machine learning into a business user’s workflow, allowing for the creation of predictive models without the need for data scientists or advanced developers. To truly leverage this platform’s potential, users must understand their data, interpret model outcomes, and correctly configure training datasets.
Advanced analytics involves sophisticated techniques and tools that delve beyond the traditional realm of BI to uncover deeper insights, make predictions, and offer recommendations. Einstein Discovery aligns with several aspects of advanced analytics, such as machine learning, pattern matching, and forecasting. Utilizing a no-code AI approach, the platform embeds algorithms to train its models, eliminating the need for manual coding.
Choosing the right features and incorporating business knowledge is an important process in utilizing Einstein Discovery effectively. Business representatives and data analysts need to collaborate from the start of the model development process. This collaboration ensures that the most relevant predictors are chosen and that the model’s outputs are aligned with business needs and goals.
Some key practices for model enhancement in Einstein Discovery include segmenting datasets into different models, handling multicollinearity, managing diverse inputs, detecting and handling outliers, and choosing the optimal bucketing method for every use case. It is essential to balance the accuracy that the model delivers with the need for understanding why the prediction was made, with business requirements often guiding these decisions.
Segmentation can improve prediction quality by creating separate models for different data segments, accommodating the behavior of each segment in the model. Tests such as examining distributions of input variables can be used to determine when segmentation is a viable strategy.
Multicollinearity occurs when dependent variables can be linearly predicted from each other, causing potential issues in model quality. Methods to address multicollinearity include a thorough investigation of data, understanding business relations between variables, and using Einstein Discovery to highlight problematic relationships.
Outliers can impact the accuracy and explainability of models. Detecting and managing outliers can improve model quality by reducing noise. Einstein Discovery can recommend an approach for handling outliers, but business input is crucial in the decision-making process.
Bucketing is an essential consideration in model quality and explainability. The right balance between optimal model metrics and clear explanations can be achieved by using different bucketing methods such as bucket by count, manual bucketing, bucket by width, or Einstein Discovery’s recommended buckets.
Lastly, adding explicit second-order variables representing interactions between different features can also improve model accuracy. However, this method should be used with caution, taking into account potential limitations such as the number of possible column value combinations and the variety of data reflecting those combinations.
Einstein Discovery provides a powerful means of advanced analytics by incorporating machine learning, pattern matching, and forecasting techniques seamlessly into the business user’s workflow. Close collaboration between business users and data analysts ensures that the right features are chosen and that the model is both accurate and relevant to the company’s goals. Adopting various model enhancement practices, such as strategically segmenting datasets and fine-tuning bucketing methods, can help perfect the insights provided by Einstein Discovery and maximize its value as an advanced analytics solution.
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