Integrating Salesforce with AI and ML for Enhanced Data Analytics

Modern data analytics operates huge amounts of data from dispersed data sources. One of the most important ones is a CRM system, Salesforce or its analogues, that contains nearly all the needed details about prospects and customers.

Raw data itself usually doesn’t have much value as it needs to be prepared for analysis. Salesforce has its in-build analytics mechanisms, which aren’t usually powerful enough to derive meaningful insights. That’s why many companies tend to transfer Salesforce data to a data warehouse with powerful analytical capabilities and in-built AI functionality.

Basics of AI and ML in Business Intelligence

The first thing to focus on is the primary aim of business intelligence – analyzing data for the sake of delivering actionable information for business prosperity. Data analysis comes in the descriptive, diagnostic, predictive, and prescriptive forms. Standard analytics tools and solutions only work with data in the area of descriptive and diagnostic analytics, explaining what happened in the past and what are the roots of that.

However, business intelligence’s purpose is to find out the ways for further development of a company for the sake of obtaining a stable market position and outperforming competitors. Here come AI and ML models that help organizations drive predictions for the future.

Artificial intelligence is the umbrella term that describes technology capable of mimicking human-like reasoning, problem-solving, and decision-making. To develop such smart systems, the process of machine learning comes first.

The ML models and algorithms could be categorized into three groups:

• Supervised learning
• Unsupervised learning
• Semi-supervised learning

Supervised learning includes classification (Decision Tree, k-NN, Random Forest, Linear Perceptron, Naive Bayes) and regression (Linear, Polynomial) algorithms. They take data sets as a source and divide them into training and test sets by labelling data. Then, when new data arrives, the system trained with supervised learning algorithms can classify it properly.

Clustering algorithms make the basis of unsupervised learning that classifies data as the output. Unsupervised learning is easier to implement but is highly sensitive to missing data values, so the dataset should be properly prepared before ML model processing.

Any algorithm in machine learning helps companies with the following:

• Discovering new patterns
• Establishing relationships in data

Exporting Data from Salesforce for AI and ML

As data warehouses have the most powerful tools for analytics, AI, and ML learning, let’s take an overview of how to export data from Salesforce to a data warehouse. In our example, we show how to replicate Salesforce data to Snowflake using the ELT approach with the help of Skyvia.

But some words about Skyvia first! This is a universal cloud platform for a wide range of data integration tasks: ELT, ETL, Reverse ETL, workflow automation, etc. It offers an intuitive visual wizard with drag-and-drop functionality for easy setup of any data integration scenario.

• Create an account or log into Skyvia.
• Click +New in the top menu and select Replication under the Integration section.
• Select Salesforce as a source and your preferred data warehouse as a destination.
• Click Add a task to indicate which objects need to be loaded into a data warehouse.

• Click Schedule if you want to organize the replication regularly.
• Click Create to save the scenario.

After the replication, see the migrated data in your data warehouse. Now, you can apply analytical functions and AI models to your data.

Building AI and ML Models with Salesforce Data

Almost every data warehouse offers built-in mechanisms for working with AI and ML models. However, most of them are based on the scikit-learn machine learning library in Python.

For instance, Snowflake has a Snowpark ML modelling module that contains Python APIs for preprocessing and transforming data, training models, and prediction algorithms. Here, you can find detailed instructions on how to build AI and ML models on Snowflake.

Building AI and ML models will help you to make predictions and discover insights for the future. This is an essential thing for those who want to remain competitive in the market and develop their businesses.

Conclusion

Artificial intelligence, similarly to data analytics, uses large datasets, but it goes far beyond just finding common patterns – it can even make predictions. So, AI can take your existing Salesforce data and foresee your future incomes, target audience characteristics, seasonal sales boost, etc.

As a rule, AI and ML models are trained and implemented on the base of a data warehouse with Python libraries for machine learning. That’s why it’s necessary to aggregate your Salesforce data on a data warehouse with the help of Skyvia first. You can even set up regular automatic data exports from Salesforce to a data warehouse to ensure all the recent information exists.