Leveraging Big Data in Salesforce for Business insights

Big Data means the massive volumes of structured, semi-structured and unstructured data that inundate businesses daily. It’s characterized by its volume, velocity and variety, making it challenging to manage and analyze using traditional data processing tools. Usually, such data is derived from social media, customer transactions, machine-generated data, etc.

Companies like Amazon and Netflix use Big Data to personalize the customer experience by recommending products or content based on user behavior. In the healthcare industry, Big Data is used to develop personalized treatment plans and predict patient outcomes. In the financial sector, Big Data is used to detect fraud and make informed investment decisions. In the transportation industry, Big Data is used to optimize routes, reduce fuel consumption and improve safety.

Salesforce optimizes resource allocation for efficiently handling large data volumes. Additionally, Salesforce’s mature support for standard SQL queries enables users to join disparate data sets and connect to various data sources. This connectivity and scalability make the platform ideal for businesses managing and analyzing big data.

Understanding Big Data in the context of Salesforce

Let’s discover a bit deeper what “the 3 Vs” means according to big data and how Salesforce manages and analyzes it.

Big Data characteristics:

• Volume: involves accumulating massive amounts of data from terabytes to petabytes generated by users, machines and networks.
• Velocity: many sources of Big Data, like social media or IoT devices, generate data continuously in real time, necessitating rapid processing and analytics. Velocity indicates the speed of data generation, collection and processing.
• Variety: includes structured (like databases), unstructured (like text, images, videos) and semi-structured (like XML files) data formats. Managing this variety requires flexible data processing capabilities.

Salesforce’s role in big data management encompasses data integration, advanced analytics, AI capabilities, real-time data processing and CRM integration, all within a secure and scalable cloud environment.

Setting up Salesforce for Big Data integration

Before importing big data into Salesforce, prepare your environment to ensure the system can handle, analyze and provide insights from large volumes of complex data. The preparation process includes a few steps:

1. Create a data integration strategy outlining the integration goals, the data types for integration and the systems involved.
2. Once the strategy is ready, configure the integration: set up the necessary APIs and data connections and ensure the appropriate security measures are present to protect sensitive data.
3. Install and configure the required tools and technologies, like data integration platforms, data management and analytics tools. It is essential to choose tools that can handle the volume and complexity of the integrated data and are compatible with the Salesforce environment.

The data integration process can begin once the tools and technologies are prepared.

Techniques for Efficient Data Import

• Data Batching: Divide large data sets into smaller batches for more efficient processing.
• Data Transformation: Preprocess data (formatting, cleaning, deduplication) before import to match Salesforce’s data structure and standards.
• Parallel Processing: Use parallel processing to speed up data import operations.
• Monitoring and Logging: Implement robust monitoring and logging to track the data import process and quickly address any issues.

Salesforce Data Import Solutions

When talking about big data, import is the most common business scenario. To import data into Salesforce, you can use its native tools, such as Data Loader or Data Import Wizard. Their capabilities and differences are presented in the table below.

Another way is to select a third-party Salesforce data import solution like Skyvia. This ETL, ELT and reverse ETL platform is cloud-based platform provide online courses on platforms like ExamLabs, user-friendly and supports 170+ connectors of different types. Its Salesforce Data Loader is a perfect-in-class Salesforce data loading solution for now.

Except for this, it provides:

• Data replication and mapping.
• Workflow automation.
• Bi-directional data sync.
• Error checking.
• CSV data export, saving to storage and uploading to FTP scenarios.

See the table below to compare the Salesforce Data loader and Skyvia tool parameters. 

 Analyzing Big Data within Salesforce

With Salesforce Analytics, businesses can gain insights into their operations, customers and market trends, enabling them to make informed decisions and drive growth.

Salesforce Reports and Dashboards

• Standard Reporting: Salesforce’s reporting feature allows users to create detailed and customizable reports based on various data points within the CRM.
• Dashboards: Users can create interactive dashboards that display data in various formats, such as charts, graphs and gauges.

Salesforce Einstein Analytics (Tableau CRM)

• AI-Powered Insights: Now rebranded as Tableau CRM, Einstein Analytics uses artificial intelligence to provide advanced analytics. It offers predictive insights and trend analysis.
• Interactive Data Exploration: This tool allows users to explore their data deeply with interactive dashboards and the ability to drill down into specific metrics or trends.

Salesforce Einstein Discovery

• Predictive Modeling: Einstein Discovery provides AI-driven predictions and recommendations. It helps identify patterns and trends in data that might not be immediately obvious.
• Automated Analysis: It automates the data analysis process, making it easier for users to understand complex data sets without needing a data science background.

Salesforce Data Studio

• Data Sharing and Monetization: Data Studio allows users to share data sets with partners and monetize their data securely. It’s handy for businesses with valuable customer insights that can benefit other parties.

AppExchange Analytic Apps

• Third-Party Analytics Tools: Salesforce’s AppExchange marketplace offers a wide range of third-party analytics apps that can be integrated with Salesforce, providing additional functionalities and specialized analytics capabilities.

Key Features of Salesforce Analytics

• Real-Time Analytics: Salesforce analytics tools provide real-time data processing, enabling businesses to make timely decisions based on the latest information.
• Mobile Accessibility: Many of Salesforce’s analytics tools are accessible on mobile devices, providing insights on the go.
• Customization: Salesforce allows extensive customization in reports and dashboards, enabling businesses to focus on the metrics that matter most to them.
• Collaboration: Tools like Chatter integrated with Salesforce analytics allow for collaborative data exploration and decision-making.
• Security and Compliance: Salesforce provides robust security features, ensuring data analysis complies with data protection regulations.

Extracting business insights from big data using Salesforce is about integrating diverse data sources, using advanced AI analytics, creating custom reports and visualizations and fostering a collaborative environment for data-driven decision-making.

Successful Big Data Integration in Salesforce

One of the best practices for leveraging big data in Salesforce is identifying key business objectives. Clarifying what the organization hopes to achieve with its data helps ensure that its initiatives align with its overall strategy and goals, including identifying specific metrics and KPIs the company wants to track or understanding the data types most relevant to achieving those objectives.

High-quality data is also critical for running an effective business. Data governance helps ensure that the organization uses its data responsibly and ethically. It’s about defining roles and responsibilities for data management, establishing data quality standards and implementing data validation and cleansing processes.

Companies can gain deeper insights into their data using predictive analytics to forecast future trends, leveraging natural language processing to analyze unstructured data, or using machine learning to automate routine tasks and processes.

Real-world Examples of Businesses Leveraging Big Data in Salesforce

• Financial institutions use Salesforce Einstein to predict customer financial behaviors, enabling personalized financial advice and product recommendations.
• Healthcare providers use Salesforce to integrate patient data from electronic health records and wearable devices to improve patient care management and provide personalized healthcare services.
• Retail companies integrate Salesforce with e-commerce platforms and social media data to understand customer buying patterns, manage inventory more effectively and create targeted marketing campaigns.
• Manufacturing firms integrate IoT device data with Salesforce to predict equipment maintenance needs, enhance supply chain efficiency and improve customer service by providing real-time client updates.
• Educational institutions use Salesforce to integrate data from various student information systems, analyze it to improve student engagement, tailor educational content and predict student success.

Conclusion

Leveraging big data in Salesforce, companies follow best practices like effective data integration, utilizing advanced analytics, focusing on data quality and regular system audits. Real-world examples across various industries showcase the transformative impact of integrating big data with Salesforce, enhancing customer experiences, operational efficiency and strategic decision-making.