Salesforce has become the central hub for many businesses in recent years, providing an easy way to manage customer information and unlock more value from it. And yet, simply having a comprehensive CRM such as Salesforce is not enough by itself since it also has to receive information from multiple sources in a seamless and convenient manner.
This is the challenge that Salesforce users encounter surprisingly often – setting up a comprehensive integration between Salesforce and other elements of a company’s ecosystem to export and manage information when necessary. The entire point of Salesforce becomes null and void when it does not receive customer information, making this particular issue the highest possible priority for Salesforce environments.
The primary purpose of this guide is to provide ample information about the topic of data integration in Salesforce. We would like to offer a complete overview of the environment, including data integration types, core components, and best practices to work with. Additionally, we are going to showcase a number of data integration software examples and try to present a comprehensive guide on how the integration process should be performed.
Understanding Salesforce data integration
Data integration in Salesforce is a process of creating and maintaining stable and fast connections between Salesforce and other business environments. Being able to offer seamless information synchronization in an automatic fashion is the main goal of such integrations. Data integration is supposed to extend the purpose far beyond a simple data exporting process from one point to another by creating a comprehensive data environment with natural data flow.
Data integration types in Salesforce
Data integration is far from a uniform and standardized process in Salesforce due to the existence of multiple possible data integration types and methods. First, we would like to compare some of the most obvious examples head-to-head:
- Real-time and Batch Integration
- Real-time integration is fast and responsive, capable of performing data synchronization as soon as there are changes in the target data storage.
- Batch integration uses scheduled intervals as its primary method of data processing, with most batches being a lot bigger than an average real-time synchronization data size.
- Bidirectional and Unidirectional Synchronization
- Bidirectional synchronization supports data flow in both directions between environments to ensure consistency and avoid data version mismatch.
- Unidirectional synchronization supports one-way data flows while maintaining the origin of data flow as the primary source of truth.
- Middleware and API-based integration
- Middleware solutions are third-party tools and platforms that can simplify complex data integration scenarios by acting as intermediaries that can receive information from one source and deliver it to another with all of the necessary transformations performed.
- API-based integration uses direct communication of one system to another by utilizing the power of Salesforce’s API, it can provide a lot of freedom for customization and granular control but can be difficult to set up.
Core components of the Salesforce data integration process
Salesforce itself is also an active participant in the data integration (and data exchange) process, with multiple important components of the platform being crucial for establishing seamless data flow. In this section, we would like to introduce four main components of data connection in Salesforce: APIs, external objects, Salesforce Connect, and Salesforce AppExchange Connectors.
Application Programming Interfaces are necessary for Salesforce to understand how to handle exported data in the first place. SOAP API is the best pick for complex operations with strict data validation rules, while REST API is more suitable for lightweight tasks such as web integrations or mobile applications. There is also Bulk API, which was purpose-made to handle large data sets with high performance, as well as many other examples of case-specific APIs that Salesforce has in its platform.
External Objects in Salesforce are virtual tables used to perform data mapping for information located outside of immediate Salesforce storage. The primary goal of External Objects is to provide access to external information without the need to copy it to the internal memory. It excels at maintaining real-time connections to data management systems, and it can also make it possible to view current inventory levels from ERP systems.
Salesforce Connect is a complex data integration tool that allows for cross-object relationships, offers real-time access to external data, and minimizes data storage costs in the process. It supports protocols such as OData 2.0 and 4.0, making it more accessible to different data exporting processes. The usage of Salesforce Connect is paramount for creating External Objects, they are not competitors but rather parts of the same system.
Salesforce AppExchange Connectors are integration solutions built in advance to work with AppExchange solutions specifically. They can be deployed with a surprisingly high performance, are updated on a regular basis, and has multiple security measures and best practices built into them already.
Challenges expected in Salesforce data integration
Data integration in Salesforce can be the source of a massive increase in business value, but it is not a perfect system, either. Practically none of the integration projects are capable of completely avoiding compliance requirements, organizational constraints, technical hurdles, and other issues that all data integration processes have, no matter how expensive and thought-through they are.
Complete understanding of all these challenges is less about resolving these issues when they happen and more about being prepared to work around them beforehand. Proper planning can avoid most common obstacles in data integration processes, even if a complete lack of issues is practically impossible in the majority of Salesforce environments.
Here, we are going to present three primary challenges for data integration in Salesforce:
- Architecture complexity
- Data fragmentation and data siloing
- Compliance and security
Architecture complexity covers the existence of multiple legacy systems that might have to become compatible with the modern Salesforce environment in order to export information in an efficient manner. This is one of many factors that alleviate the complexity of data exporting and transformation rules that data integration frameworks have to deal with, as well.
Being able to work around existing capabilities of the business infrastructure is another valuable factor that helps avoid performance bottlenecks and other similar issues. Even the existence of many different APIs in Salesforce contributes to the complexity of data integration projects in most cases.
Data fragmentation and data siloing are both substantial issues that affect the state of the information in some way. The lack of standardized data governance makes it difficult to control and manage information from multiple sources. This, in turn, makes it even more difficult to create a single source of truth in the combined environment and maintain it for a prolonged period of time.
Issues with data quality in the form of duplicate or incomplete records negatively affect the usefulness of information in the environment, and the introduction of multiple data formats from external environments makes data integration even more difficult to manage.
Compliance and security are much more interconnected than one might expect. Certain security measures are commonly mentioned as primary compliance requirements in regulations such as CCPA or GDPR, along with the requirement of a detailed audit trail for a specific time period. Data encryption, both in transit and at rest, is another common requirement, along with user access management systems and other measures that exist to reduce the risk of data breaches or unauthorized data access.
A clear understanding of all the challenges the data integration process in Salesforce might encounter is a good first step toward creating a successful implementation plan down the line.
Best practices for data integration in Salesforce
Another useful element of preparation for data integration in Salesforce is the strategic approach to the entire process. Preparation is key in this situation, and following all the best practices mentioned below should help maximize the value of integration while avoiding most of the common issues.
1. Define the objective of integration
A clear and measurable objective is a crucial element of any integration effort, considering the fact that integration should be a business necessity and not just a technical possibility. The objectives in question can be identified by analyzing the current business situation, asking stakeholders from multiple departments, and so on. Once established, documenting specific use cases and their outcomes would work wonders when it comes to avoiding issues in the future, and the creation of measurable success metrics improves the clarity of how successful the implementation is.
2. Work on improving data quality
Unfortunately, even the state-of-the-art integration setup would not be able to operate at its fullest if the data quality in the environment is poor. If you want to receive actionable and reliable insights from the data integration framework, then working on data quality should be one of your main priorities.
Data format standardization and a validation rule set should both work as a good starting point, followed by clear data ownership roles, automated data quality checks, and so on. A comprehensive data dictionary should make it easier to define transformations and field mappings while regular cleanup activities and audits would make it easier to keep track of the centralized data environment and its current state.
3. Utilize native Salesforce tools for integration
Salesforce has a wide range of tools and solutions for different use cases, some of which can be helpful at different stages of the data integration process. Both Salesforce Connect and AppExchange Solutions are good examples of such tools that we went over before, and MuleSoft is another great example of a data integration solution that we are going to discuss in more detail below.
4. Work on the flexibility and scalability of the integration framework
It is difficult to imagine a stagnant modern business without any changes in its infrastructure. As such, the future integration architecture should be able to accommodate many different situations where the company grows or changes in some way. Common considerations in these situations include API limit considerations, bulk processing, caching, detailed plans for peak load scenarios, and many other theoretical situations.
5. Remember about the necessity of security and compliance
As mentioned before, security and compliance go hand in hand in most situations, and treating data security as an add-on to your data integration strategy is a recipe for disaster. A proper data integration environment would have both security and compliance implemented into it from the start, with features such as encryption, data access monitoring, authentication control, security auditing, and so on. As for the regulation-specific requirements, they are obviously going to differ depending on the exact compliance framework – GDPR facilitates data portability and data minimization (among other requirements), HIPAA necessitates data encryption for PHI and access control logging, and so on.
Main use cases for Salesforce data integration
Data integration in the context of Salesforce often connects various environments and frameworks, depending on the business needs. Below, we present five common examples of data integration: marketing automation, ERP, customer support systems, e-commerce, and data warehousing.
Marketing automation integration is supposed to reduce the gap between marketing and sales departments by creating a closed loop of customer engagement and data-driven insights. Tools such as Marketo, HubSpot, and Mailchimp are commonly used for integration with Salesforce, with content engagement tracking, advanced segmentation, email campaign performance metrics, and other features being necessary for this particular instance of data integration process.
ERP integration aims to concentrate information in a single source to act as the single correct version of all customer and transactional data in order to improve data accuracy and streamline operations. The correct implementation of data integration in ERP environments simplifies cash flow management, improves customer satisfaction, and reduces the number of data entry errors. Platforms such as Oracle, SAP, and Microsoft Dynamics are common examples of external data sources that can be integrated with Salesforce as ERP environments.
Customer support systems integration strives to deliver consistent customer experience to any of the existing touchpoints and also attempts to improve the efficiency of the service itself. Features such as SLA monitoring, ticket tracking, and unified customer view are practically mandatory for such environments. Jira, Zendesk, and ServiceNow are commonly used as platforms to integrate with Salesforce in this context.
E-commerce integration combines customer relationship management with online sales to create a singular, consistent commerce experience. It necessitates product catalog management, inventory synchronization, order management, and other similar features in platforms such as WooCommerce, Magento, and Shopify.
Data warehousing and analytics integration is used to transform raw data into useful insights to improve data-driven decision-making. It requires predictive analytics, historical data analysis, and data warehouse synchronization to function properly, and the most common examples of environments like these are Snowflake, AWS, and Google BigQuery.
Data integration can be used to create immense value for specific situations, and each of these use cases represents a potential opportunity for such value generation. However, the probability of actual success depends on proper implementation, careful planning, and regular data-driven optimization.
Popular Salesforce data integration tools
Being able to choose the integration tool that fits your business environment the best can dramatically improve the chances of the data integration process being a success. You can find six examples of potential data integration solutions with a substantial level of popularity in the list below.
MuleSoft
MuleSoft is an integration platform owned by Salesforce that provides comprehensive integration capabilities with multiple supplementary features. It uses an API-led connectivity approach that works great with large and complex enterprise environments.
MuleSoft can offer:
- Mule runtime engine
- Data Weave transformation engine
- Dedicated Anypoint Platform for unified integration
- CloudHub tool for easier deployment and more.
It supports various data formats and multiple integration patterns, has a built-in data mapping solution, advanced logging capabilities, and a choice between batch and real-time data processing.
Zapier
Zapier is a cloud-based automation platform that connects web applications using a no-code interface. The simplified interface drastically reduces the skill entry barrier for the solution by offering easy creation of automated workflows between applications such as Salesforce and others.
Zapier can provide:
- Schedule-based triggers
- Shared team folders
- Visual workflow builder
- Format and filter data
- Conditional logic, etc.
Zapier’s integration capabilities include support for WebHook, a one-to-many data distribution model, basic data transformation capabilities, automatic data synchronization, lookup tables for data mapping, and more.
Jitterbit
Jitterbit operates as an API integration platform that tries to offer enterprise-grade capabilities in a user-friendly package. It can be deployed both on-premise and in the cloud, making it a great choice for complex corporate infrastructures with hybrid environments.
Jitterbit’s feature set includes:
- API Manager
- Process Templates
- Harmony Integration Platforms
- Private Agents for on-premise connection
- Project Management tools, and more.
Jitterbit works great as a data integration solution with bi-directional synchronization, advanced error handling, complex data transformation support, built-in data quality tools, and many other features.
Airbyte
Airbyte is a data integration platform with an open-source core that puts a lot of focus on its community-led development and transparency. It can be used to work with both ELT and ETL processes (Extract, Load, Transform, and Extract, Transform, Load, respectively), making it possible for organizations to leverage pre-built connections and build their own ones at the same time.
Airbyte’s feature set covers:
- Docker-based deployment
- Version control integration
- Open-source codebase
- Custom transformation support
- Community-contributed connectors, and so on.
Airbyte is great in data integration situations with its schema evolution handling, data warehouse integration, Change Data Capture support, automated schema mapping, extensible architecture for custom needs, and multiple other capabilities.
Exalate
Exalate is a tool for decentralized synchronization between various environments. It offers great data integration capabilities in cases where selective information sharing is a necessity and high levels of security and autonomy are mandatory.
Exalate can offer the following feature set:
- Trigger management
- Version control
- Decentralized architecture
- Sync configuration UI
- Audit logging, and more.
Exalate can offer plenty of capabilities in certain data integration scenarios with its toolset of change tracking, filtered data sharing, custom field mapping, automated sync rules, secure data transmission, and other convenient capabilities.
GRAX
GRAX is a powerful data value platform that combines multiple data management capabilities—whether it’s backup, archive, or integration. It excels at capturing and managing Salesforce data while also acting as an intermediary for external data sources when it comes to importing additional information into Salesforce.
GRAX offers an impressive feature set, including:
- Version control
- Backup automation
- Time-machine recovery
- Historical data capture
- Analytics integration
- Recovery tools, and more.
Although data integration is not GRAX’s primary capability, it can still be used in certain data integration scenarios due to its feature set. With native Salesforce integration and data warehouse connectivity, its historical data synchronization simplifies data collection, and its data lifecycle management dramatically improves overall control over Salesforce data—including data from external environments.
Unify Your Enterprise Data with GRAX
Break down silos and create a single source of truth.
Conclusion
The growing complexity of the digital landscape for modern businesses makes it essential to implement more unconventional features and strategies to gain a competitive edge. Data integration is one such feature, and it’s practically mandatory for most businesses working within Salesforce environments.
This article aims to demonstrate how challenging it can be to achieve successful integration without developing a broader business ecosystem that goes beyond data integration as its primary focus.
Implementation guidance will vary significantly depending on factors such as integration methods, data integration software, and more. However, most integration processes can be grouped into five key steps:
- Data source analysis. Identifying all the data sources to be integrated with Salesforce would be a good place to start with all the databases, cloud applications, and legacy systems. A comprehensive inventory of all the formats, dependencies, and data fields would go a long way toward making sure that you are not missing anything.
- Integration method choice. Evaluate multiple factors to choose the correct integration method for your business. Parameters such as existing infrastructure capabilities, real-time needs, and data volumes should offer a great perspective on which of the three options (APIs, middleware, native) would work best for you.
- Data flow mapping and setup. Perform a detailed mapping of the data flow process with all endpoints, triggers, and synchronization frequencies. Clear transformation rules should be defined, and error handling protocols must be established for data consistency.
- Integration testing process. Thorough testing should be performed for all plans and setup configurations in order to locate and resolve data discrepancies and other types of errors before deploying everything to a live environment. It is also highly recommended to use multiple scenarios for testing purposes, including high-volume situations and edge cases to validate the range of capabilities of the integration.
- Monitoring, maintenance, and improvement. The health of data flow should be monitored at regular intervals, and automated alerts for anomalies or disruptions would make it much easier to detect irregularities. Thorough auditing of the integration for both compliance and performance would make it more convenient to update configurations as time goes on.
At the end of the day, data integration can be difficult to implement and even more challenging to improve as time goes on. However, its advantages tend to outweigh the negatives, considering how successful data integration enables real-time data synchronization, breaks down data silos, reduces the possibility of human errors, and simplifies future growth. It is a process that should not be approached lightly, but it can also be exceedingly beneficial if done right.
Frequently Asked Questions
What is by far the most commonly used method of data integration in Salesforce?
API-based integration relying primarily on REST APIs is the most common method of data integration in Salesforce. It deals with the majority of minor integration tasks while being highly compatible with all kinds of tools and services and supporting data exchange in real time with high security.
Which one is better: real-time or batch integration?
Real-time integration works best when working with smaller-scale interactions that typically do not require large data masses to be exported frequently – including mission-critical business processes, financial transactions with instant processing, etc. On the other hand, batch integration operates with large data masses, limited API calls, complex data transformations, and legacy systems – putting complexity over frequency.
How long does it take to complete a data integration process?
A typical data integration process from start to finish tends to take from several weeks up to six months or even more, depending on the number of data environments, their complexity, transformation needs, and other factors.
