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Streamlining Cross-Platform Data Integration for B2B Enterprises

From supply chain management systems to analytics, data is critical for most operations. Small operations are performed through a single platform like a spreadsheet. But complex processes demand cross-platform integration.

You may have used CRM platforms. They are classic examples of cross-platform data integration. These tools integrate multiple sources, such as different marketing channels, analytics, and order management to provide a better user experience.

Another example may include e-commerce. We’ve integrated our e-commerce platform with a logistics management system. It updates the stock availability and ships the products in real-time.

In this article, we’ll go through the basics of cross-platform data integration for B2B enterprises and help you understand the importance and drawbacks of the same.

What is Cross-Platform Data Integration?

Data integration is the process of bringing small components from a single or multiple data sources into a single system for a unified view. Cross-platform data integration is a subset that integrates data from different platforms primarily available in different formats.

For example, cross-platform data integration helps share patient information (PHI), including scans, prescriptions, and test results, among hospitals and healthcare providers while complying with HIPAA regulations.

Real-time applications of similar integrations, like remote monitoring and telemedicine, also have helped monitor patients during the COVID-19 pandemic.

Cross-Platform Data Integration Processes

Data sorting, interpretation, and synchronization are the three key elements of cross-platform data integration. The collection part is handled by different sequences that aren’t related to today’s discussion.

In this section, we’ll discuss a few tools and methods that are used for the mentioned processes.

Data Mapping

You must understand the structure and format of the two systems before integrating them. Data mapping is like creating a translation guide that addresses both systems.

Suppose your sales team requires five datasets (Name, phone number, email, buyer persona, and funnel position) from three different marketing channels (Facebook, Google Ads, and offline collection). Data mapping makes the connection between these channels to the sales CRM for better customer handling.

Three techniques are typically employed to map homogenous data.

  • Manual Data Mapping

Manual mapping is the most controlled and fully customizable data mapping solution. But, it requires professional data mappers to code and map data sources.

  • Schema Mapping

Schema mapping, a semi-automated data mapping technique, relies on tools and some knowledge of coding. It involves your team oscillating between manual and automated data mapping.

  • Automated Data-Mapping

Completely reliant on automated tools, the Automated data-mapping techniques typically offer generic GUIs for drag-drop operation. It’s generally used when you don’t have access to mappers.


An Application Programming Interface (API) is a set of codes or protocols that dictates the communication between two platforms and software. APIs are mostly instantaneous.

Think about your favorite restaurant. Hundreds of customers don’t overwhelm the chef with info requests and orders. You’re given a menu with a list of dishes to choose from. You, as a customer, interact with the menu and make a selection for the chef and staff to carry out the request. The menu is an example of API.

Traditional methods, such as file transfers, CLI, and custom integration, takes a greater time and manual interaction to facilitate the same processes.

Two main types of APIs are used in real-time cross-platform integrations: RESTful APIs and GraphQL APIs.

Both have their use cases, with REST architecture focusing more on interacting with pre-defined data and GraphQL fetching user-requested data from the servers.


Middleware is software that uses APIs to separate system interactions. Technically, it takes vertical slices off of the stacked layers of the systems to make more robust connections than APIs.

Chapters of a book, for example, are most efficiently navigated by adding a table of contents. Owing to the vertical approach, the risk of data competition and duplication is prevalent. Despite the caution, middleware is popular for the tools and supplies that enhance the basic API libraries.

ETL Tools

A subset of the middleware tools, the ETL (Extract, Transform, Load) tools specialize in data-related tasks and lack functionalities like transaction management and load balancing.

ETL tools mostly revolve around data extraction, transformation, and processing. Data mapping and schema transformation tools also often are included in the package. Tailored for data integration and reporting, these tools can handle much larger volumes of data than middleware tools.

It’s easier to understand the difference between ETL and middleware tools through an example.

An ETL tool for a large e-commerce company would extract data from the online order system before transforming the data to, say, include packaging details or calculate shipping dates, and finally load the data into the logistics partner’s system to facilitate the shipping process.

On the other hand, Middleware tools may communicate with the concerned system about orders that need to be processed and if the transactions have already been made.

Custom Development

Most large B2B enterprises with their partners develop their custom cross-platform data integration system to fulfill their specific needs. This type of arrangement is more costly and requires exclusive knowledge transfer to maintain and update.

Benefits of Cross-Platform Data Integration for B2B Enterprises

A scaling business demands faster operations. As you prioritize business expansion and customer experience, certain elements of your business will need to be unified and synchronized.

Cross-platform data integration, as discussed, helps with that. However, here are a few more reasons to embrace the same for your growing business.

Better Efficiency

Data integration, through automating data exchange between systems, reduces manual interaction, data entry, and labor to produce results with better efficiency.

More Accuracy

As human interactions are minimized, the probability of the occurrence of human error reduces to a near-zero level. It ensures that the datasets are accurate and consistent throughout the systems.

Near Real-Time Integration

Manual file transfers, CLI, and all other traditional systems lack real-time data integration. But with modern technologies, such as APIs and ETL tools, data is updated in real-time or near-real-time. This is critical to make informed decisions and provide better customer service.

Cost Savings

While the initiation costs of these tools may be on the higher side, integrated systems can lead to long-term cost savings through reduced manual labor, minimized errors, and faster operations.

Drawbacks of Cross-Platform Data Integration

The potential drawbacks, that may still withhold your decision to upgrade to data integration tools are:

Integration Complexity

Your existing employee base may lack the necessary coding knowledge to integrate the complex systems and you may not be ready for the significant investment required to build a team yet.

But, easier fully automated cross-platform data integration tools are made available for you to pay a fee and use without needing to write a single line of code.

Greater Initiation Cost

These tools often come with a greater initiation cost than manual handling. But, the tools pay for themselves in the long run and seldom fail to generate the desired ROI, given the business is growing.

Data Security Concerns

As you’re expected to run your sensitive data through different channels and third-party providers, there exists a risk of mishandling and theft. However, it can be mitigated by vetting the security components associated with each partner.

Inaccurate Data Propagation

While rare and usually a result of human error, inaccuracy in the datasets can propagate to the end node without manual supervision and intervention. These issues are mitigable to an extent, but can’t be guaranteed to be eradicated.

Vendor Dependency

Depending on the integration tools or platforms used, businesses may become dependent on specific vendors, limiting flexibility and potentially increasing costs in the long run. Contracts and maintaining optimum business relations are essential to mitigate this issue.

The Bottom Line

Cross-platform data integration is the key to unlocking the true potential of your data. Embrace it wisely, and it can be a catalyst for your business's success.

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