Hit enter to search.
Data is the engine that drives modern business.
Every day, your company collects a lot of information, such as customer contacts and interactions, sales activities, orders, quotes, warehouse inventory, logistics, support tickets, and marketing campaign data.
But, do modern businesses use this wealth of collected data? Unfortunately, statistics show that they don’t.
Often, companies gather data from diverse sources. Moving data from one source to the other in order to get a unified view of the information can be difficult. As a result, many end up having disconnected systems and silos of data.
One of the most effective solutions to this challenge is real-time data integration. Especially if you’re using multiple SaaS applications. To connect siloed data, you need a smart approach to data management, aka a solid integration strategy.
In this article, we will investigate the different data integration methods to help you choose the right solution that will help you get those actionable insights into your business growth.
Data integration is the process of combining data from many different data sources, typically for analysis, business intelligence, reporting, or loading into an application.
It’s like cooking: you take various ingredients (your data) and use different data integration methods to create a meal (practical business insights).
Businesses often use a lot of applications to improve efficiency and serve their customers better. And, most of the time, these applications have their own databases and data warehouses.
Yet, if they are separated and unconnected, they bring little benefit. Therefore, for them to work together, they need to communicate! So, data integration is a must, as it creates a unified view of all data assets.
EXAMPLE
An e-commerce company decides to connect its CRM and ERP systems.
When a customer places an order, secure data transfer ensures that both systems instantly share information: the CRM tracks the customer's purchase history and preferences, and the ERP manages inventory and shipping.
Connecting these systems allows the company to notice such trends as: which products certain customer groups buy more often, or when they typically make purchases. This information then helps personalize marketing communication and plan inventory better – all because these two systems are talking to each other.
Every day businesses use a lot of applications, systems, and data warehouses. For these systems to work in unison, they need to be connected, because otherwise it becomes nearly impossible to make better sense of all the available information.
Put simply, all data must be available in a single place, in real-time for all the right stakeholders (including manufacturers, suppliers, logistics companies, customers). This way you’d be able to optimize processes and improve customer service.
To illustrate the tangible benefits of data integration, let’s continue with the ERP-CRM integration.
Businesses put a lot of effort into making every prospect or customer satisfied. They want to know all about their customers’ needs and requirements. That’s why turning this vendor-customer relationship into a partnership is the most efficient sales approach.
And from the operational standpoint, the benefits of such integration are plenty:
A CRM system can significantly enhance your understanding of your customers. However, for a comprehensive and accurate 360-degree view of your customer, integrating your CRM with your ERP is essential.
EXAMPLE
An online furniture company was struggling to keep up with 500+ orders per month.
The team spent hours manually copying order details from their e-commerce platform, accounting software, and shipping system. Customer support reps had to juggle between three different screens just to answer a basic "Where's my order?" question.
After implementing data integration, the company connected all these systems to work as one.
Now, when a customer orders a sofa, the information automatically flows everywhere it needs to go. The warehouse gets instant picking lists, the shipping system creates labels automatically, and the accounting software updates in real-time. What used to take 15 minutes of data entry per order now happens in seconds.
Deciding how to perform a data integration project can be a difficult decision to make. It requires a good understanding of what data integration methods and techniques are and which ones fit your business best.
There are many types of data integration, and you can easily get lost in readings about data migration, data streaming, data models, data virtualization, or data federation. Therefore, it’s important to keep it simple.
The most common data integration techniques can be grouped into five categories:
With Uniform Data Access, you can access enterprise data from disparate sets and present it uniformly, while allowing the data to stay in its original location.
The Uniform Data Access provides one standard method to access data, regardless of where it's stored in databases, files, cloud services, etc. It handles all the complex stuff behind the scenes, such as different data formats and storage systems. It also lets applications work with data without needing to know the technical details of how or where it's stored.
In other words – you don't need to know how each system works - you just ask for what you want, and the Uniform Data Access layer figures out the "how" part.
The benefits of the Uniform Data access data integration techniques include:
But Uniform Data Access has limitations. You can only use this integration method on similar data sources (same type of database). Also, data management only allows you to have limited information about the data's history and version management.
➡️ For example, an e-commerce company that needs to retrieve customer information from its SQL database, a cloud storage service, and a CRM system can use a single set of commands to access all the customer data, without needing to know the technical details of each system.
Common Data Storage (or CDS) is a data integration approach where data from different sources is copied and stored in one central location, using one standard format.
Also known as Data Warehouse, CDS includes data version management and allows you to combine data from different sources, such as mainframes, databases, flat files, etc.
The benefits of CDS include:
As to the limitations of this data integration technique, you should be aware that data might not be real-time (it depends on update frequency), CDS requires more storage space (for storing copies), you’ll need to maintain synchronization with source systems, and may face scalability challenges with very large datasets.
➡️ For example, a retail company copying daily sales data from all stores into a central data warehouse. Instead of querying each store's system separately, analysts can access all sales data from one place, in one format.
Application-based Integration is a method of connecting different software applications that a company works with by using a central app or platform to manage data flow between them.
Instead of each app storing and processing data separately, an integration tool pulls data from various sources, processes it, and sends it to the correct applications. This data integration technique allows a business to combine information from multiple tools without having to modify each one individually.
The benefits of the Application-based Integration are:
But, the Application-based Integration has limitations, especially if you are handling large volumes of data and large numbers of sources. Therefore, this technique is mainly suited to integrate a minimal number of applications and for more limited amounts of data from a limited number of sources and applications.
➡️ For example, a customer support app can instantly access updated customer data from a CRM system through an integration platform, making service faster and more personalized.
Also known as common user interface, Manual Data Integration is an approach where users access multiple data sources through a single interface (like a dashboard or a portal), but the data stays in its original locations and formats.
In other words, it simply presents the different data sources in one view and lets users see these multiple sources at once, while the original data is kept in its original location.
The benefits of the Manual Data Integration technique are:
On the flip side, the limitations are plenty. This technique requires manual effort to combine/analyze data, and as a result is prone to human errors. Moreover, it’s time-consuming, especially for large datasets, and may end up having inconsistent data formats between sources, and can only be used with a minimal number of sources and a small volume of data. And there are little automation possibilities.
➡️ For example, a financial analyst uses a dashboard that displays both QuickBooks accounting data and Salesforce customer information side by side, but must manually copy and combine the data in Excel for analysis.
Middleware is a layer of software that creates a common platform for all interactions internal and external to the organization:
Acting as an intermediary layer that handles data movement and transformation, Middleware Data Integration connects different systems and applications, translates data between different formats, routes information between systems, manages data transformations in real-time, and handles security and access control.
Middleware technology has helped many companies rationalize legacy IT systems into reusable, general-purpose functionality blocks that facilitate quicker changes to business processes.
Many middleware services are accessed through APIs – sets of tools, definitions, and protocols that allow applications to communicate with each other. APIs make it possible to connect completely different products and services through a common layer.
As to the limitations of the Middleware data integration technique:
➡️ For example, a hospital uses middleware to automatically translate and transfer patient records between its appointment scheduling system, electronic health records, and billing system, ensuring all systems stay synchronized in real-time.
SaaS companies can benefit a lot from data integration by unifying all their data sources.
Instead of scattered data repositories, data integration gives them a centralized view of their operations and customer interactions. This leads to better decisions that are based on real data, not guesses, and reduces duplicate data entry and manual work.
Business leaders can quickly spot opportunities, issues and trends, while customer support teams can improve their service by having immediate access to all relevant customer information.
Moreover, data integration speeds up innovation for SaaS companies. When data flows smoothly between applications, teams can experiment, test, and release new features more effectively – because they have complete information at hand.
Finally, data integration improves security and compliance, as it ensures all data is stored and managed consistently, reducing the risk of errors or security breaches.
EXAMPLE
A project management software (SaaS) company had its customer data stored in too many places. Their support team used a help desk system, their customer success team worked in a separate customer portal, and their usage data was sitting in yet another analytics tool. There was no way to quickly find out what their customers were up to.
By embracing real-time data integration tools, the company managed to connect all these systems. Now, when a customer contacts support, the team instantly sees a complete picture: recent feature usage, subscription status, past conversations, and even which training materials the customer has accessed.
More so, when a customer reported a workflow issue, the support team immediately saw the customer hadn't used a key feature that would solve their problem. Instead of just fixing the issue, the support team offered a personalized training session. Result? The customer learned about features they didn't know existed, their usage doubled, and they became one of the company’s advocates. Win-win!
All data integration techniques allow you to store, analyze, transfer, and view your data in a unified way.
To decide which technique suits your organization best and which data integration tool you should invest in, you need to fully understand your business and its processes. Also, you need to have a good understanding of the applications that your organization is using, as well as the reason why you are using them.
For example, if your needs are simple, then a relatively simple data migration tool will suffice. But if your needs are complex, then you might have to invest in a complex data integration platform.
Whichever data integration method you choose, to make it simpler, first of all, you need to ensure each application contains high data quality. Then, you need to evaluate the amount of data you have. If it’s substantial, then it’s advisable to seek professionals’ helps
Most large companies worldwide have invested in both an ERP system and a CRM system – some simultaneously, some step-wise.
A very common data integration scenario is that of an ERP-CRM integration. Most large companies worldwide have invested in both an ERP system and a CRM system, and integrating them is what can make the biggest difference to a company’s operations.
Finding the appropriate data integration method depends on factors such as data volume, real-time requirements, and security needs.
To help you choose the best integration technique for your business needs, you first need to consider these three factors:
Now, let’s visualize the five techniques we described above by who’s likely to benefit from each type most.
UNIFORM DATA ACCESS is best for the companies that:
➡️ For example, a bank that needs to access customer data from multiple branches but regulations prevent data consolidation.
COMMON DATA STORAGE is best for companies that:
➡️ For example, a retail chain consolidating sales data from all stores into a data warehouse.
APPLICATION-BASED INTEGRATION is best for:
➡️ For example, a small e-commerce company connecting their shopping cart directly to their inventory system.
MANUAL INTEGRATION is best for:
➡️ For example, a small consulting firm manually updating their client database weekly.
MIDDLEWARE DATA INTEGRATION is best for:
➡️ For example, a marketing agency connecting multiple client platforms (social media, email, CRM)
Here is a quick checklist that should help you make the right decision.
Choose |
When |
UNIFORM DATA ACCESS |
|
COMMON DATA STORAGE |
|
APPLICATION-BASED INTEGRATION |
|
MANUAL INTEGRATION |
|
MIDDLEWARE |
|
Rapidi was developed to make data integration projects as simple and robust as possible.
It is an innovative cloud integration system with pre-configured, adaptable business processes and templates that support the most common CRM-ERP integration points.
In particular, Rapidi’s data integration tool easily integrates Salesforce CRM application with any of the Microsoft Dynamics ERP solutions using pre-configured transfers/templates. Designed to enable direct integration, it makes your data flow easily between the two systems.
Installation is easy with the programming-free Rapidi data integration platform. No programming is required, even if you want to extend your Salesforce or Microsoft Dynamics with add-on solutions or customizations.
The underlying technologies in Rapidi contain specific features to optimize integration with Salesforce and Microsoft Dynamics ERP. As a result, you don’t even have to go through an extensive testing process. You just need to make sure you have good data quality.
Rapidi runs via the RapidiConnector, a unique technology that ensures data communication between Rapidi and your on-site systems is not compromised. The RapidiConnector resides in your network and is automatically compressed, and SSL encrypts data before transferring, making it practically impossible to decipher.
Rapidi Data Integration Solution is an innovative iPaaS integration hat is three-fold in its simplicity: in design, onboarding, and use. It is fast yet flexible, secure, and robust.
Data is crucial for modern business.
Yet still, a lot of companies are not using it effectively, as about 68% of enterprise data goes unused. This means only one thing – there is an urgent need for better data integration strategies.
There are five main ways to integrate data, each suited for different needs:
When choosing a data integration technique, you should consider three fundamental questions: the speed at which you need access to data, the volume of data you’re handling, and your technical and budgetary resources.
A successful data integration is a godsend for any business – big or small. Not only will they get a comprehensive view of customers, reduce manual work, and improve their decision-making through real-time access to data, but they will also be able to enhance customer service and stay ahead of the emerging trends and customer behavior changes.
Transforming data into insights you act upon is the ultimate goal!
When choosing the right data integration technique for your business, remember – there's no one-size-fits-all solution. The best integration method depends on your specific business needs, resources, and goals. Success comes from matching the right method to your unique business circumstances.
So, choose wisely. Or better ask Rapidi for help! We will listen to your needs and offer the solution that fits your business goals.
Follow us on LinkedIn to learn more about Rapidi Data Integration Solutions, and download this e-Book for additional information about Rapidi.
Beate Thomsen, Co-founder & Product Design
Carrer de la Font del Colom, 6,
L'Aldosa,
AD400 La Massana, Andorra
Copyright © 2024 Rapidi.
All Rights Reserved
Terms & Conditions |
Privacy Policy