Data Integration Techniques: Transforming Data Into Actionable Insights

According to Inc.com, up to 73 percent of company data goes unused. How do you ensure that your organization uses the data you collect? 

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. 

Data integration could be the answer to that challenge. Below, we will investigate the different data integration methods to help you choose the right solution.

WHAT IS DATA INTEGRATION 

Data integration is the process of combining data from many different data sources, typically for analysis, business intelligence, reporting, or loading into an application. Data integration is highly relevant to most organizations of medium to large size because they typically use a wide array of applications to gain efficiency and serve their customers better. 

Most of the time, these applications have their own databases and data stores. These applications could be cloud-based or hosted on local servers (on-premise). Regardless, they are only beneficial if they share data between them, so data application is a must. 

To make qualified business decisions, organizations need to have one unified view of their customer data. Developing processes for consolidating data from multiple applications and creating a unified view of data assets – this is what data integration is all about.

HOW TO GET THE MOST OF DATA INTEGRATION

Most organizations are using tons of applications, systems, and data warehouses. They may wish to connect to the systems of their business partners, for example, manufacturers, suppliers, logistics companies, customers, etc. 

These data sources are often disparate and siloed, making it nearly impossible to make better sense of all the available information. It is vital to connect the data from disparate sources with each other to optimize the value of the gathered insights. 

Only when this information is available in a single place in real-time for all the right stakeholders, you will be able to streamline processes and improve customer service.

The benefits of an integration between an organization’s ERP and CRM systems are plentiful. Sales representatives put a significant effort into making every prospect or customer satisfied. They want to know all about their needs and requirements.

Turning the vendor-customer relationship into a partnership is the most efficient sales approach. However, it can be challenging to give salespeople an accurate 360-degree view of their clients. 

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.

 

Integrating these two systems can yield much more substantial benefits from an operational standpoint.

• Integrating ERP and CRM is the only way to get a complete 360-degree view of your customers. From prospect to sales and support, to finance and accounting, together, these systems provide complete visibility into your customers’ needs, buying habits, order history, preferences, account standing, etc. 

You will get better insight into your customer base, and you can use it to build lasting relationships with customers and determine where there is potential for future growth.

• You gain tremendous efficiency because you have instant access to critical information. Always having access to business-critical information in real-time (whichever system you are working in) means that you can quickly and accurately respond to your customers’ and other stakeholders’ questions and requests. 

For example, about inventory levels, shipments, customer financials, order history, returns, payments, pricing, etc., without having to search for answers in multiple systems. You avoid wasting your time finding information, and you improve customer experience.

• You get rid of manual routines that are time-consuming and prone to human error. You also eliminate redundant data entry and have a chance to streamline processes and automate workflows, enabling you to increase response time to your customers.

What are the different types of data integration Techniques?

It would not take long to convince any C-level executive about the benefits of integrating these applications. Deciding how to perform the integration can be a more difficult decision to make. This requires a good understanding of what data integration methods and approaches are and which fit one’s organization 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. It is important to keep it simple. 

The most common ones can be grouped into five categories:

1. Uniform Data Access

With Uniform Data Access, you can access enterprise data from disparate sets and present it uniformly. Uniform Data Access does this while allowing the data to stay in its original location. 

It leaves the data in the source system and defines a set that can provide a unified view to various customers across a platform. There is zero latency from the source system to the consolidated view. 

If you use this data integration method, a separate store is not required for the consolidated data.

Uniform data access has limitations. You can only use this integration approach 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.

2. Common Data Storage

Common Data Storage (or CDS) is a storage space that enables you to manage and securely store data used by multiple applications or programs. Common data storage tends to copy the data from the source systems to a new system. 

Also known as Data Warehouse, this technique helps collect data from various sources, combining them into a central space and management (database files, mainframes, and flat files).

As opposed to uniform data access, CDS includes data version management and allows you to combine data from different sources (mainframes, databases, flat files, etc.)

3. Application-Based Integration

Application-based Integration solutions are specialized programs that locate, retrieve and integrate your data. Using these, you can transform and integrate data so that information from different sources is made compatible. 

Application-based Integration accesses various data sources and returns integrated results to the user. This method becomes increasingly complex as the number of system interfaces and data formats to homogenize and integrate grows. 

It has limitations if you are handling large volumes of data and large numbers of sources because it requires the applications to implement all the integration efforts. 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.

4. Common User Interface

Common User Interface means manually locating the information in each data source and comparing or cross-referencing them yourself to get the insight you need. Therefore, it is often called “manual integration”. 

In manual integration, the users operate all the relevant information to access from the source system or web page interface. This means that users must deal with different user interfaces and query languages and will need to have detailed knowledge of location, logical data representation, and data semantics.

The Common User Interface approach gives no unified view of the data. This method has obvious scaling limitations and can only be used with a minimal number of sources and a small volume of data.

5. Middleware Data Integration

Middleware is a layer of software that creates a common platform for all interactions internal and external to the organization:

• system-to-system
• system-to-database
• human-to-system
• web-based interactions
• mobile-device-based interactions

Middleware integration refers to applications that connect two or more applications. Middleware integration tools are great for connecting critical internal and external systems. 

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. APIs are 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.

Which form of data integration should you select?

Data integration methods give you a way to store, analyze, transfer, and view your data in a unified way. To decide which method suits your organization best and which data integration tool could be interesting for you, you need to fully understand your business and its processes. 

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. Your needs might be simple and require a simple data migration tool. 

They could also be very complex, and you might have to invest in a complex data integration platform. 

Retrieving data from various sources can always be a challenging task. Whichever data integration method you choose, to make it simpler, you need to ensure each application contains high data quality.

Data integration is not a simple task – but it does not need to be complex. Designing a good structure and architecture to store your data can be a tremendous task, especially in the age of big data, business intelligence, and artificial intelligence (AI). 

The amount of data collected in organizations can be quite overwhelming. According to CIO.com, enterprises, on average, can use up to 1000 cloud apps. It is often a good idea to seek help and advice from professionals.

Data integration method example between CRM and ERP

Your organization might not be one that uses 1000 applications, and your application integration needs could be a lot simpler. It does not mean that you won’t find data integration beneficial. A very common data integration scenario is that of a CRM-ERP integration. 

Most large companies worldwide have invested in both an ERP system and a CRM system – some simultaneously, some step-wise. 

 

While there is no doubt about the benefits of each system separately, integrating them is what can make the biggest difference to a company’s operations. Combined, however, these two systems can help companies take an exponential leap.

ERP-CRM INTEGRATION SUBSTANTIAL GAINS: 

• Complete visibility into your customers’ needs, buying habits, history, and preferences
• Access to business-critical information in real-time
• Avoid wasting your time finding information
• Improve customer experience
• Eliminate redundant data entry
• Streamline processes and automate workflows
• Let your users find the information they need easily and stop using alternative systems

Nowadays, there are even very cost-effective cloud-based data integration solutions that allow fast, automated, and secure data integration. Integration solutions can run as a service, and implementation time is, in most cases,, limited to 3 to 10 working days.

This makes it possible and affordable to move forward with an integration. Rapidi’s data integration tool easily integrates Salesforce CRM application with any of the Microsoft Dynamics ERP solutions using pre-configured transfers/templates.

What type of data integration approach does Rapidi use?

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. 

Rapidi allows you to quickly integrate Salesforce and Microsoft Dynamics ERP. It has been designed to enable direct integration, so your data will never get stuck between your 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 is a unique technology that ensures secure data communication between Rapidi and your on-site systems. It resides in your network and automatically compresses, and SSL encrypts data before transferring, making it practically impossible to decipher.

Rapidi Data Integration Solution is an innovative IpaaS integration solution that is three-fold in its simplicity: in design, onboarding, and use. It is fast yet flexible, secure, and robust.

Learn more

Follow us on LinkedIn to learn more about Rapidi Data Integration Solutions, and download this e-Book for additional information about Rapidi.

Key Takeaways

A robust data integration strategy is essential for businesses to derive compelling insights from their data. The key to this strategy lies in creating a unified data source system, where data from disparate systems is integrated into a single source or a source of truth.

This ensures that business users have access to a complete picture of company-wide data, enabling them to make informed and data-driven decisions.

One of the core components of this strategy is the use of virtual databases and cloud platforms, which act as a central location for data. These modern systems provide real-time data integration, ensuring that the latest information is always available for analytics-ready data.

Common storage integration and uniform access integration are two methodologies that help in consolidating data into a consistent format, making it easier for business users to extract actionable insights.

 

The integration of disparate data sources into a single database or a central storage location not only aids in reducing maintenance costs but also in enhancing the performance requirements of the systems involved.

Hybrid cloud environments and cloud-based environments are increasingly being used to facilitate this integration, offering scalable integration architecture that meets various business requirements.

API-based integration and native API integrations are becoming the norm in modern systems, allowing for seamless communication between systems. This minimizes the need for manual intervention, such as the use of CSV files for data transfer.

Enterprise data replication and complex transformations are also critical components of the integration pipeline, ensuring data consistency across systems.

However, integrating separate applications and operational systems requires technical expertise, particularly when dealing with complex transformations and the replication process.

The integration initiative should include rigorous data cleansing steps and quality checks to maintain quality standards. Batch processing is often employed in scenarios where real-time integration is not feasible, though it requires a more hands-on approach.

 

For businesses to achieve successful data integration, it is crucial to understand their integration requirements fully. This involves mapping out the key steps in the integration pipeline, from sourcing data from disparate systems to processing it into analytics tools.

Integration technologies like middleware and API integrations play a pivotal role in this process, facilitating the transfer between systems and enabling a more efficient and scalable architecture.

Ultimately, the goal of data integration is to provide business users with a unified interface, offering a clear and comprehensive view of the data. This aids in making critical business decisions and generating compelling business insights.

Whether through manual methods or more advanced automated processes, the end objective is to align the integration strategy with the company’s business objectives, ensuring that the data is not only integrated but also aligned for maximum utility and efficiency.