Today, most businesses depend deeply on data. Data drives everything from customer support to business strategy. Why is that? Because businesses are flooded with an unprecedented number of data sources and types: legacy systems, sensors, log files, mobile devices - all of these sources here to generate data that can provide them with a competitive advantage. That’s the good news. The bad news is that someone needs to make sense of this overwhelming amounts of data. Without drowning. This is where data integration comes into play. But what is data integration and how is it working? Let’s get you acquainted with the basics of data integration.  

What is data integration?

Simply put, data integration is about combining information from various sources into something useful. Data integration combines data from different sources and provides users with a unified view of them. For example, take a website where a user can search for a variety of information about cities (traffic statistics, weather, hotels, demographics, etc.). The information may come from different sources/databases (traffic statistics, weather statistics, etc), but it has to be consolidated in order to be used in one single application.

Anybody who’s tried to fetch information from multiple systems for reporting purposes understands the importance – and the complexity - of data integration. When a business grows, new locations, new revenue streams and changing priorities will increase the amount and affect the form of its data – making data integration more and more important and more and more complex.

How is data integration best handled?

The need for consolidating data has existed for a long time – much before modern data integration tools were developed. At the time, integrating multiple data sources typically meant a lot of ad-hoc hand coding between different data sets, which resulted in an expensive solution, with difficult maintenance. Often, these integrations were developed from scratch in-house or by a partner. They were poorly documented, and if the developer who developed them left the company, updating or modifying the integration was a nightmare. My advice to you: if you can, avoid hand coded, homemade solutions!

Read also: Data integration platform vs custom integration: Which way to go?

Today, modern data integration tools have been developed to handle the data collecting, data transformation, and data provisioning functions in an efficient, transparent and highly adaptable manner. Data integration systems such as RapidiOnline come out-of-the-box with a number of pre-configured integration points between pre-defined systems (for example Salesforce’s CRM and Microsoft Dynamics’ ERP). This is ideal, as in reality, companies that wish to integrate two (or more) systems always have common ways of working and therefore similar requirements.

How can companies benefit from data integration?

The benefits of 2 systems (for example an ERP and a CRM) taken separately are undeniable, and can by themselves make a huge difference in a company’s operations. Combined together however, these two systems can potentially help companies take an exponential leap.

Let’s take the ERP-CRM integration example. Companies typically benefit from a CRM-ERP integration by:

• Reducing manual data entry (prone to errors), which frees their resources to focus on value-adding tasks.
• Optimizing and automating processes , for example their sales process, by giving real-time, instant access to business-critical data.
• Providing with a true 360-degree view of customers . The better insight they get into their customer base, the better they will be able to build lasting relationships with customers and determine where there is potential for future growth.
• Securing high system ROI , via sky high user adoption. By securing that the two systems are well integrated, companies make sure that their users will always find information in their systems and will never have to go back to alternative ways to find data to perform their daily tasks.

Read also: Real-life benefits of Salesforce-Microsoft Dynamics integration

Why is data integration challenging?

Many of the companies that embark on a data integration project find themselves faced to serious challenges. Typically, it is because the complexity of a data integration project typically grows with:

• The number of systems used : Data volumes tend to increase with the number of data sources you have. Some enterprises having nothing less than hundreds of systems, running on multiple platforms (for instance a combination of on-premise, Cloud and private hosting), in various versions and different geographical locations. When data resides in so many silos it adds a whole new complexity and security dimension to a data integration project.
• The customizations made to the systems : Although today many systems and applications come out-of-the-box with role-tailored functionality, most implementation projects include extra customization and development efforts to support enterprise-, division- or user-specific working processes and habits… This can result in hundreds of custom modules or features; literally a maintenance and upgrade nightmare, but also quite a challenge when you have to integrate the different systems.
• The lack of consolidated approach to data integration : When data integration is approached as a multitude of point-to-point custom integration scripts without a common direction, then the data integration project is doomed to fail to deliver the desired business critical single view of data. Data must be synchronized in an automated and reliable manner across all platforms for a company to have one version of the truth. Errors caused by inconsistent, data and manual data entry can prove very costly for companies and disrupt business activities.

Read also: Top challenges of data integration projects

Top criteria to keep in mind when selecting a data integration solution

The above-described challenges can easily be if you choose the right data integration solution. Making the right choice is then vital, so that you can bring all these elements together and deliver the desired one view on your data.

When evaluating a solution, you need to make sure that:

• The solution is proven, stable and reliable.
• The solution will be easy and quick to build.
• The solution is easy to understand on a technical level, and intuitive and simple to use for your business users.
• The solution is able to connect to your various data sources and be able to consume your various data types.
• Mistakes and errors are inevitable. The solution needs to be able to monitor data integration transfers and proactively alert your administrators when an error occurs.
• The solution can scale with your business and the addition of new data sources.

Read also:

The best way to build your data integration architecture
Top 5 reasons why data integration projects fail - and how to avoid failure