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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.
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.
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.
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:
Read also: Real-life benefits of Salesforce-Microsoft Dynamics integration
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:
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:
Read also:
The best way to build your data integration architecture
Top 5 reasons why data integration projects fail - and how to avoid failure
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