Data Integration Best Practice | 2 mins read

Data Integration Best Practices & Common Mistakes to Know

data integration best practices common mistakes to know
Chloe Henderson

By Chloe Henderson

By following data integration best practices, businesses can prevent common mistakes when choosing solutions, saving capital and frustration. Selecting an appropriate integration platform ensures employees can easily navigate through the system and data exchange is optimized.

An advanced data integration tool allows businesses to efficiently monitor internal operations with access to real-time quantities, enhancing overall data management.

What is Data Integration and How Does it Help Businesses?

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Data integration is the process of collaborating information from internal and external sources into one interface, allowing users to access all operational data in one location. Oftentimes this information, known as big data, is stored in a data warehouse outside of its original site, allowing users to perform analytics. Information that remains only within its source system is unreachable and therefore, cannot be analyzed or converted into business intelligence.

The gathered data can be quantitative or qualitative, from inside operations such as point-of-sale (POS) metrics, or from a third-party data source such as online reviews. This wide range of disparate sources collectively contributes to a well-rounded perspective of a company's overall performance, allowing management to gain a broader scope of their business.

In order to extract information, an integration platform must go through an ingestion process that filters, routes, and translates data. This ensures that all information is in a standardized format, making it accessible and readable for users.

While there are many types of data integration models available, advanced platforms such as cloud integration automates information sharing. With a modern data management tool, information is pulled from various sources and consolidated without human intervention. Without this solution, companies must designate an employee to manually pull and combine data from existing systems. This process requires a significant amount of time and increases the risk of human error.

By implementing an integration tool, organizations can reduce expenses related to traditional consolidation methods. They can also reduce the risk of human error, ensuring accurate information that promotes decision-making and data management.

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Common Data Integration Mistakes and Best Practices

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Beginning an integration project can be stressful as it may require a complete data transformation. However, businesses must thoroughly plan their implementation process to avoid common integration mistakes. Before delving into the data integration process, management should consider the following.

  • Understand Business Needs
Mistake- Many providers offer what seem to be similar data management solutions, when, in fact, they may use different integration strategies. While this may not seem important to the average user, the software's internal programming and operations determine what applications it can interact with.
Therefore, if a business adopts an integration tool that cannot translate data from its legacy system, information within these traditional services remains trapped. This leaves the company with an ineffective integrator and wasted capital.

Best Practice- Before finalizing a purchase on an integration platform, companies should consult with their IT department to outline their connectivity needs and established software. Only then can the business decipher which provider and service best meet their needs.

  • Build or Buy?
Mistake- Many businesses have the impression that developing integration software is the economical choice over purchasing a pre-programmed solution. However, building an integration service from scratch has many hidden costs, from blueprints to specialized maintenance, which can quickly add up.

Best Practice- Companies must consider the additional costs of customized software and determine if it outweighs the benefits. Often, standard solutions offer similar features and are just as productive and functional as personalized services.

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  • Consider Long-Term Benefits
Mistake- While understandable, management tends to focus on the short-term advantages of integration rather than considering the long-term benefits. Although a speedy return of investment (ROI) is ideal, data integration's longevity should be the priority.

Best Practice- As technology advances with every second that passes, integration solutions must be able to adapt quickly, or else they will become obsolete. Therefore, businesses should research services to determine if they have flexible components and the ability to incorporate evolving technologies. This ensures that they invest in a long-lasting system, preventing additional expenses related to implementing a completely new solution in the near future.

  • Focus on Software Users
Mistake- Studies show that 41% of employees find the company integration tool challenging to use. This is because many solutions are programmed with experienced IT workers in mind instead of the typical employee. However, if a software is not user-friendly, it cannot be effectively utilized to the business's full advantage.

Best Practice- Since employees are the typical users of integration software, businesses need to ensure their new system is user-friendly and can be navigated by people with limited IT knowledge.


From enhanced communication to an improved workflow, data integration offers businesses many advantages. However, there are several mistakes companies commonly make when implementing a new system due to inadequate research and preparation. To successfully invest in data management, organizations should focus on using the best integration practices proven to ensure system optimization.

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