Top 5 Data Integration Best Practices


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When it comes to pulling, transforming, and loading massive volumes of data siloed away in multiple data sources into a central repository, you have to set up a data integration process. Modern-day businesses need a solution that can effectively handle varying workloads, scales on demand, and offers an intuitive user interface. For it to work effectively, you have to follow a set of data Integration best practices, whether you are building a custom solution or buying one.  

No sweat, we have compiled a complete list of common mistakes to avoid and best practices to implement for your data integration process.     

Table of Contents

Most Common Mistakes in Data Integration

Searching for and implementing an optimal data integration solution is a challenging feat to achieve. You might miss out on a few things in the hush and rush of opting for a solution. Worry not. Here’s a short list of common mistakes you can avoid while selecting a data integration solution: 

Not Enough Research on the Build vs. Buy Approach

If you are only using a couple of data sources and need to perform data replication rarely, then the manual effort & time required to build custom pipelines is justified. However, when complex data from multiple data sources need to be updated every few hours, you have to be aware of the ROI of burdening your engineering team. In this case, automated no-code tools like Hevo Data can be used as an efficient & economical solution. 

Overlooking the Core Business Needs

There is a swarm of data integration solutions out there using different approaches and offering unique features. To ensure that the primary needs of the firms are met, business users need to be aware of the basics of how the solution extracts data, transforms, and loads it to a destination. Is it offering connections to all your sources? How secure is it? Can it perform the required transformations and load data to your destination at a frequency your business needs? Questions like these that cater to your business requirements need to be covered before onboarding any solution.

Only Focusing on the Short-Term Benefits

As your business grows, the rise of data sources is inevitable. The amount of data that needs to be handled also increases exponentially. Hence, when looking out for a solution, you can’t just think of the current data volume and sources. The solution should be able to scale economically with your business as well as handle fluctuating workloads without compromising on performance.

Forgetting the Business User

A dataversity article reported that around 41% of business users find Data Integration technologies complex to use. The non-tech, i.e., the business users, need a solution that requires minimal technical knowledge while operating it. Firms need to shift their focus towards a beginner-friendly UI that doesn’t need coding and allows business users to quickly set up the data integration process in just a few clicks. 

Benefits of Having a High-Quality Data Integration System

Implementing data integration best practices for setting up a quality data system allows you to reap the following benefits:

  • Saves Time: Manually integrating and cleaning data requires several hours. Also, manually loading massive volumes of data from several sources is a time-consuming process, thereby causing delays in decision-making. Automated tools and solutions can swiftly handle large amounts of data and can provide near-real-time data integration with minimal downtime in case of a pipeline issue.
  • Maintains Data Quality: Without manual intervention, the chances of error are reduced significantly. Pre-defined data quality approaches ensure data coming in multiple formats is consistently integrated together and replicated without any data leakage.
  • Scalability: A well-thought data integration could cater to all your varying workloads and scale when the data sources and amount of data increase. 
  • Enhanced data accessibility: A data integration system can make it easier for users to access and use data from different sources, improving the accessibility of the data. By providing a single, unified view of data from multiple sources, a data integration system can help improve decision-making by giving users a more complete and accurate picture of their business.

Also, read Benefits of Data Integration, for detailed information.

Data Integration Best Practices

To have a robust data integration system at your organization, the following set of data integration best practices can be implemented:

  • Define Clear Long-Term Business Goals: Before opting for an integration solution or platform, you should have clear visibility of the short and long-term business objectives that this data integration will help you complete. Have a thorough analysis of the ROI you will get out of the solution and how it will remain beneficial in the future when your business scales.
  • Choose the Right Data Integration Tool: Selecting a data integration solution should be done by taking into account factors such as the size and complexity of your data, the types of data sources you are working with, and the performance and scalability requirements of your system.  
  • Go with Simplicity: Data integration is a complex process that can be quite difficult for business users to understand. One of the good data integration best practices is to select solutions that allow non-tech savvy users to quickly get started and debug a problem with minimal assistance from the IT/Engineering team.
  • Understand the data: Thoroughly understand the data sources being integrated, including the data structure, format, quality, and any potential issues or challenges. Ensure that the data is consistent and reliable throughout the data integration process, i.e., starting from the data sources to be integrated and loaded to the destination.  
  • Assign Roles and Responsibilities: An enterprise-level data integration system has different parts to be handled by specialists. As one of the important data integration best practices, assigning specific roles and permissions to users can streamline coordination and improve overall effectiveness.  

Final Thoughts

Keeping the mistakes in mind and implementing the above-mentioned data integration best practices, you should be able to successfully run a data integration system in your organization. Based on your business needs, you can opt to build a system from scratch or simply go for an automated tool. If it is a one-time replication, then a manual or custom solution makes more sense. However, if your business team needs complex data from various sources frequently that needs to be transformed into an analysis-ready form, you can try cloud-based ETL tools like Hevo Data, which offers 150+ plug-and-play integrations

Visit our Website to Explore Hevo

Saving countless hours of manual data cleaning & standardizing, Hevo Data’s pre-load data transformations get it done in minutes via a simple drag n-drop interface or your custom python scripts. No need to go to your data warehouse for post-load transformations. You can simply run complex SQL transformations from the comfort of Hevo’s interface and get your data in the final analysis-ready form. 

Want to take Hevo for a spin? Sign Up for a 14-day free trial and simplify your data integration process. Check out the pricing details to understand which plan fulfills all your business needs.

Share your experience of learning about data integration best practices! Let us know in the comments section below!

Sanchit Agarwal
Former Research Analyst, Hevo Data

Sanchit Agarwal is a data analyst at heart with a passion for data, software architecture, AI and writing technical content. He has experience writing more than 200 articles on data integration and infrastructure. His passion in helping data practitioners to solve their day to day challenges drives him to provide more value through content creation.

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