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.
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.
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
- 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.
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
- 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.
Sanchit Agarwal is an Engineer turned Data Analyst with a passion for data, software architecture and AI. He leverages his diverse technical background and 2+ years of experience to write content. He has penned over 200 articles on data integration and infrastructures, driven by a desire to empower data practitioners with practical solutions for their everyday challenges.
