Data integration is integral to business intelligence (BI) operations. Companies compile massive amounts of information from various sources and need reliable to collect, format, and disseminate it. Integration projects enable companies to format raw data for various analytics operations.
If it sounds complex, that’s because it is. It’s labor-intensive, time-consuming, and costly. Moreover, flaws in the process could have catastrophic consequences. It takes data scientists and business users working hand-in-hand to undertake these projects successfully. Following the best practices in this article will also help make your integration operations run smoothly.
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Ensure your data integration team understands their roles.
As we previously stated, data integration projects are team projects. Every member of your integration team must have a defined role with functions that play to their strengths. Be strategic about who you add to the project. It’s even better if you already have a dedicated team who’s worked on projects together before. They’ll have better chemistry and maybe even their own language to make communication easier.
Speaking of communication, it’s one of the keys to your project’s success. It’s not enough for everyone to merely have a role. Team members must understand their roles and how doing their jobs affects other team members in their roles. Communication must be constant and concise to ensure the operation’s integrity.
Determine the purpose of your integration project.
You read in the previous section that it’s important for the members of your integration team to know their roles. That means you must have a well-defined goal for your project.
There are numerous use cases for data integration projects. One of the most common projects is transferring on-premise data to the cloud. Putting company data in the cloud enables companies to make it accessible to members of teams abroad and partners. Another common integration use case is real-time data warehousing. A data warehouse is similar to a virtual library for data in that it formats and stores data in a logical environment.
Collecting data from various sources for analytics is another prominent reason for integration projects. Data analysts choose different source systems from which to collect data. Ultimately, knowing the purpose of your project will help you determine the right tools and strategy to employ.
Choose the appropriate data integration tool.
Knowing the purpose of your operation will help you determine which data integration platform employ. Using the right data integration platform is like using the right tools and English instructions to put together a Scandinavian bookcase. You might get done if you have the wrong tools and the instructions are in Scandinavian. However, the right tools and instructions you can understand will save you a week’s worth of sleepless nights.
Data virtualization platforms are one of the most powerful data virtualization tools. Virtualization platforms collect data from internal and external disparate sources without removing the data from source systems. The virtualization process is great for analytics operations ranging from predictive analytics to visual analytics. TIBCO, a leader in data science, strives to make data virtualization tools that are easy to use and provide the near-real-time insights small businesses and large corporations need.
Plan for future needs.
Plan your integration project with your company’s future data needs in mind. You can even use predictive analytics to determine what future needs will be. Use your current project and what you learn from it to produce optimal results on future projects.
Data integration projects require time, effort, and teamwork. It’s integral to the project to make sure everyone on your team knows their role and communicates effectively. Additionally, choosing the right data integration platform will help streamline your project and improve ROI. Data integration will always be a complex process, but this roadmap will make the journey a little easier.