Selecting the right data integration product is critical to meeting the increasing demand in companies for data that can help drive more informed business decisions. The tool you choose to integrate and translate this data into information that can generate actionable business insights must fulfill your organization’s requirements. Otherwise, it will become expensive, unused shelfware. Even worse, custom manual coding of integration scripts — with all its downsides — will prevail.
The data integration product evaluation process starts with gathering and prioritizing requirements such as source and target systems, the types of data you have to pull together and the forms of integration that will be needed. There can be a lot of variables in those requirements. For example, you may have a mix of structured and unstructured data to integrate. And the data integration platforms now offered by vendors support a variety of integration use cases: extract, transform and load processes; application integration; cloud-based and real-time integration; data virtualization; data cleansing; and data profiling.
Once you have the requirements in hand, you can move on to creating a list of specific features and functions to evaluate products against. Ultimately, your organization needs to select the data integration tool that’s the best fit for its use cases and budget, and one that can be implemented given your enterprise’s resources and skills — not necessarily the most feature-laden product, or one deemed the best by industry analysts.
(This is the third article in a four-part series on data integration platforms. The first article laid the groundwork by identifying the types of tools and how they’re used. Article No. 2 examined specific use cases for buying a data integration tool. This article is designed to help you determine which features are most important to your organization. The concluding article will examine the leading commercial data integration platforms, comparing and contrasting their features.)
(Learn more about data integration and all things BI in my Business Intelligence Guidebook – From Data Integration to Analytics. Chapter 11 is on data integration design and development and chapter 12 is on data integration processes. )