This is the second in a series of posts about the topic of data federation. Click here for the full series.

In the previous post, I described the basics of data federation and how the technology can be useful in today’s complicated IT environments. Next, let’s apply data federation to some real-world problems and examine some ways that data federation can save the day.

Here’s a quick scenario: a business just acquired a new company and is excited about a new customer base. Naturally, the marketing department wants to examine the list of customers from the newly acquired company and compare it with the list of existing customers. That way, they can get a view of how many new customers were added through the acquisition. After all, the answer can make the difference between a “good buy” and a “great buy” in the minds of executives and shareholders.

To find this answer using traditional IT methods, a company would merge databases from both new and old companies. This is harder than it sounds, however. Anybody familiar with most enterprise architectures knows that customer data is often housed in multiple applications or data warehouses, not in a single source within an organization. So, there could be dozens of pools of customer information – from both companies – feeding this integration project.

Embarking on a batch-oriented data integration program to solve this situation is costly and would divert resources from more immediate IT projects. Additionally, it will take weeks (maybe months) to finally integrate all this data and create a unified customer view.

Data federation provides a faster way to find the answer. By creating a virtual view of the various sources, you can bring together customer lists from the various sources and combine them to create a unified view of the customer base of both the companies. A nice presentation layer, which shows the data in business-friendly terms on top of this virtual layer, makes this a much easier way to consume this information for the users in the marketing department.

The advantages: time to value is short, and the infrastructure footprint is minimal. This ability to efficiently create a data federation layer to solve a current business problem is an extremely important differentiator against traditional data management approaches.

In the next blog, we will discuss another use case – security and compliance. Until then, leave a comment and tell me how you have seen data federation in action.