Don’t waste time and resources on creating what’s already there.
Extracting and normalizing customer data from multiple sources is the biggest challenge with client data management, according to the Aberdeen Group. OK, true, a lot of companies consider linking and migrating customer information between databases, files and applications a sticky, if not risky, process to deal with. Gartner says corporate developers spend approximately 65 percent of their effort building bridges between applications. That much! No wonder they risk losing lots and lots of data, not even mentioning the time and efforts this may involve. Why spend time on creating what’s already there?

Do
Find an integration provider that suits you. There are plenty of vendors. Of course, there isn’t a universal integrator that would suit everyone, as each tries to cover a a certain area and solve a particular problem. So, you just need to spend a bit of time looking for the right vendor.

Don’t let expenses frighten you.
In today’s enterprises, most data integration projects never get built. Why? Because most companies consider the ROI (Return on Investment) on the small projects simply too low to justify bringing in expensive middleware. Yeah, so you have your customer data in two sources and want to integrate (or synchronize). But then you think “Hey, it costs too much, I might as well leave everything as it is. It worked up till now, it’ll work just as well in the future.” Then after a while you find yourself lost between the systems, the data they contain, trying to figure which information is more up-to-date and accurate? Guess what? You’re losing again.

Do
If ROI is an issue, consider open source software. With open source data integration tools you could have your pie and eat it too. Open source can offer a cost-effective visual data integration solutions to the companies that previously found proprietary data integration, ETL, and EAI tools expensive and complicated.

Not having to pay license fees for BI and data integration software should make companies previously scared of insufficient ROI return to the data integration market.