Seven Data Virtualization Keys
Consider applying seven secrets practiced by your enterprise counterparts to make your own advanced data virtualization projects and architectures successful.
Remember "The Six Million Dollar Man"? "Gentlemen, we can rebuild him. We have the technology. We have the capability to build the world's first bionic man. Steve Austin will be that man. Better than he was before. Better, stronger, faster."
I've been thinking about that introduction a lot since I shattered my wrist in May. They gave me what the doctor called "a bionic arm," which - I was disappointed to see - was nothing more than a metal splint with a lot of pads, velcro and a joint so I could move my elbow. No lifting cars for me. Bummer.
That's how things go so often with technology: It always sounds so promising, but fails to deliver on that vision.
That's part of the reason why less than 20 percent of IT departments in North America and Europe are using data virtualization and why even fewer realize its potential, according to a recent ComputerWeekly article quoting the study's author, Forrester analyst Brian Hopkins.
Hopkins believes that's on the verge of changing, thanks to improved data virtualization products that include better performance and the type of integration capabilities IT knows and loves - including connectors for third-party data sources and security measures such as data masking.
Forrester predicts interest in data virtualization will grow over the next 18-36 months, largely because it addresses the weaknesses of other integration options. A TechTarget article quotes this explanation by Hopkins:
Integration by ETL creates data quality problems and delays information delivery. Integration by DBMS consolidation is high impact, expensive, and risky.
So how can you get started with data virtualization? Forrester recommends three baby steps, according to TechTarget: