Top 10 Benefits of Virtualization
Virtualization has taken a firm hold at most enterprises these days, but the fact is we've only just begun to unleash the true potential of the technology.
At a minimum, a journalist is supposed to answer four questions in every article: who, what, when and where. Yesterday, I looked at the who and what of data virtualization. Today, let's examine the when and where - and then look at one question I can't pin down.
When would you use it? Data virtualization comes in handy for all those pesky little integration projects you just can't seem to get to. In fact, Forrester Research says those projects are a great stepping stone for introducing data virtualization into your organization. If it doesn't require ETL, then you should consider data virtualization, according to a recent study by the research firm. Forrester sees it as a great alternative to both expensive data marts and ETL.
"Integration by ETL creates data quality problems and delays information delivery," wrote analyst Brian Hopkins in a recent study. "Integration by DBMS consolidation is high impact, expensive, and risky."
My answer often surprises them because this is not an either-or situation: data virtualization should be a ubiquitous layer in your infrastructure that all data consumers use to access enterprise data. ... In other words, the answer to the question is that you should (almost) always use data virtualization, and sometimes you may also need to use ETL and other data integration strategies.
So, apparently, when is somewhat open to interpretation. I think our next "W" can help shed more light on the general question of use.
Where do you use data virtualization? I haven't read anything that restricts where you might use data virtualization in terms of an organization's size or focus. I can tell you it seems to be best for those with business end users or analysts who need access to real-time data.
While I found no restrictions, I did find several examples of where it can really help with other tech-related investments. For instance, it happens to be a perfect fit for those organizations that have or are pursuing a service-oriented approach to architecture - that's right, I'm talking about SOA. Long, long ago, SOA and data integration expert David Linthicum wrote that he thought data integration was a problem area for SOA, but now he and other experts agree that data virtualization goes with SOA like peas and carrots.
"Data is more logically grouped to make that data more useful, which this has been the promise of SOA for years," Linthicum writes. "An SOA with a data services layer is a valid architectural approach to dealing with both underlying business processes as well as sets of services."
It also works well with master data management, cloud and increasingly, Big Data, according to a recent Composite Software whitepaper outlining the five most popular use cases for data virtualization.
It's apparently now popular in the life sciences field, where pharmaceutical companies are using it to gain insights into research data.
That's the who, what, when and where of data virtualization. You'll notice why isn't one of the four W's, but I think in this case, the four basic W's go a long way to explaining why you might pursue data virtualization.
So what's my unanswered question? Simple: What are the cons of this approach? Nothing is without tradeoffs, and while a recent TechTarget article briefly mentioned concerns about security, privacy and regulatory compliance, I couldn't find anything that really drilled down on these issues. That just doesn't sit well with the journalist in me. Hopefully, we'll soon hear more about the caveats to data virtualization as the hype cycle progresses, so you can go into it with not just the who, what, when and where, but an idea of the problems you might encounter.