Ian Pestel: Well yes that’s really a good point, and naturally yeah the monitoring can actually be through a process. So for example we have one customer, a big pharmaceutical company, and they have to provide data to the business intelligence community, people from a variety of sources. And one of the problems they were encountering was that they were trying to provide data out there, and there was a need to provide it very quickly. But also when they provided it, often there were problems where the data people were looking at had gone away.
So I want to ask you now a different question. Often they were depending a great deal of time on using very traditional data warehousing techniques just to make data available for the business. So what we do now is they actually use data virtualization, and they can deliver the data very, very quickly to the business.
Then they can monitor it, and actually then check the data services that are being used, are actually being used as the business intended and to the amount of the business requested. And once they have monitored that quickly, as it was actually three months, they then start to then look at actually putting value to the bigger enterprise data landscape. So in some way that’s going through a triage process about what data was being used and who is using it and when its being used. Data virtualization gives you that capability to do that process very, very quickly and also, as you say, to monitor it through an accomplished process.
Eric Kavanagh: Yeah, good stuff, good stuff. So what are some of the other mega trends that you have seen? Speaking to what Philip was talking about earlier, are there any? I mean he is talking about the need for more real time data. I am guessing that you are seeing that kind of thing, right?