Even in established organizations that have established on-premises systems they're looking at building new cloud BI applications and particularly around analytics often to take advantage of the data gravity. The gravity being that lots of the data already in the clouds.
They want to move analytics to the cloud and take advantage of it, as I mentioned here, point technology using in database processing. You've got powerful platforms out there. Why not use them effectively for churning through analytics and even for data preparation, ETL kind of activities to support analytics. We see a lot of trending towards that being able to use the parallel processing and more powerful platforms in the cloud for this.
Then the other thing to mention about Cloud of courses is it's not one Cloud but many Clouds and multi-cloud. We see most organizations are using the big ones: Amazon, Microsoft, Google, but certainly even others, multi-clouds sometimes. It just works out that way that they're moved into the Cloud in different parts of the organization, moved to different Cloud systems, or it's by design because they want to avoid vendor lock-in and platform lock-in and have some flexibility there.
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This is certainly a feature of the Cloud these days that it isn't necessity of having analytics. It will be portable and going to solutions that can support multiple different clouds so not just one, but certainly the big ones out there if not others as they come along. Take advantage of the Cloud infrastructure.
Here I'm kind of grouping together what's going on really: going into the development world which is certainly changing quite a bit around Cloud. This is around the use of embedded BI systems and or rather embedded analytics in API containers and virtualization and really moving into this kind of a more virtual world around using microservices and things like that for managing and orchestrating the use of containers.
We're starting to see embedded analytics systems begin to make use of that in the Cloud. First we'd talk about really just the Cloud platform impact and why that's important in terms of the power and the scalability and the dynamism. But now we're looking at how these applications are constructed and how they're put together and interact with each other.
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Basically there are features and functionality that can now more easily put together and integrate and as sort of as components to create the impediment bedded analytics. This is a very important development in analytics. Using APIs containers, using a more virtualized kind of idea around containers and then using the standard's very important so that it's easy to plug in different functionality that you need.
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Then the other benefit about the cloud is also being able to control releases and upgrades. We certainly are seeing that with commercial BI software vendors, but there's no reason why organizations themselves as they manage embedded analytics couldn't be doing the same and being able to do it more effectively from a centralized posture, rather than having to go to each individual embedded information system, which takes a tremendous amount of time and effort from IT standpoint.
It's great for third party developers and OEM providers which have adopted embedded BI solutions. As the third party developers have accumulated a particular expertise, a subject matter expertise or business process. Well there's increasing demand for those kinds of third party systems to have intelligence and insight, to be able to have the kind of use of data that's effective through dashboards, through a drag and drop visual interfaces, to provide access to different types of data than may have been in traditional embedded systems, to have more, in other words, more breadth around the data.