“Cloud” and ”SaaS” (software as a service) are two terms often bundled together and used to describe the same thing. The way we at QlikTech use the term 'cloud' is to refer to the use of off-premise, often distributed, computing environments for the purpose of running applications and managing, storing, and analyzing data. We use the term 'SaaS' to refer to a specific distribution and implementation model for applications running in the cloud (software running on multi-tenant servers in the cloud and vendors charging customers a subscription fee to use the software).

QlikView and The Cloud:

QlikView can be deployed on premise or in the cloud. In fact, QlikTech’s own demo site (http://demo.qlikview.com) runs on servers in the Amazon EC2 cloud. Customers that want to put QlikView on servers in the cloud purchase licenses from QlikTech just as they would if they were deploying the software on premise. This is an attractive solution for customers that:

• Do not want the upfront capital costs of acquiring server hardware
• Need a flexible environment that can easily scale as their deployment needs scale.

What’s important to note is that in cloud-based environments the data resides in the cloud. This somewhat obvious point is an important one, particularly with respect to business intelligence solutions: The “raw materials” for any successful BI deployment are the data that underlie the deployment. The location of this data will often determine whether a BI solution is brought in house or deployed in the cloud.  If data is on premises and your analytics are in the cloud, then potentially large data sets must be uploaded to the cloud for analysis. If your data is in the cloud, then it still needs to be transferred to your analytic environment. Of course, both of these scenarios are dependent on great bandwidth.

On the other hand, if your data already lives in the cloud, in the same environment as your analytic applications then it makes sense to use cloud analytics.

Through connectors to cloud-based offerings such as Salesforce.com, QlikView can be used to access and analyze data resident in the cloud while maintaining the strict security requirements needed. Therefore, there are no obstacles to QlikView working within a cloud environment: the degree to which it does is determined by how customers choose to deploy QlikView.

QlikView and SaaS:

Current trends in the BI platforms market indicate that BI SaaS offerings are not gaining the type of traction that other cloud-based solutions (such as CRM, for example) are gaining. According to Forrester Research, cloud BI will continue to chip away at on-premises BI, but it’s still a long road ahead. Heavy customization and integration of enterprise BI platforms, tools, and applications done by subject-matter experts and consultants will not go away. Another major reason for this is that most organizations continue to maintain their core operations data on premises. 

Currently, QlikTech has many OEM partners that are using QlikView to deliver SaaS-based BI offerings to their customers. For these partners, their customer base and business model lends itself to providing solutions via a SaaS model. For example, one partner provides a SaaS-based call center transaction solution to their customers. For an additional fee, their customers can avail of an ‘analytics module’ (built with QlikView) where they can analyze and discover insights in their call center data, to help them with resource allocation, RMA trends and so on.

At present, QlikTech does not offer QlikView via a SaaS model. (This would involve QlikView running on multi-tenant servers in the cloud and QlikTech charging customers a subscription fee to use the software). This is due to limited customer demand for such an offering. As the prevalence of cloud-based data increases, so may the demand for SaaS-based BI increase.

We continue to closely watch the growing trends in both Cloud and SaaS environments, and where appropriate to our customers' needs, will continue to provide innovative solutions to meet their Business Discovery requirements.