3 Use Cases for Hybrid Cloud BI and Analytics
Many companies, large and small alike, are already using some kind of hybrid approach to cloud BI -- even if they don't think they are.
- By Fern Halper, Ph.D.
- December 10, 2013
Although adoption of cloud BI has been slower than predicted, TDWI is seeing an increasing number of companies investigating the technology. One deployment model that is garnering interest is the hybrid cloud model. In several recent TDWI surveys, hybrid cloud adoption for BI was gaining momentum, even over private clouds. Hybrid cloud BI will become increasingly important for cloud BI in general.
A hybrid cloud is a computing environment that includes the use of public and private clouds (and even data centers) often where there are one or more touch points between systems. The reality is that many companies, both large and small, are already using some kind of hybrid approach to data analysis -- even if they don't think they are. Consider that more companies are using public cloud CRM systems such as Salesforce.com or SugarCRM and then bringing that data back on-premises and combining it with other data for analysis. That's one example of a hybrid cloud.
Another example is a SaaS ecosystem in which a company might create all of its data in the cloud using various SaaS applications. The data might also be analyzed there -- in a BI or analytics application that runs in the cloud. The applications all touch that BI application or even each other in some way.
Here are three emerging use cases for hybrid cloud BI and analytics:
Use Case #1: Reduce data in the cloud
Many companies are dealing with big data -- i.e., disparate kinds of data that might be high volume and high velocity. In the public cloud, some examples might include geospatial data or clickstream data, search data, or even social media data. Some companies dealing with big data choose to process the external sources in the public cloud and then bring the reduced (analyzed) data set on-premises to make it part of a bigger analysis. This is a good use of a hybrid cloud model. The company does the computing work in the public cloud when it doesn't need the source files for anything else. The cloud provides the scalability and flexibility to operate on these data sets.
Use Case #2: Use an analytics-as-a-service application
These are specific, targeted horizontal or vertical applications that can be called upon from the cloud (generally from the public cloud) when needed. For example, a credit card fraud application might be run in the cloud and then the results are pulled back into the rest of the analysis on premises (say, in a private cloud). Another example: a campaign management system or an analytics service that does retention analysis. One way to think about it is almost as a skills-as-a-service model, given the lack of skills in the advanced analytics space.
Use Case #3: Analytic tools and sandboxes
More companies are looking to move analytic workloads to a public cloud environment. This is especially true with predictive and advanced analytics. Organizations like it because of the scalable and elastic nature of the cloud. They can get as much capacity as they want to run models there. The data is taken off-premises from one data store or application and sent to the public cloud. There it can be combined with other data and analyzed.
For instance, certain cloud providers (Amazon is one) provide a centralized repository of large public data sets (such as the United States Census) for its clients. Companies are also experimenting in the public or private cloud on proof of concepts in a sandbox type of model. However, these sandboxes are often maintained by an IT group that can control them and make it easier to productize a sandbox.
Clearly, the cloud can be very useful for BI because it is on-demand, self-service, has rapid elasticity, and is massively scalable. The cloud can provide significant compute power for BI and analytics. That's not to say that there aren't challenges. There are, both from an organization and technology perspective. They include politics, perceived security concerns, data integration, and governance among others. However, cloud BI is slowly starting to mature as companies realize the benefits and decide if it is right for them.