Four Essential Drivers of Cloud BI
These days, an enterprise’s ability to respond to change in a cost effective manner still has a “sink or swim” impact on its success. That’s probably why in recent years, there’s no shortage of talk surrounding business agility.
Cloud computing has emerged as a major driver of business effectiveness, as it enables the automated scalability of IT resources at a moment’s notice, in response to internal or external demands. Inevitably, when full resources aren’t required, this drives costs savings and delivers an immediate ROI.
Business intelligence is also a mainstay of business agility, as it dispenses massive insight into company data, along with the ability to make strategic decisions and forecasts in an up-to-the minute, on-the-fly fashion.
As the assets of these two technologies merge in the form of cloud BI, it’s easy to see why their popularity is on the rise— over one third of companies deploy cloud components in their business intelligence strategy. Such agility has a compelling impact on performance. It’s fair to say that cloud computing is fast becoming a veritable business strategy, based on the following drivers:
1) Reduced overall IT costs
2) Up-to-the-minute flexibility
3) Speed of implementation
4) Hardware and software maintenance reduction
Examining these drivers in greater detail leads to some pretty interesting findings, which we’ll discuss below:
1. Reduced Cost
Departments often put off getting the BI system that they know will enhance their performance, because they dread having to go through the capital expenditure approval process, not to mention absorbing the costs of pricey hardware and upgrade costs. They also don’t want to have to face the challenge of reallocating staff to manage BI infrastructure.
The carrot that cloud BI dangles is certainly one of reduced costs and headaches—both are viable arguments in favor of cloud BI.
But keep in mind that costs are primarily reduced when it comes to hardware. In terms of actual software costs, phrases like “reduced cost up-front” or “quicker time to ROI” are actually closer to the truth.
For instance, many hosted BI solutions offer cloud BI licensing as an option and many cloud BI solutions are SaaS (software as a service) solutions. User licenses are paid for on a monthly or yearly basis and companies don’t truly own them; they simply pay for use of the software. The applications are hosted outside of the enterprise and accessed via the internet.
Companies that can’t afford a capital expenditure for the upfront costs of BI software licenses often opt for this model. But once the numbers are crunched, it’s clear that after paying their monthly fee for two years, they would “break even” on what would have been spent on an up-front software license purchase.
Flexibility is all about the ability to match service capacity tofluctuating demands of business users, as well as the capacity to select from a wide variety of cloud BI deployment models.
Cloud computing has the edge in malleability, in that it allows resources to be scaled high or low, depending on a company’s current needs. With SaaS BI, it’s easier and more cost-efficient to add users to an application, compared to the process involved with traditional on-premise. With the SaaS model, whether you are adding 5 or 500 users to a dashboard, you simply pay for an additional “seat” and supply the user(s) with appropriate credentials for accessing the dashboard, using a web browser.
On the other hand, adding a user to an on-premise BI application requires installing the software on the local computer, perhaps the need to upgrade the computer, and then synch the application with the other computers on the network. It’s definitely a more cumbersome, if not expensive, process.
With technologies like Amazon Redshift, a cloud data warehouse solution, managing vast amounts of data in the cloud becomes not only flexible, but scalable and affordable. Users can access any number of servers on demand and scale BI deployments up or down as needed.
And at less than $1,000 per terabyte per year, Redshift is a fraction of the cost of most traditional data warehousing solutions. It can easily be deployed as part of a cloud BI solution, where some or all of the BI stack is deployed in the cloud.
For example, a company may choose to house their data in the cloud with Redshift and also choose a SaaS BI provider (full cloud environment). Or they may use Redshift for data warehousing and deploy an on-premise BI solution (hybrid cloud environment.)
They also have the option to keep their data warehouse on-premise, but go with a SaaS BI provider, which is another kind of hybrid cloud environment. Lastly, they might use Redshift, plus BI that’s not SaaS—but is hosted in the cloud (marrying cloud + a traditional BI license model that’s simply deployed on cloud servers rather than in-house servers.) if those options aren’t enough to get you thinking, there’s also the choice of cloud servers that are public, private or hybrid.
You get the idea – there is a multitude of choices regarding cloud BI deployment models! But of course, it’s not a black and white choice. Items like sensitive financial data, is likely to be hosted on-premise, while other data, like that contained in a dashboard application, can be kept in the cloud. Whatever an enterprise’s requirements, the vast flexibility of cloud computing makes it clear that there is an appropriate cloud model that will, in fact, be appropriate.
3. Speed of Implementation
The fact that cloud BI can be implemented much faster than a traditional on-premise solution stands as a key factor in the IT decision making process. Cloud BI over on-premise translates to fast environment availability without the hassles of acquiring infrastructure and the delays associated with software deployment. Removing these obstacles delivers an immediate benefit in terms of reducing the duration of the BI implementation, and of course, time saved is an immediate pay-off for most businesses. But organizations should also keep in mind that customized SaaS BI solutions can oftentimes be more complex, especially with applications that arrive pre-configured.
4. Hardware/Software Maintenance Reduction
On-premise infrastructure for business intelligence includes data warehouse appliances, BI servers, and human capital to manage and maintain hardware. For some organizations, it may be more cost-efficient to outsource these tasks to an IaaS (infrastructure-as-a-service) or PaaS (platform-as-a-service) cloud provider. This enables the enterprise to run large-scale data analysis for a fraction of the cost it would take to house and maintain the necessary physical and human capital internally. Since the need to configure, optimize and update hardware and software is handled externally, choosing a cloud BI model where infrastructure is the cloud provider’s responsibility turns out to be a more affordable option, cost-and time-wise.
The number of companies with cloud components in their BI stack is on the rise. As trust in cloud computing evolves and more businesses seek a nimble infrastructure, Cloud BI makes it possible for smaller organizations to get into the game—namely, businesses that don’t need all the extras of fully customizable platforms or don’t have an IT staff that can maintain BI in-house.
Keeping the above factors in mind, any option that sanctions the rise of analytics certainly scores our nod of approval.
Hope you enjoyed reading this blog.
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