Cost savings and operational efficiencies have always been the main motivations for moving data management to the cloud. Data resides in, and is processed in, the unlimited cloud space, eliminating the need for costly on-premises data centers with high maintenance costs and limited capacity.
At least, that’s long been the common belief. Unfortunately, it’s too good to be true, as is becoming apparent. The majority of respondents to a 2016 Gartner survey said that using the cloud had actually increased their operational expenditure.
“ Proactive budget controls are the first step toward managing cloud budgets and enforcing predictable spending”
“Data and analytics leaders who use cloud infrastructure for analytics, data lakes and other data-intensive environments are often surprised at the size of their monthly cloud spending,” says Adam Ronthal, research director at Gartner. “The main reason for this is that on-premises data centers and the cloud work according to different economic principles. On-premises, you tend to maximize utilization, as capacity and cost are fixed. In the cloud, it’s the other way round: Users must aim to consume as few resources as possible, as they will be charged for every single computing action.”
This doesn’t mean you should shun the cloud altogether — there are options for cost optimization in the cloud that don’t exist on-premises — but it does require data and analytics leaders to apply three financial governance controls to their cloud environments.
Most cloud service providers (CSPs) already provide options to apply budgets and quotas at both account and individual-project level. There are native budgeting and forecasting services with varying degrees of integration for existing data management solutions. Data and analytics leaders should educate themselves about the different options and set budgets for their various cloud activities.
“Proactive budget controls are the first step toward managing cloud budgets and enforcing predictable spending,” Ronthal says. “However, native controls are often very basic, as CSPs focus their attention on services that offer them more value.”
After you set budgets, decide what to do when budgetary capacity is exceeded or unexpected spending occurs. Options range from sending a notification to the resource owner to more drastic steps like throttling back capacity until the next budget cycle or blocking access to the exceeded resource.
In addition to budgeting and forecasting capabilities, many CSPs provide features at the service offering level that enable finer control of resource allocation — and consequently of the cost of resources. An example is the separation of compute and storage resources; if users can independently scale these, there are many ways to realize cost savings.
“ ISVs have a more immediate interest to build cost optimization and financial governance controls than CSPs do”
Much of the integrated functionality for tracking and budgeting within CSP frameworks is still in its adolescence, and often requires significant customization. Independent software vendors (ISVs) have seized the opportunity that this relative immaturity presents by building additional capabilities for financial governance directly into their cloud-based offerings.
“In general, ISVs have a more immediate interest to build cost optimization and financial governance controls than CSPs do. This accounts for some of the more advanced tools in some ISVs’ data management offerings;” Ronthal says.