Today, organizations rely on data analytics in making strategic decisions to enhance their operations and promote business growth. In reality, though advanced analytics capabilities are in high demand today, their provision comes at increasing costs as well.
A recent survey conducted by 1PATH revealed that nearly half of small and medium-sized businesses allocate between $10,000 and $25,000 for the purchase of data analytics tools, with 41% of respondents incurring similar amounts each year in maintenance fees.
As a result, sometimes these service charges can explode beyond the planned budget, and the event is popularly referred to as “bill shock.” This incident usually refers to unexpectedly high charges from the use of cloud-based services for analytics, which shoot past the budget originally planned.
For businesses that want to exploit data without getting left financially blind-sided, countermoves against these risks need to be designed and executed. Here lies the feasible approach that an organization may turn to while keeping its analytics spending in check so as not to neglect the use of data insights in these enterprises. All of these—from the manipulation of resources to budgeting techniques—will arm businesses with confidence and a clear understanding with which to face the expenses that come with data analytics.
Understanding the Causes of Bill Shocks
- Unforeseen Data Volume Growth
Operations of businesses nowadays surge exponentially with so much data being discharged from those operations—from various sources—including increased user activity, new data sources, or changes in data collection practices. The volume can attract high charges in storage and processing unless such organizations maintain clear control over their usage of data.
- Lack of Understanding of Pricing Models
A McKinsey Survey indicated an area in which organizations are woefully weak: a concerningly low knowledge regarding the application of pricing models, especially regarding communication of value and new prices to customers. The survey indicated that more than half of its respondents (57%) believed that their companies are not trained in sales to support negotiations related to price. Further, 42% reported that their organizations offered no deal-level pricing guidance.
- Overutilization of Cloud Resources
Recently, Flexera’s 2023 State of the Cloud Report reported that enterprises are spending 35% more on clouds than what is needed to accomplish business needs. Normally, overutilization of resources results from poor resource management and monitoring practices. Organizations pay for unused or underutilized resources when they are not able to successfully track their utilization of the cloud.
- Third-Party Tool Integrations
Combining third-party applications could increase the working functionality but complicate the management of cost. Each third-party application comes with a unique pricing pattern. The more solutions an organization integrates the more challenging to track every single cost on all platforms. A company may end up overspending and even miss some discounts offered through some alternatives.
- Vendor Lock-In and Escalating Maintenance Costs
Vendor lock-in can greatly affect an organization’s bottom line. The companies are tied to a particular vendor’s platform, and it comes with some form of lock-in, making it challenging to change providers or negotiate better terms. According to the yearly SaaS Inflation Index report by Vertice, 73% of SaaS vendors priced their solutions up during 2023; with dramatic spikes from notable companies such as HubSpot, by 12%; Microsoft, at 15%; and Webflow, at 23%. This sudden price growth puts enormous pressure on budgets and is worsened by spiraling maintenance costs.
Strategies to Avoid Data Analytics Bill Shocks
- Establish Clear Cost Forecasting and Budgeting
Having understood what you are going to be charged for, you will not have bill shocks on data analytics. In other words, you should develop a detailed forecast on cost, based on data volume, usage pattern, and tool/service you are going to use. Set up a complete budget to guide you in distributing resources in those different areas for your analytics operation: data storage, processing, and visualization.
- Set Usage and Spending Limits
An organization needs to set up proactive usage and spending limits so that data analytics spending can be effectively managed. These can be thresholds that trigger alerts or automated actions when spending approaches a pre-defined limit. For example, quotas can be provisioned based on cloud resources/subscriptions that prevent unwanted overages. Such monitoring by organizations instills responsibility and makes all departments concerned about budgetary constraints.
- Optimize Resource Allocation and Right-Sizing
Cloud resources are often priced based on usage. Optimize resource allocation to ensure that you do not pay for resources unless you use them. Closely monitor your workloads of data analytics and adjust the size of resources and resource types as per necessity. This is called right-sizing—finding instances where resources are too provisioned and resizing them appropriately.
- Implement Governance and Accountability
Such an organized governance framework around data analytics operations would generate far better accountability and minimize the propensity to overspend. This means putting strong policies and practices in place for proper usage of data, documentation of processes, and communication among team members. For instance, if data science projects result in expenses that deviate from the original estimates, a governance team can evaluate its value and rectify steps before costs spin out of hand.
- Collaborate with Data Analytics Experts and Cloud Providers
Never compromise on getting the right expertise for your data analytics and cloud computing work. Cloud vendors offer specialized consulting services and equipment built to help you optimize your allocation of resources as well as expense management. Hire experienced data analytics professionals who will take you through a step-by-step, detailed guide to optimum practices that help in cost optimization, and the building of robust governance frameworks.
Conclusion
Effective management of cost in data analytics is a critical dimension through which businesses can accrue insight without running over their budgets. Clear forecasting, usage limits, resource optimization, and proper governance are only a few proactive measures that could be implemented by organizations to get full utilization without extra costs popping up from somewhere. Such strategies will help companies decide with ultimate data-backed certainty and grow further ahead in the fast-paced nature of the current business environment.
Are you ready to optimize your data analytics strategy to avoid costly surprises? Ascentt’s data and advanced analytics solutions help manage costs along the way to maximizing the value of insights from your data. Our highly experienced team brings comprehensive services in these areas, helping to improve data quality and further implementation of advanced analytics techniques to aid in decision-making, not letting bill shocks hinder your way.
Contact Ascentt today to find out how we can help you realize your data’s full potential for continued growth and more manageable costs.