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Before recession fears kicked in last year, technology buyers seemed comfortable funding rapid growth and implementing tools and systems to drive digital transformation across the organization, even if the timeline for return on investment was uncertain.
As the tides turned, IT budgets are under pressure, questioning which strategic priorities will continue. In the race to manage costs, C-level business leaders must consider whether cutting back on IT spending may inadvertently stall their bottom line of business while missing opportunities to fix inefficiencies.
In the data management space, this is an important topic as data grows exponentially every year regardless of what is happening in the market. Reducing the amount of data managed is simply not an option, so the focus must be on finding efficient routes to market and optimizing IT spend in ways that don’t stifle innovation. Ensuring your data strategy is recession-proof is critical to positioning your organization for success and continued growth when economic conditions improve.
This article is the first in a two-part series on how to drive innovation and growth for your line of business and IT organization while generating cost savings and simplifying your data management ecosystem. In this article, I focus on cloud cost management, a key area where we see room for improvement within IT spend.
Reducing wasted cloud spending and managing outliers
Although data analytics environments within cloud-based systems are easy to launch, they can be difficult to manage across all business segments. In some cases, organizations manage multiple clouds, not because of a strategic business priority, but because different departments have chosen their preferred cloud provider to run their applications. Before stopping or postponing a new innovation project, consider what budget can be offset by reducing existing cloud spending.
Often, the most difficult cost component for cloud businesses isn’t just the premium they pay cloud providers in exchange for elasticity and uptime; it’s also the cost of moving data in and out of their analytics systems.
Even more financially damaging for companies trying to manage cloud costs is that those costs are difficult to predict. Pay-as-you-go computing, networking, and storage costs can quickly spiral out of control as the volume of data flowing through an organization grows. Usage-based pricing is great for small-volume data sets and occasional use, but can be prohibitively expensive for high-volume, continuous workloads. Waste can quickly accumulate across multiple departments and cloud environments, and the ability to offset the IT budget here can help fund new innovative projects and revenue-generating opportunities.
Identify workloads where continuous data integration and analysis negatively impact your ability to increase performance and scale, and work with your organization or partner network to identify the right technology and solution stack to get more value from your cloud investment. This is a critical first step in reducing waste and managing outliers for your organization, especially in data analytics environments.
Workload consolidation and tools to streamline operations
If you’ve watched the data management industry and the rise of cloud-based ecosystems over the past few years, you’ve noticed the rapid growth of tools and technologies that address all aspects of the data supply chain. While these tools can deliver critical business value, they can also complicate operations. A careful review of your infrastructure can reveal opportunities to streamline costs by consolidating workloads and tools.
In the data management space, we are seeing the emergence of scalable technologies that consolidate various features and capabilities within a single platform. For example, data ingestion and loading (ETL and ELT workflow), data warehousing, real-time analytics, and machine learning capabilities are typically locked within different solutions. Now we see technical solutions that address all three and more within a single solution—continuous loading of large data sets, scaling of traditional OLAP workloads with real-time analytics workloads for growing numbers of concurrent users and service classes, and native support for AI and machine learning learning directly within a database or data warehouse.
Simplified data management can reduce operational headaches by consolidating disparate tools into a single solution. And by minimizing unnecessary data movement, organizations can further reduce the costs of these workloads.
Consideration of alternative pricing models for new and renewal contracts
Most cloud data stores charge a single price for storage, with an additional, variable price for usage. As data analytics activities grow, so do costs, especially for complex and continuous analytics workloads. This can lead to unexpectedly large bills and makes budgeting difficult.
Fortunately, other pricing models are available. For example, some data analytics vendors offer core-based pricing that can be calculated during a pilot project or at the start of a project, based on a combination of data volume, compute requirements, and SLA. This can be useful for customers who want to pay a consistent price each month, regardless of how much data they upload and analyze.
To maximize the benefits of a kernel-based pricing model for your organization, identify workloads that require high volume, continuous data processing either due to constant data streaming or loading, CPU-intensive analysis, or both. If you consistently process large amounts of data at a constant capacity, considering a core-based pricing model that provides maximum processor capacity at a fixed or predictable cost is one potential approach.
Don’t let cost concerns keep you from demanding more from your data.
As the technology industry adapts to a changing financial environment, it’s only natural to look for ways to cut costs and reduce new initiatives. Cloud spend management as suggested above offers an opportunity to reduce costs without sacrificing innovation.
While cost management in the cloud is an initial step towards recovering IT costs in challenging times, there are additional opportunities within system architecture and staffing to consider. Join me next time as we look at managing the cost of systems architecture in data analytics as a path to staying resilient and delivering further innovation and business growth.
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