Now, if your data workload is mission-critical – for example, if it’s part of a predictive analytics service that delivers product recommendations to your customers in real time, thereby contributing to revenue generation – you can probably justify spending a lot of money on it. In that case, you’d likely choose to store the data in a warehouse that is designed to optimize queries, and you’d devote plenty of compute resources to it.
But what if the data workload is less critical? What if, for russia whatsapp number data instance, it’s part of an auditing process that your business performs periodically, but which doesn’t have to deliver results in real time? It would be a lot harder to justify paying for top-tier data infrastructure in that case.
In short, determining whether your cloud data is cost-optimized isn’t a matter simply of looking for obvious instances of unnecessary spending. It’s also about assessing whether the money you’re spending on data workloads in the cloud makes sense given the business results that they help deliver.
To make that assessment, you need to know much more than what you’re spending on cloud data resources, or how your spending varies over time. You also need to know which business purpose the spending supports, as well as which stakeholders are responsible for the spending.