Demystifying Actual Vs. Projected Data Infrastructure Costs

 In AI, ML and Data

Demystifying Actual Vs. Projected Data Infrastructure Costs

Most data leaders across industries agree that the data infrastructure ecosystem is impossibly complex. While companies have different viewpoints as to what constitutes the optimal data stack, the outcome is heavily influenced by one common denominator – money. The economics of technology is one of the key techniques that helps businesses differentiate and choose between data platforms, with cost as the barometer.

Data leaders today realize that their legacy systems are costing them more than they should, but the idea of moving to a modern, cloud-based infrastructure fills them with more than a little fear and aversion. And quite understandably so – adopting a new set of tools for your business is a challenging and painstakingly protracted process with uncertain outcomes. But the performance, scalability, and cost benefits of the cloud are hard to beat. And the first step in deciding whether a cloud-based platform is the right fit for you is to identify the costs of continuing to operate legacy infrastructure. We have come up with a multi-dimensional comparison to demystify the actual costs that businesses currently incur on their legacy systems and the projected savings that they stand to gain through a modern, cloud-based platform.

  • Legacy Storage Cost
    Growth in storage cost is inevitable as businesses expand. The cost of hardware, server room, and power required for on-premises storage is much higher than signing up for a cloud-based platform¬. You need to hire professionals to install and maintain your servers and, since the hardware has a finite lifespan, you need to bear the cost of replacing it every few years. The inability to easily scale up or down is a persistent issue with on-premises storage, over-buying servers and underutilizing them is a common lament.
    A cloud-based data platform can help you get past the worries of planning for capacity, installing hardware and software, and managing its upkeep with its on-demand, pay-as-you-go model that leaves these cares in the hands of third-party professionals. According to a Forrester study, companies can save nearly $3.5 million over three years through cloud-based storage.
    But these cost efficiencies can only be beneficial if you correctly determine which data should be moved to the cloud. Storing all your data in the cloud is not affordable. If capacity, performance, data protection and other data operations are not aligned with your business needs, it is fully possible for cloud costs to surpass legacy storage costs.
    A lot of businesses use a blend of on-premises storage with cloud storage platforms to control hardware sprawl and maintenance overheads. This hybrid strategy can be very effective in helping you reclaim costly on-premises storage while matching storage costs to data value by only extending select data workloads to the cloud.
  • Legacy Compute Cost
    With legacy systems, you need to purchase enough compute capacity to meet peak demand over time, but most businesses end up using only a small fraction of the available compute capacity. It is hard to spin up compute resources on-demand, so you have to make sure that you buy more than enough capacity to meet usage peaks. But for every minute that you are not running at peak load, you are overpaying for unused capacity.
    On the cloud, you can enjoy true cost-effectiveness with the ability to effortlessly scale compute capacity to fulfill demand and scale back when usage peaks dip. An economic impact study by Forrester states that organizations can save up to $2,50,000 worth of compute costs on the cloud per year.
  • Cost of ETL Developers
    Data preparation with legacy systems is another trying activity that requires skilled ETL developers to invest considerable time in moving data from transactional information sources to operational systems and finally to data warehouses. Traditional ETL tools fall short in managing large volumes of data and create bottlenecks between data transformation and analysis. This prolongs data processing causing business users and analysts to wait for multiple days before they see a report. Companies have to hire additional ETL developers to match business demands, a skill set that is not entirely cheap.
    A cloud-based platform substantially decreases query time and enables dynamic data querying that makes existing ETL developers more efficient and reduces the need to hire additional talent. In contrast to traditional ETL, cloud-based platforms offering ELT make it substantially faster to move data from source systems to data warehouses and reduce wait times by performing transformation on-demand. Cloud-based tools can shorten data processing time from days to minutes, delivering up-to-date reporting to the organization.
  • Cost of Database Management
    On-premises legacy systems require a small army of engineers for provisioning, day-to-day maintenance, upgrades, and support. Cloud solutions are fully managed and require negligible involvement from customers. Serverless databases and managed relational database services offered by cloud providers eliminate administrative responsibilities. New databases can be quickly set up in a few clicks. Distribution of resources, backups, hardware failure, and failover operations are handled by the provider without customers even noticing. With reduced database management needs, engineers can be redirected to project-related tasks such as developing new products and enhancing existing product offerings.
  • Faster Time-to-Launch
    Launching new projects with a legacy setup requires considerable capacity forecasting, provisioning of additional hardware, and day-to-day management. It can often take three to four months to secure enough capacity to embark on a new project. A cloud-based platform with its ability to spin up resources on-demand can slash the time to launch a project from months to hours. In fact, Forrester revealed that with the cloud, the time needed to launch a project can go down by 90%. This leads to more revenue-generating projects being launched each year with fewer IT resources.

Modern, cloud-based platforms make sense economically for businesses trying to maximize the ROI from their data infrastructure. If the greatest obstacles stopping you from becoming data-driven are cost and hardware scalability limits, then the deadlock has been broken by cloud-based offerings. By getting rid of many of your cost constraints, you can try the new projects you always wanted to try and focus on strategic data analytics that can grow your business.

We have collected some thought-provoking information around average data infrastructure costs across the industry and projected savings that a lot of companies are benefiting from by migrating to new technology. If you’d like, we can share this information with you and use our cost evaluation methodology to run some numbers around how much you can potentially save. You could probably be spending far less on data while realizing a lot more business value and ROI. Get in touch with us if you are interested.

Get in touch with us if you’d like to opt for a free cost assessment of your data infrastructure

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