Assessing the economic impact of a real-time data platform

Timely, integrated data which supports multiple applications, operational systems, and analytical workloads is the goal for growing enterprises determined to compete on the global stage. Unfortunately, too many are contending with traditional data platforms and disparate technology stacks that are still slowly grinding the data processing gears from transactional to operational and then to analytics systems.

The disadvantages of legacy systems are legion, but transformation takes time, resources, and considerable financial investment. Ironically, it can be easier to get Board members to sign off on additional servers to bolster and stabilise legacy architecture than to consider investing in a move to a modern real-time data platform. In the meantime, IT teams struggle to get the necessary performance out of the traditional systems that support their workloads.

Unsurprisingly, organisations with business requirements involving high data volumes, rapid read/write rates, and critical systems are finding that the cost of ownership of their infrastructure is rising at an alarming rate. And it is increasingly hampering their ability to scale to meet market demands.

Business leaders want to hear that they can migrate to a modern real-time data platform that will support their workloads without impacting performance. At the same time, they can reduce the total cost of ownership (TCO) associated with legacy infrastructure.

To demonstrate how they can do this, we first need to look at the challenges they are facing:

High expenditure

High costs are associated with achieving the required memory and power with existing mainframes. Companies are using expensive resources to build additional infrastructure to manage their legacy systems. They can’t access monitoring or automation tools that would make development resources productive. Uptime can be compromised, particularly when a company is dealing with complex transactions.

Hampering scalability

It is challenging to add more data to existing workloads or scale horizontally to include more transactions, analysis, or operational activity if a legacy system struggles to manage the existing volume of data.

Poor performance

The different technologies that comprise a legacy system can function, but their age and overuse render them slow without excessive bandwidth. For example, suppose large data sets must be processed quickly and reliably. In that case, this will be difficult and could result in a loss in earnings, mainly if customers are involved in high-volume transactions or critical services, such as fraud detection.

These are considerable issues for any business to be facing. If organisations invest in real-time data platforms, they will deliver operational stability and high-performance 24x7.

Optimisation of data storage

Organisations can reduce storage footprints and save on the costs associated with infrastructure without sacrificing performance. Databases equipped with automation tools are easier to manage and can add clusters and replicate data across data centres. In addition, the reliability of the modern data platform means less downtime and fewer issues with monitoring dashboards and tools, thereby freeing up resources.

Improved performance

Real-time data platforms can be implemented without disrupting the business and simultaneously improve performance metrics. There is less system downtime and enhanced value across multiple application areas. If a company applies more data to decision-making, for example, to enhance customer experience, it can improve conversion rates.

A ‘composite’ case to prove TCO To test the theory, analyst company Forrester recently visualised for Aerospike a ‘composite’ company based on the experiences of several customers – a global billion-dollar conglomerate with a large customer base and established brand. Before the imagined implementation of a real-time data platform, the environment consisted of 600 on-premises servers and six dedicated developers.

As the ‘composite’ company started implementing the modern data platform and adding data to its ‘use cases’, it simultaneously reduced its server footprint. In addition, it redeployed some of its developers, reducing its costs. The role of the new platform was to support transactional, operational, and analytical workloads, and in each case, it succeeded in realising the business value and, therefore, lowering costs.

Forrester modelled a range of projected low, medium-, and high-impact outcomes for the composite company based on evaluated risk. Following is the three-year net present value (NPV) for each scenario using a real-time data platform:

● Projected high impact of a $72.1 million NPV and projected ROI of 574%

● Projected medium impact of a $64.4 million NPV and projected ROI of 513%

● Projected low impact of a $56.1 million NPV and projected ROI of 446%

Reinforcing these findings is the case of Criteo, the French ad tech company, which wanted to consolidate its physical server estate to reduce its TCO and cut CO2 emissions. So it replaced 1000 existing servers with 150 Aerospike real-time data platform servers in 2021 and has already saved millions of dollars. Going forward, Criteo plans to replace 5000 servers with 600 in a phased approach. As a result of this migration, emissions savings are expected to be visible

by next year.

Proven TCO should elevate the investment decision.

Both these scenarios demonstrate that migration to a real-time data platform is increasingly essential for companies that process large volumes of data from multiple sources that enable them to make critical, timely decisions. Of course, money and resources must be invested, however with the TCO clearly proven, an essential factor in the investment decision is already made