Organisations measure their return on investment from both operational and business perspectives.
This is one finding from a survey of 200 senior IT and data executives at large companies with an annual revenue of $50-million or more.
The study, which was commissioned by Conduktor, found the majority (93%) of respondents use multi-platform data streaming architectures, and more than four-in-10 (43%) respondents are already using data streaming for training or running AI.
Respondents identified a series of operational measures to evaluate the success of data streaming initiatives. The top three include: end-to-end latency (time from ingestion to action or insight); data freshness (time from event creation to availability for use); and system uptime and failure recovery times.
Other measures include processing cost per gigabyte or per event, volume of events processed successfully per second (throughput), scalability to handle peak loads without degradation, accuracy and completeness of streamed data (no loss, no corruption).
Business metrics
Respondents name the top three business metrics as being:
- Increased revenue driven by real-time data services or products;
- Improvements in customer satisfaction or NPS related to real-time experiences; and
- Enhanced accuracy and speed for real-time decision making.
Additional KPIs include reduction in fraud, security incidents, or financial losses; faster time-to-market for data-driven products or features; and a reduction in operational costs through automation and faster incident detection.
Nicolas Orban, CEO of Conduktor, comments: “Organisations carefully track streaming performance (latency, throughput, uptime), but struggle to connect these operational metrics to business outcomes like revenue growth or customer satisfaction.
“This measurement gap reveals a deeper issue: without unified governance across streaming platforms, teams optimise individual systems while enterprise-wide ROI remains unclear. The solution isn’t better metrics; it’s governed infrastructure that makes the connection between technical performance and business value transparent.”
According to Dataintelo, the global market size for streaming data processing system software was valued at approximately $9,5-billion in 2023 and is projected to reach around $23,8-billion by 2032, reflecting a compound annual growth rate (CAGR) of 10,8% over the forecast period.
Dataintelo says: “The surge in the need for real-time data processing capabilities, driven by the exponential growth of data from various sources such as social media, IoT devices, and enterprise data systems, is a significant growth factor for this market.”