Manufacturing’s data problem isn’t volume – it is the right insight

The challenges today's manufacturers face include:

• Overload with limited actionable insight
• Poor data quality and inconsistent sources
• Siloed systems that significantly slow down decision-making

It is no longer an issue of a lack of data – it is about a lack of connected insight across the entire production, supply chain, and distribution processes.  As manufacturers continue to digitize, leveraging the right combination of technologies and use cases is key to optimizing IT spend for maximum productivity, according to Gartner.

Manufacturers need to understand which data points are essential and how to connect them directly to the metrics that define operational success, such as overall equipment effectiveness (OEE), throughput, downtime and order fulfilment, for example. The key is pinpointing what matters most within the infrastructure and aligning it with business strategy – so the data drives measurable operational improvement.

This is where observability comes in. Observability transforms raw data into actionable insights by directly linking logs, metrics and traces to business-critical KPIs. Traces, for example, reveal how efficiently processes flow across systems from end-to-end and where friction may be impacting productivity. According to a recent observability report, 74% of respondents stated that implementing observability enhances employee productivity, and 65% reported a positive influence on revenue. (Splunk,2025)

To achieve this, manufacturers must establish a robust infrastructure that ensures seamless operation. This includes the end-to-end management of logs, traces and metrics from data collection and storage to visualization. Critical components include interoperable platforms, real-time edge processing and strong data governance. A unified observability strategy is pivotal to this – one that can scale across enterprise systems and individual manufacturing plants, providing real-time visibility that improves reliability, optimizes production and minimizes costly disruptions.

Downtime, for example, costs Global 2000 companies $400 billion annually. (Splunk, The hidden cost of downtime). These enterprises lose $200 million on average each year because their digital environments fail without warning. Observability leaders, however, are four times more likely to resolve instances of unplanned downtime in minutes, versus hours or days, according to a report from Splunk.

Modern manufacturing extends far beyond the factory floor, encompassing the entire value chain, from production line health and supply chain flows to logistics and sales channels. With an increasingly competitive digital landscape and unprecedented disruptions, from geopolitical uncertainty to dynamic customer demands, advanced data analytics are crucial for extracting actionable insights, driving informed decisions, and maintaining a technological edge. 

IDC predicts that by 2027 companies that do not prioritize high-quality, AI-ready data will struggle to scale Gen AI solutions, resulting in a 15% productivity loss. (ISC Futurescape, 2026)

Unified end-to-end observability is crucial for enterprises seeking to leverage advanced analytics and AI, as it ensures that data is accurate, complete, and actionable. By offering a unified end-to-end view across systems – from edge devices to applications - observability allows teams to monitor logs, metrics, and traces in real-time, rapidly spot anomalies, and maintain a reliable data pipeline. Without observability, advanced analytics and AI initiatives risk being fed incomplete or inconsistent data, which can generate inaccurate analysis and flawed insights.

By aligning an end-to-end view of operations and operational signals with strategic KPIs, manufacturers can significantly reduce downtime, improve production quality, strengthen forecasting capabilities and ultimately create a smarter, more resilient and secure manufacturing ecosystem.

Manufacturing is investing in automation, IoT and AI to transform, but many still operate with fragmented visibility across operations, IT and security. This lack of unified observability leaves enterprises vulnerable to unplanned downtime, prolonged recovery times and hidden cyber and supply chain risks. 

Digital manufacturing leadership is no longer measured solely by efficiency gains, but by the ability to operate a resilient environment at scale. Manufacturers that adopt end-to-end observability move from reacting to disruptions to preventing them by speeding up MTTR, strengthening cyber posture, and consistently outperforming competitors on quality, reliability, delivery and customer confidence.

To find out more about observability in manufacturing, download our latest ebook: