USA, Oct 20, 2026
Every application, cloud service, and security tool in the enterprise produces a steady stream of logs, metrics, traces, and events. That telemetry is essential for day-to-day operations, modern software, infrastructure, and technology initiatives, yet as telemetry volumes grow, so too do cost and complexity.
Unfortunately, "more data" does not always mean "better data" or "better business outcomes." In fact, the opposite is often true.
In Splunk's most recent Machine Data Outlook Report, for instance, 74% of technology leaders say AI and automation have caused moderate or significant data management challenges. Despite collecting more data than ever, 53% frequently or always lack the machine data required to support AI or automated decision-making, and 56% frequently or always limit data collection because of budget or infrastructure constraints.
These findings reveal a stark divide between how much telemetry organizations collect and how much value they get from it. Without a way to prioritize, organize, and contextualize telemetry, much of that data only adds cost without improving reliability, security, or AI readiness. This means that organizations are paying to store data rarely used, while the data they actually need can be fragmented across tools or never even collected at all.
Formal observability and security practices, supported by a modern data platform, can help close that gap. These practices establish how telemetry is collected, used, and managed across teams, while the platform provides the scale to prioritize, organize, and contextualize that data across the enterprise.
The Splunk Platform provides a single, unified solution for analyzing and acting upon data across the enterprise, making it an excellent foundation for both observability and security practices.
More Telemetry Does Not Equal Better Outcomes
New applications, cloud environments, security tools, and AI workloads continuously add telemetry data, but this means that most organizations now collect more data than they can effectively use. Also, without proper context, that data can't surface the most relevant signals, relationships, or business insights. Growing telemetry volumes create challenges across the organization, including:
- Higher costs. Low-value data consumes the same ingest, storage, and licensing budget as the data teams depend on.
- More complexity. Data spread across separate IT, engineering, and security tools forces teams to search multiple systems and reconcile conflicting views.
- Slower decisions. Noisy data means noisy alerts, and teams can spend more time sorting through information than acting on it.
- Weaker AI outcomes. AI models and agents depend on governed, contextual data, and fragmented or unorganized telemetry limits their accuracy and value.
In short, without effective prioritization, organization, and context, too many resources can be spent wrestling with data instead of using it.
One way to turn the tide is by adopting formal observability and security practices, enabled by a modern data platform.
How Observability and Security Practices Bring Focus to Telemetry
Formal observability and security practices give organizations shared goals, clear ownership, and defined measures of success for their telemetry. They identify the organization's critical services, risks, and business outcomes, along with the data that supports each one. According to Splunk's State of Observability 2025, leading observability practices generate 53% higher return on their observability investments than their peers and are nearly twice as likely to say observability significantly improves revenue, employee productivity, and product roadmaps.
With formal practices in place, organizations can:
- Prioritize telemetry by business value. Tie data to critical services, service-level objectives, and security risks, so high-value data receives premium treatment and the rest is filtered, aggregated, or retained at lower cost.
- Consolidate data across teams. Observability and security draw on much of the same telemetry, and coordinating the two practices reduces duplicate collection and disconnected tools.
- Add context to every signal. Connecting performance, network, and security data shows whether an anomaly is an operational issue or a threat, and how it affects the business.
- Prepare data for AI. Governed, well-defined telemetry gives AI models and agents the reliable context they need to deliver accurate results.
These practices give every team a shared framework for how telemetry is prioritized, organized, and used. To scale across the enterprise, observability and security practices must be built on a platform that can prioritize, organize, and contextualize data across every domain.
Unifying Observability and Security Data with Splunk
Prioritizing, organizing, and contextualizing telemetry at enterprise scale requires a platform that can work with data across the organization. The Splunk Platform does just that, while giving teams the ability to search, analyze, visualize, and act on any type of telemetry.
Teams can search and investigate telemetry across sources, analyze it with monitoring, alerting, and reporting, and visualize it through custom dashboards accessible from anywhere. Those insights then fuel faster, more confident decisions across IT, security, and the broader business.
Splunk offers:
- Machine learning and AI. Improves security and business outcomes by bringing machine-level intelligence to data.
- Data streaming. Collects, processes, and distributes telemetry data to Splunk and other destinations in milliseconds.
- Federated search and analytics. Triggers and correlates searches across Splunk and external stores, to treat, route, govern, and retain telemetry based on value and use.
- Scalable index. Collects and ingests data from thousands of sources.
- Powerful dashboards. Complex data stories communicate concepts clearly to technical and business stakeholders alike.
- Collaborative tools. Enables teams to interact with data and collaborate from anywhere via mobile, TV, and augmented reality.
Splunk Cloud Platform is available as a self-managed enterprise deployment or on demand, as a scalable, cloud-based service. Both deployments offer advanced data management capabilities and can be used as a foundation for modern observability and security practices.
Make Telemetry Work Harder with Logicalis and Splunk
Telemetry data improves outcomes when organizations can prioritize, organize, and contextualize it. Formal observability and security practices, supported by solutions such as the Splunk Platform, give teams more control over telemetry data and costs, while delivering faster, clearer answers.
Logicalis Managed and Professional Services teams help organizations assess current telemetry, design observability and security practices, and deploy the Splunk Platform, with ongoing support and optimization as data volumes grow.
Contact Logicalis today to learn how we can help make your enterprise telemetry work harder.