The federal government collects vast amounts of data. However, data isn\u2019t meaningful without context and if it\u2019s not in the hands of the right people at the right time.<\/p>\n\n\n\n
Massive data sets are incredibly complex. Imagine what it takes for the Centers of Disease Control to gather public health data from across disparate systems or for the Pentagon to draw on sensor data for faster decision making during training. Before reports on any given disease reaches the public, or reconnaissance data is shared across military branches, the data takes a complex journey through systems and across networks to ensure it gets to the right entity.<\/p>\n\n\n\n
To protect this \u201cjourney,\u201d agencies must understand the interdependencies between the various heterogeneous systems \u2013 network, cloud, and IT functions \u2013 and their underlying health and performance.<\/p>\n\n\n\n
Too often multiple systems, an overload of alerts and disjointed analytics make it hard to achieve the actionable insights necessary to identify and resolve mission-critical activities rapidly. One solution to this problem is observability.<\/p>\n\n\n\n
Taking monitoring an important step further, let\u2019s look at three ways observability can help federal IT pros work smarter, not harder as they harness data to fulfill their missions.<\/p>\n\n\n\n
Traditional monitoring tools typically provide insights into specific parts of the infrastructure \u2013 network, database, cloud, application, and so on. But these tools don\u2019t scale or provide the level of insight that modern IT infrastructures require.<\/p>\n\n\n\n
For instance, they typically only provide alerts and insights into anomalies after they arise \u2013 at which point the user experience is already impacted. Neither do they offer visibility into cross-domain correlation, operational dependencies, or service delivery (i.e., is the data getting where it needs to go, securely and rapidly?).<\/p>\n\n\n\n
This is where the concept of observability excels. Observability goes beyond traditional monitoring to provide proactive insights into problems across heterogeneous infrastructures before they occur. For example, combining full-stack observability with the power of artificial intelligence (AI), machine learning (ML), and predictive analytics \u2013 also known as AIOps \u2013 IT pros can collect data from across the entire ecosystem, even hybrid environments, and glean the insights and context they need without a flood of telemetry to sift through.<\/p>\n\n\n\n
With this visibility, they can observe end-to-end service health, data movement, security, and availability, before data availability is impacted. They can quickly pinpoint what components are degrading performance, understand interdependencies in the network stack, and even predict or anticipate situations, compliance issues, or security threats for more proactive remediation and uninterrupted service.<\/p>\n\n\n\n
A key benefit of observability and AIOps is that it can response automatically without the need for humans to get involved. This smart technology learns about the agency\u2019s environment, observes remedial action taken manually by people and uses these observations to automatically respond to future problems and trigger mitigation workflows \u2013 without the need for manual involvement.<\/p>\n\n\n\n
In this way, full-stack observability ensures a more operationally resilient, autonomous IT systems while freeing IT pros to focus on mission-critical activities, such as infrastructure optimization.<\/p>\n\n\n\n
As the federal government\u2019s data lake grows, agencies need to know how performance is impacted and where to scale capacity. Observability provides these insights by shining a light on interdependencies in network infrastructure and applications with full-data correlation. It also scales easily as environments get larger, more complex, and distributed \u2013 breaking down silos, simplifying procurement, and accelerating digital transformation.<\/p>\n\n\n\n
For federal IT pros, the path toward working smarter, not harder, begins with implementing full-stack observability. As agencies increasingly look to turn disparate data sets into highly available and actionable intelligence \u2013 in the shortest amount of time \u2013 these unified, flexible technologies will loom large.<\/p>\n\n\n\n
Sai Krishna is GVP of Engineering at SolarWinds, an Austin, Texas-based observability and IT management software company.<\/i><\/p>\n\n\n\nHave an opinion?<\/h2>\n\n\n\n