Monetizing Observable Data Will Separate the Winners and Losers

November 04, 2022

Contributor: Lori Perri

The future is not about predicting; it’s about preparing.

Imagine buying insurance for your car solely on the basis of actual driving behavior rather than predictions based on the number of speeding tickets you received?

Tesla offers insurance based solely on their “observable” real-time driving behavior. Tesla vehicles “observe” and measure driving behavior using sensors to produce a monthly safety score. It has been estimated that average drivers could save 20% to 40% on their premium, and those with the safest scores could save 40% to 60%.

“Applied observability is about clarity rather than creativity as it is based on confirmed stakeholder actions, rather than intentions,” Distinguished VP Analyst David Groombridge said at the Gartner IT Symposium/Xpo™ conference. “Even if we don’t know what the decision was, or if it was implemented differently than what we planned, we can see the actual outcomes in data. By including the context in which that data was captured and then using AI to analyze and make recommendations, we can create a feedback loop that allows an enterprise to make faster and more accurate future decisions.”

What makes applied observability a trending technology

By applying observability systematically, organizations can increase their speed of response and optimize business operations in real time.

Gartner expects that by 2026, 70% of organizations successfully applying observability will achieve shorter latency for decision making, enabling competitive advantage for target business or IT processes.

“The business value of observability is using information at the surface level to understand what is actually present internally,” says Distinguished VP Analyst Frances Karamouzis. “Observable data is valuable because it’s not based on intentions, obligations or promises but instead from confirmed stakeholder actions, making it truly an evidence-based source of decision making.”

Applied observability enables organizations to use their data artifacts for competitive advantage. When planned strategically and executed successfully, applied observability has shown itself to be a powerful approach to data-driven decision making.

Observability capabilities are rapidly being built across a number of functions of the organizations:

Observability isn’t a single technology or defined market. It spans many business functions and layers of the organization, applying tools to enrich the observable data that is generated. The most common places where results are made available to users are decision intelligence and analytics solutions.

Applied observability defined

  • Observability is the ability to understand what is happening inside a system based on the external data released by that system. Observability requires that actionable data from multiple sources is appropriately connected, optimized and enhanced for context. 

  • Observable data refers to any variable that can be observed and directly measured. For an enterprise, it often comes from one or more existing IT systems. 

  • Applied observability is the applied use of observable data in a highly orchestrated and integrated approach across business functions, applications, and infrastructure and operations teams. It enables shortening the time between stakeholder actions and organizational reactions, and so allows proactive planning of business decisions.

Three key elements of applied observability

The three key elements of applied observability include:

  1. Democratized opportunity. Every organization has massive amounts of observable data in the form of digitized artifacts. The challenge lies in converting that data into a strong set of capabilities, especially across the organization. 

  2. Multiple concurrent data layers exist in different parts of the organization: infrastructure operations, middleware, applications, data, functional workflow and business process layers. The business and IT owners of each of these layers are often already exploring various elements of observability, but bringing them together will achieve much greater value. 

  3. Implementation can be a difficult, complex and long journey due to the combined demands of the multiple parallel layers. However, it can be divided into logical increments. This must be done with a strong overall strategic plan or blueprint across the organization.

Learn what loyal customers find useful about applied observability

Applied observability helps organizations achieve nearly real-time response. Response speed leads to customer satisfaction and loyalty. Shorter feedback loops between stakeholder actions and organizational reactions enable proactive planning of business decisions based on customer actions that are positive, negative or indecisive (or lacking in information). For example, a positive feedback loop between customer behavior and reward mechanisms can be a key differentiator to improve customer loyalty. Similarly, using actual customer behavior as a measure of risk can reduce exposure compared with using theoretical customer models.

In short:

  • IT leaders’ highly orchestrated use of actual stakeholder actions, rather than intent or predictions, drives competitive advantage.

  • Driving better, faster, and more consistent and effective business and IT decisions is key to being successful.

  • Observability is not a forecast or prediction but rather a truly evidence-based source of decision making.

David Groombridge is Distinguished VP Analyst on the IT Leaders and Technical Professionals team at Gartner Research & Advisory. He undertakes analysis on all phases of the IT sourcing cycle, with a particular focus on best practices in the sourcing of outsourced digital workplace services, hybrid infrastructure services and hosting of SAP systems. He provides guidance for clients on vendor selection, contract pricing, structure and terms, and commercial negotiations.

Frances Karamouzis is Distinguished VP Analyst in the Gartner Research & Advisory group focusing on AI, hyperautomation (inclusive of robotic process automation and decision modeling) as well as the business and IT services to drive outcomes. Ms. Karamouzis is focused on research that addresses strategic planning, evaluations, business cases and service delivery capabilities, and disruptive trends.

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