Data Observability Glossary

Adaptive AI

Adaptive AI is artificial intelligence that learns and adjusts its behavior when new data, feedback, or situations appear. Instead of being reprogrammed, it improves in real time and becomes smarter over time. This makes systems more flexible and useful in changing environments. For example, an adaptive AI in e-commerce can update product recommendations instantly when a customer’s shopping habits change.

Agent Communication

Agent communication is the way AI agents share information and coordinate with each other. They may exchange messages, signals, or data to work together toward a goal. Clear communication helps agents solve problems faster and avoid conflicts. For example, a fleet of delivery drones uses agent communication to share route updates and prevent mid-air collisions.

Agent Loop

An agent loop is the repeated cycle an AI agent follows to get things done. In each loop, the agent observes its environment, decides what action to take, and then acts. This cycle continues until the goal is reached. For example, a smart thermostat uses an agent loop to sense room temperature, decide if heating or cooling is needed, and adjust the system automatically.

Agent Memory

Agent memory is the information an AI agent keeps to make better decisions. It can be short-term (recent actions) or long-term (past experiences and knowledge). Memory helps agents learn, adapt, and respond more naturally over time. For example, a customer support chatbot uses agent memory to remember what a user asked earlier in the conversation so it can give more accurate answers.

Agent Pipeline

Agent pipeline is the step-by-step process an AI agent follows to complete a task. It usually includes gathering data, analyzing it, making a decision, and taking action. A well-designed pipeline makes agents faster and more reliable. For example, a fraud detection agent uses a pipeline to collect transaction data, check for unusual activity, decide if it’s risky, and then alert the security team.

Agent Specialization

Agent specialization is when AI agents are designed to focus on specific tasks or roles instead of trying to do everything. Specialized agents perform better because they are trained for a clear purpose. For example, in a data platform, one agent may specialize in monitoring data quality while another focuses on detecting security risks.

Agent-Based Modeling (ABM)

Agent-Based Modelling (ABM) is a way to simulate how many individual “agents” (such as people, machines, or organizations) act and interact in a system. Each agent follows simple rules, but when many of them act together, complex patterns appear. ABM helps researchers and businesses understand how behaviors and decisions spread across a group. For example, a city may use ABM to see how drivers change routes during traffic jams and how that affects congestion.

Agentic AI

Agentic AI is artificial intelligence that can plan, decide, and act on its own to achieve goals. Unlike traditional AI that only reacts when told what to do, agentic AI takes proactive steps, such as fixing errors, monitoring systems, or suggesting improvements. For example, an agentic AI in data management can automatically detect a broken pipeline and reroute data without waiting for human intervention.

Agentic AI Architecture

Agentic AI architecture is the design framework that shows how agentic AI systems are built and work. It defines how agents sense their environment, make decisions, and take actions in a structured way. A strong architecture makes AI agents more reliable, scalable, and easier to manage. For example, a company may use agentic AI architecture to connect data monitoring agents, compliance agents, and customer support agents into one coordinated system.

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data observability platform

Software application that leverages analytics and ML/AI to improve reliability, scalability, and costs across an organization's data, pipelines, and workloads. Provides visibility into the health and performance of all aspects of enterprise data systems.

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data outage

Period of downtime during which users and/or downstream applications are unable to access certain data assets. May be caused by a variety of situations, such as resource contention, structural changes, or system health issues.

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data pipeline observability

End-to-end visibility into the flow and cost of data across an organization's interconnected systems.

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data pipelines

Processes and technology used to ingest data from source systems into an organization's data ecosystem.

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data profiling

Process of crawling, analyzing, and summarizing data in a way that helps users understand the organization's data.

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data quality

Measurement of a data set's overall health for its intended use. Minimizing or eliminating missing and incorrect data are key aspects of ensuring data quality.

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data reconciliation

Process of ensuring that data has arrived as expected during its movement from point A to point B.

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data reliability

Ensuring the dependable delivery of quality data in an uninterrupted, on-time schedule. Data reliability is essential for building trust with business users.

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data roi

Return on investment realized by leveraging an organization's data. Calculated by subtracting data's estimated return minus its total cost, which includes the sum of costs pertaining to data storage, compute, pipelines, and related systems. The net return is then divided by the total cost, multiplied by 100 and expressed as a percentage.

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data success

Effective use of data to achieve an organization's business goals and support its use cases, such as BI reporting, data applications, embedded analytics, AI/ML workloads.

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data swamp

Derogatory term used to describe an organization's data when it is siloed or generally disorganized. Guaranteeing data quality and accessibility becomes difficult—if not impossible—when data swamps exist, thereby eroding end user confidence in organizational data and leading to an even murkier data swamp.

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data validation

Process of ensuring that data follows and conforms to the schema definition, follows business rules, and is accurate and usable.

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data waste

Tangible and intangible costs that can be attributed to a company's inefficient storage and utilization of data.

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data-lineage

A historical accounting of data's journey from its original data source to present day usage, including any dependencies and connected assets.

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mttr

An abbreviation for "mean time to resolution," a metric that measures a team's responsiveness to resolving issues. (Sometimes referred to as "mean time to recovery.") Calculated by dividing the sum of all time required to resolve issues during a given period by the total number of incidents during the same period. Data teams should strive for low MTTRs.

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observability data

Data that helps an organization understand the reliability, scale, and cost of its data, processing, and pipelines. Used to predict, prescribe, prevent, troubleshoot, optimize, and contextualize.

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over provisioning

Acquiring or deploying more of a particular resource (storage, compute, etc.) than what is actually necessary to support an organization's current needs. Often occurs as a safeguard to protect against unexpected changes in demand.

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schema drift

Structural changes to schemas and tables, such as the addition or deletion of a column, that can break pipelines or impact downstream applications.

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service level indicator (sli)

KPIs that measure a service provider's adherence to targets set forth in a company's SLA. Examples of data-related SLIs include data pipeline uptime percentage and average response time.

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service level objective (slo)

Specific targets that are defined by an SLA and agreed to by key stakeholders within an organization. Data-related SLOs commonly relate to system availability and service provider responsiveness.

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