Decision Intelligence Glossary

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Adaptive analytics workflows

Adaptive analytics workflows are analytics processes that adjust dynamically to changing data conditions, user roles, or business priorities without requiring manual reconfiguration. They route analysis, surface alerts, and update reports based on rules or AI judgment, reducing the time between a business change and an analytical response. Adaptive workflows are particularly valuable in fast-moving operational environments where static scheduled reports fail to keep pace with conditions.

AI for system diagnostics

AI for system diagnostics is the application of artificial intelligence to monitor, interrogate, and explain the behavior of business systems such as ERP, CRM, and supply chain platforms. AI diagnostics detect anomalies, flag performance issues, and trace root causes across interconnected systems faster than manual investigation. The result is earlier identification of operational problems and reduced time to resolution.

AI Playbook

An AI Playbook is a structured, evidence-based business briefing generated by eyko Beats from an organization's live data. Unlike a dashboard or a report, a Playbook progresses through three analytical layers: what happened, why it happened, and what to do next. Each Playbook includes an executive summary, a narrative analysis, supporting visualizations, and prioritized recommended actions. Playbooks are generated from a plain language prompt and delivered in minutes, not days.

AI-assisted system reasoning

AI-assisted system reasoning is the use of artificial intelligence to trace logic, relationships, and cause-and-effect chains within business systems. It goes beyond surface-level reporting to investigate why a metric changed, which upstream factors contributed, and how different variables interact across connected data sources. It reduces the analytical burden on human teams by automating multi-step investigation in complex data environments.

AI-driven KPI tracking

AI-driven KPI tracking is the automated monitoring of key performance indicators using AI to detect deviation, identify trends, and flag performance gaps without manual review cycles. AI-driven KPI tracking maintains a continuous watch across metrics rather than relying on periodic human review, surfacing alerts when indicators move outside expected thresholds. This approach shortens the time between a performance change and an informed management response.

AI-driven scenario modelling

AI-driven scenario modelling is the use of artificial intelligence to generate and evaluate multiple potential future states based on changes in key business variables. Rather than requiring analysts to build individual scenarios manually, AI-driven scenario modelling produces a range of outcomes, including sensitivity analysis and probability weighting, from a single prompt or input set. Finance, supply chain, and operations teams use scenario modelling to assess risk and plan responses before conditions materialize.

AI-enhanced system knowledge base

An AI-enhanced system knowledge base is a structured repository of business rules, definitions, processes, and domain knowledge that is made accessible to AI platforms to improve the accuracy and relevance of generated analysis. It ensures that automated insights reflect the actual definitions, priorities, and logic of a specific organization rather than generic AI assumptions. This is the foundational layer that converts general-purpose AI output into business-specific intelligence.

AI-powered business storytelling

AI-powered business storytelling is the automated generation of narrative explanations that translate data into plain-language business context. It converts numerical findings into structured prose that describes what changed, why it matters, and what the implications are, reducing the interpretive burden on analysts and leaders. The output is designed to be shared directly with decision-makers rather than requiring an intermediary translation step.

AI-powered data insights

AI-powered data insights are analytical findings generated by artificial intelligence from structured or unstructured business data, designed to surface patterns, anomalies, and recommendations that would be difficult or time-consuming to identify manually. AI-powered insights go beyond descriptive reporting by applying predictive and explanatory logic to identify root causes and forward-looking signals. The practical goal is to reduce the time and expertise required to convert raw data into actionable conclusions.

Application behavior modeling

Application behavior modeling is the practice of analyzing how business applications such as ERP, CRM, and finance systems are actually being used (transaction patterns, user behavior, process flows) to surface insights about operational performance. It treats system activity itself as a data source, revealing inefficiencies, anomalies, and optimization opportunities that are invisible in conventional output reporting. It is particularly relevant for organizations running complex ERP platforms with large transaction volumes.

Application-aware data fabric

An application-aware data fabric is a data integration architecture that understands the context of the business applications it connects, including data structures, field definitions, and process logic, rather than treating data as generic rows and columns. This approach reduces the manual effort required to map and clean data from source systems and produces more accurate cross-system analysis. It is especially valuable in environments running multiple ERPs or enterprise systems with different data models.

Application-centric data intelligence

Application-centric data intelligence is an approach to business analytics that starts from the applications an organization runs (ERP, CRM, supply chain platforms) rather than from a generalized data layer. It preserves the context and business logic embedded in source systems, producing analysis that reflects how the organization actually operates rather than a normalized abstraction of its data. This stands in contrast to warehouse-first approaches that flatten application context during ingestion.

Auto-generated performance reports

Auto-generated performance reports are structured business reports produced automatically from connected data sources without requiring manual compilation or formatting. They can be scheduled or triggered by events, reducing the time and analyst effort required to produce standard operational reviews. The quality of auto-generated reports depends on the underlying data model and the rules used to structure the output.

Automated business intelligence

Automated business intelligence is the application of automation to the production, distribution, and maintenance of business intelligence outputs such as dashboards, reports, and data summaries. Automated BI reduces the recurring manual effort of report-building and ensures that outputs are consistently refreshed from live data. It does not replace the interpretive work of understanding what reports mean or what to do about them. That gap is addressed by Decision Intelligence platforms.

Automated insight contextualization

Automated insight contextualization is the process of automatically enriching analytical findings with relevant business context, including industry benchmarks, organizational priorities, historical baselines, and role-specific framing, before they are presented to users. Without contextualization, a metric deviation is just a number; with it, the same deviation becomes actionable information. Automated contextualization reduces the manual framing work that typically falls to analysts or managers before insights are shared with leadership.

Autonomous Analytics

Autonomous analytics is an approach to business analytics in which the system independently identifies what to analyze, runs the analysis, and delivers findings without requiring a user to define the query or build the report. It monitors business conditions continuously and initiates analysis when conditions warrant it, rather than waiting for a scheduled report run or a user-submitted request. The goal is to reduce the latency between a business event and an informed response.

Autonomous data discovery

Autonomous data discovery is the automatic identification of patterns, anomalies, and relationships in business data without requiring users to define what to look for. It applies AI to scan across connected data sources and surface findings that might otherwise be missed in manually curated report sets. It is particularly valuable in large, complex data environments where the volume of potential signals exceeds what human analysts can monitor.

Autonomous Reporting

Autonomous Reporting is a reporting model in which business reports are generated, formatted, and distributed automatically based on connected data, without requiring human assembly. It eliminates the recurring manual effort of producing standard operational reports and ensures consistent formatting and data freshness. It is distinct from Decision Intelligence in that it automates the production of reports rather than interpreting what those reports mean.

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Cognitive analytics for enterprises

Cognitive analytics for enterprises is the application of AI reasoning capabilities, including pattern recognition, natural language understanding, and inferential logic, to enterprise business data. It goes beyond statistical analysis to interpret data in context, identifying meaning rather than just measuring metrics. Enterprise deployments typically involve complex, multi-source data environments and require the AI to understand domain-specific definitions and business rules.

Context-aware data storytelling

Context-aware data storytelling is the generation of data narratives that are shaped by the specific role, industry, business unit, or strategic priorities of the intended audience. It ensures that the same underlying data produces different explanations depending on whether the reader is a CFO, a supply chain manager, or a regional director. The result is analysis that is immediately relevant to the reader rather than requiring reinterpretation for each audience.

Contextual prompt generation

Contextual prompt generation is the automated construction of analysis prompts that incorporate relevant business context, including current performance baselines, historical comparisons, and organizational priorities, before submitting a query to an AI system. It improves the quality and specificity of AI-generated analysis by reducing the reliance on users to provide complete context manually. It is a key enabler of consistent, high-quality output in AI-powered analytics platforms.

Continuous data monitoring

Continuous data monitoring is the ongoing, automated surveillance of connected data sources to detect changes, anomalies, or threshold breaches as they occur rather than on a scheduled reporting cycle. It enables organizations to respond to operational signals in near real time rather than discovering issues during a weekly or monthly review. It is the data layer that supports proactive alerting and autonomous analytics.

Conversations

Conversations is an eyko Beats capability that allows users to interrogate a Playbook in natural language after it has been generated. Rather than switching to a separate query tool, users ask follow-up questions directly within the Playbook context, drilling into specific data points, requesting breakdowns, or testing assumptions against the underlying analysis. Conversations keeps the analytical dialogue connected to the structured evidence already in the Playbook, so follow-up questions produce grounded answers rather than generative guesses.

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Data-backed strategic insights

Data-backed strategic insights are business conclusions and recommendations that are directly supported by quantified evidence from organizational data, rather than relying on intuition, anecdote, or general market knowledge. They link specific data findings to specific recommended actions, making the basis for a recommendation traceable and auditable. They are distinct from general strategic advice in that every assertion can be attributed to a specific data source or finding.

Data-driven narrative generation

Data-driven narrative generation is the automatic production of structured prose that explains what business data shows, why it matters, and what it implies for decision-making. It converts quantitative findings into plain-language analysis that can be consumed by non-technical audiences without further interpretation. The quality of generated narratives depends on the richness of the underlying data model and the depth of business context available to the generation system.

Decision Intelligence

Decision Intelligence is a category of business software that goes beyond data visualization to explain what is happening in a business, why it is happening, and what should be done about it. Where business intelligence (BI) tools present data in charts and dashboards, Decision Intelligence platforms generate structured, evidence-based briefings that include root cause analysis and recommended actions. eyko defines Decision Intelligence as the operational layer above dashboards, where data becomes direction rather than simply becoming visible.

Deep system telemetry analysis

Deep system telemetry analysis is the analysis of detailed operational signals from business systems, including transaction logs, process timings, error rates, and usage patterns, to diagnose performance issues and surface optimization opportunities. It treats system behavior as a data source in its own right, rather than only analyzing the business outputs that systems produce. It is particularly relevant for organizations running high-transaction ERP environments where operational efficiency directly affects financial performance.

Domain-aware AI platform

A domain-aware AI platform is an AI analytics platform that is trained or configured to understand the specific terminology, logic, and relationships of a particular business domain, such as finance, supply chain, or manufacturing, rather than operating as a general-purpose AI. Domain-aware platforms produce more accurate and relevant analysis because they interpret data within the correct business context rather than applying generic reasoning. Domain awareness is typically achieved through structured knowledge loading, fine-tuning, or skills-based configuration.

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Embedded domain knowledge graphs

Embedded domain knowledge graphs are structured representations of relationships between business concepts, entities, and rules that are built into an AI platform to improve the accuracy of generated analysis. A domain knowledge graph might encode the relationships between product categories and margin structures, or between customer segments and payment behavior. Embedding this structure enables the AI to reason about business data in terms of actual business logic rather than pure statistical correlation.

End-to-end analytics automation

End-to-end analytics automation is the automation of the complete analytics workflow, from data ingestion and transformation through analysis, narrative generation, and distribution, without requiring manual intervention at any stage. It reduces the total time and resource cost of producing analytical outputs and ensures consistency across the reporting cycle. It is distinct from partial automation, where some steps such as interpretation or formatting still require human effort.

Enterprise AI data platform

An enterprise AI data platform is a software platform designed to connect, process, and analyze large volumes of enterprise business data using AI, at the scale and security requirements of large organizations. Enterprise AI data platforms typically include connectors for major ERP and CRM systems, data transformation capabilities, AI-powered analysis, and enterprise-grade access controls and audit trails. They are built to operate across complex, multi-system environments rather than on a single data source.

Evidence-based decision support

Evidence-based decision support is a decision-making approach in which recommendations are grounded in quantified analysis drawn from actual organizational data, rather than relying on experience or assumption alone. Evidence-based decision support systems surface the specific data findings that support each recommendation, making the basis for action transparent and reviewable. This approach reduces decision risk and improves organizational accountability by creating a traceable link between data and action.

Explainable AI insights

Explainable AI insights are AI-generated analytical findings that include a clear, human-readable explanation of how the conclusion was reached, which data was used, and what logic was applied. Explainability is a critical requirement for enterprise AI deployments because leaders need to trust and defend the recommendations they act on. Explainable AI reduces the black-box risk by making the reasoning behind each insight visible and auditable.

eyko Beats

eyko Beats is eyko's Decision Intelligence platform. It connects directly to the business systems an organization already runs (ERP, CRM, finance, supply chain, and operations) and generates AI Playbooks in response to plain language prompts. eyko Beats replaces the manual cycle of analyst interpretation, stakeholder alignment, and executive reporting with structured, decision-ready intelligence delivered in minutes.

eyko Ideas

eyko Ideas are pre-built Playbook starting points for specific, recurring business questions. Each Idea is a defined analytical template built around a known business problem (churn risk, demand anomaly detection, margin sensitivity, pipeline coverage, and more) across five domains: Customer, Supply Chain, Sales, Marketing, and Financials. Users select an Idea, connect their data, and receive a full Know What / Know Why / Know What Next analysis in seconds. eyko currently provides 30 Ideas across the platform.

eyko Skills

eyko Skills are structured knowledge modules that load business context directly into the eyko Beats platform before a Playbook is generated. Skills define the industry, persona, company, subject matter, or business application that should shape the analysis. Combining a Manufacturing industry Skill with a CFO persona Skill and a Cash Management subject Skill produces a Playbook built specifically for that context, not a generic AI output. Skills are the mechanism through which eyko Playbooks reflect how a specific business actually operates.

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Generative AI storytelling

Generative AI storytelling is the use of generative AI to produce narrative explanations of business data, converting analytical findings into structured prose that describes what happened, why it happened, and what it means. It automates the interpretive writing that analysts and managers typically produce manually after reviewing reports. In a business context, the quality of generated stories depends on the richness of the underlying data and the business context available to the AI system.

Generative reporting assistant

A generative reporting assistant is an AI-powered tool that generates business reports in response to natural language prompts, drawing from connected data sources to produce structured outputs including narratives, tables, and charts. It replaces the manual report-building process with a prompt-driven workflow, reducing the time from question to formatted output. The assistant's usefulness is determined by the breadth of data it can access and the quality of business context it applies to the generation process.

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Insight-to-action narratives

Insight-to-action narratives are structured analytical outputs that progress from identifying a business finding to recommending a specific action, with clear reasoning connecting the two. They are designed to eliminate the gap between "here is what the data shows" and "here is what you should do about it," a gap that typically requires analyst interpretation or management discussion to bridge. Insight-to-action narratives are the output format of Decision Intelligence platforms and contrast with the observation-only output of traditional dashboards and reports.

Intelligent data summarization

Intelligent data summarization is the automatic reduction of large volumes of business data into concise, prioritized summaries that highlight the most significant findings without requiring users to review the full dataset. It applies AI to rank findings by materiality, filter noise, and structure output for the intended audience. The goal is to reduce the cognitive load on decision-makers by presenting only what is most relevant rather than everything that is available.

Intelligent observability platform

An intelligent observability platform monitors business systems and data sources continuously, using AI to detect anomalies, surface signals, and provide explanations when conditions change. It goes beyond traditional monitoring by adding interpretation, not just flagging that a metric moved, but identifying why it moved and whether it warrants attention. It bridges the gap between system-level monitoring tools and business analytics platforms.

Intelligent prompt augmentation

Intelligent prompt augmentation is the automatic enrichment of a user's plain-language query with relevant context, including data definitions, historical baselines, business rules, and role-specific framing, before it is submitted to an AI analytics system. It improves the precision and relevance of AI-generated analysis without requiring the user to provide complete context manually. It is a key component of enterprise AI platforms that need to produce consistent, high-quality output across a range of user skill levels.

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Know What / Know Why / Know What Next

Know What / Know Why / Know What Next is the three-part analytical framework that structures every eyko Playbook. Know What defines what changed or is currently happening in the business. Know Why investigates the root causes behind that change across connected systems. Know What Next delivers prioritized, evidence-based recommended actions tied directly to those findings. The framework is designed to replace the manual interpret-investigate-align cycle that follows most dashboard reviews.

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Narrative intelligence platform

A narrative intelligence platform is a software platform that generates structured, plain-language business narratives from enterprise data, designed to replace or supplement analyst-written reporting with automated, AI-generated explanations. It combines data connectivity, AI reasoning, and natural language generation to produce outputs that can be consumed directly by business leaders without requiring technical interpretation. The platform's value is measured by the accuracy, relevance, and actionability of the narratives it produces.

Natural language query interface

A natural language query interface allows business users to interact with data systems using plain-language questions rather than structured query languages such as SQL. Natural language interfaces lower the technical barrier to data access, enabling non-technical users to retrieve and analyze data without analyst support. The accuracy of responses depends on the quality of the underlying data model and the semantic layer that maps business language to data structures.

No-code reporting tools

No-code reporting tools are reporting and analytics tools that enable business users to build and modify reports without writing code or SQL. They use visual interfaces such as drag-and-drop builders, template libraries, and point-and-click configuration to make report creation accessible to non-technical teams. They reduce dependence on analyst or IT resource for standard reporting tasks while accepting a degree of flexibility tradeoff compared to code-based approaches.

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Predictive analytics engine

A predictive analytics engine is a system that applies statistical models and machine learning to historical business data to forecast future outcomes, identify emerging trends, and quantify probabilities. Predictive analytics moves analysis from describing the past to anticipating the future, enabling proactive rather than reactive decision-making. Common applications include demand forecasting, churn prediction, revenue projection, and risk scoring.

Prompt engineering assistant

A prompt engineering assistant is a tool that helps users construct effective prompts for AI-powered analytics systems, improving the quality and specificity of generated outputs. Prompt engineering assistants may suggest prompt structures, apply business context automatically, or reframe vague queries into precise analytical requests. As AI-powered analytics platforms mature, prompt quality increasingly determines the quality of the analysis produced.

Pulses

Pulses is an eyko Beats capability that monitors connected data sources and surfaces performance alerts when metrics deviate from expected ranges. Unlike scheduled reports or static dashboards that require users to actively check in, Pulses sends proactive signals when something in the business changes and warrants attention. Pulses operate as the early warning layer that identifies when a Playbook analysis should be triggered.

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Real-time data visualization

Real-time data visualization is the presentation of business data in visual formats (charts, graphs, maps, dashboards) that update automatically as underlying data changes, without requiring manual refresh or report rebuild. It gives operations and leadership teams a current view of business performance rather than a snapshot from the last reporting cycle. It is a foundational capability of modern BI platforms and is most valuable when the data being monitored changes frequently enough to affect decisions.

Reasoning engine for analytics

A reasoning engine for analytics is the component of an AI analytics platform responsible for applying logical inference, causal analysis, and structured reasoning to business data to produce explanations and recommendations. A reasoning engine goes beyond pattern recognition or statistical correlation to identify why things are happening and what the implications are. It is the distinguishing component of Decision Intelligence platforms compared to conventional BI tools that surface what happened without explaining it.

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Scalable data intelligence solution

A scalable data intelligence solution is a data analytics architecture that maintains performance and accuracy as the volume of data, number of users, and complexity of analysis grows. Scalability is a foundational requirement for enterprise deployments where data volumes can reach billions of rows and concurrent user demands can be significant. A scalable solution achieves this without requiring proportional increases in infrastructure cost or manual administration.

Self-healing data pipelines

Self-healing data pipelines are data integration pipelines that automatically detect and correct errors, such as schema changes, failed connections, or data quality issues, without requiring manual intervention. They reduce the operational overhead of maintaining data infrastructure and minimize the risk of silent failures that produce inaccurate analytical outputs. They are particularly valuable in environments with many connected source systems where manual monitoring of each connection is impractical.

Self-service analytics platform

A self-service analytics platform enables business users to access, analyze, and visualize data independently, without requiring technical support from IT or data engineering teams for standard analytical tasks. Self-service platforms provide intuitive interfaces, pre-built data models, and visual analysis tools that put data access in the hands of the people closest to business decisions. The value of self-service is measured by the proportion of analytical questions that teams can answer without analyst mediation.

Semantic layer for prompt design

A semantic layer for prompt design is a structured mapping between business terminology and the underlying data fields, relationships, and definitions that an AI system uses to interpret user queries. A semantic layer ensures that when a user asks about "gross margin" or "days sales outstanding," the AI applies the organization's specific definition rather than a generic one. In AI-powered analytics, the semantic layer is what makes natural language queries produce accurate, business-specific results.

Smart dashboard generation

Smart dashboard generation is the automatic creation of dashboard layouts and visualizations based on the data available, the user's role, and the business questions most likely to be relevant, rather than requiring users to manually select charts and configure views. It applies AI to determine which metrics, comparisons, and time periods are most meaningful given the context, reducing the configuration effort required to produce a useful starting point. The result is more relevant than a blank canvas but more flexible than a static template.

System-aware prompt engineering

System-aware prompt engineering is the practice of constructing AI prompts that account for the specific structure, terminology, and logic of the business systems from which data is drawn. It ensures that queries produce accurate results by aligning the prompt language with the data model of the source system. For example, applying JD Edwards or SAP field names and process definitions rather than generic terms. This is a prerequisite for high-quality AI analysis in complex ERP environments.

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The Decision Layer

The Decision Layer is the analytical tier that sits above traditional business intelligence. BI tools operate at the reporting layer: they visualize and present data. The Decision Layer is where that data is interpreted, root causes are identified, and actions are recommended. Most organizations rely on analysts and leadership meetings to operate this layer manually. eyko Beats is built to operate at this layer automatically, picking up where dashboards leave off.

Decision Intelligence, defined.

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