Technical Definitions & Acronyms
Quick reference for all the technical terms and acronyms you'll encounter in vendor-prospect discovery
Pro Tips for Maximum Value
🎯 When to Use This Tool
Use this before discovery calls to prep on prospect's tech stack or during calls when unfamiliar terms arise. Don't fake knowledge - "Let me look that up" (2 minutes with this tool) beats "Yeah, we work with that all the time" (lies).
✅ Best Practices
- •Review "Why It Matters" sections before calls - Understanding that Snowflake indicates Level 3+ data readiness helps you qualify prospects faster.
- •Use category filters to study prospect's ecosystem - If they use SAP, review all ERP + Integration terms to prepare for complex middleware discussions.
- •Bookmark frequently-referenced terms - Keep this page open during discovery calls for quick reference when prospect mentions "our Informatica pipeline."
- •Note related terms for follow-up questions - If prospect uses Kafka, check related terms (Event Streaming, Microservices) for intelligent follow-ups.
🚨 Common Mistakes to Avoid
- ✗Assuming you know a term without checking - "API Gateway" might mean AWS API Gateway, Kong, or Apigee. Each has different integration approaches.
- ✗Using acronyms without confirming meaning - "We use DLP" could mean Data Loss Prevention OR a specific vendor product. Always clarify.
- ✗Skipping "Examples" sections - Knowing Oracle has multiple ERP products (Fusion, NetSuite, E-Business Suite) prevents embarrassing assumptions.
- ✗Not studying before technical deep-dive calls - If CTO is on the call and you don't know Kubernetes from Docker, credibility vanishes instantly.
🔗 Combine with Other Tools
- →Discovery Questions - Use technical definitions to craft informed questions about prospect's infrastructure and data readiness
- →Qualification Scorecard - Technical terms inform scoring (Databricks user = higher data readiness score than Excel-only shop)
- →Technical Readiness Checklist - Cross-reference terms from checklist with definitions to verify customer prerequisites
SAP (Systems, Applications & Products in Data Processing)
Leading enterprise ERP platform used by large enterprises for finance, HR, supply chain, and manufacturing. SAP implementations are typically complex and heavily customized.
Examples:
SAP S/4HANA (cloud/on-premise), SAP Business One (SMB version)
Why It Matters:
SAP integrations often require specialized middleware and certified consultants. Expect longer integration timelines and higher costs than other ERPs.
Related Terms:
Oracle ERP Cloud
Enterprise resource planning platform from Oracle covering financials, procurement, project management, and supply chain. Available as cloud (Fusion) or on-premise (E-Business Suite).
Examples:
Oracle Fusion Cloud, Oracle E-Business Suite, Oracle NetSuite
Why It Matters:
Oracle has multiple ERP products (Fusion, NetSuite, JD Edwards, PeopleSoft). Clarify which version during discovery as integration approaches differ significantly.
Related Terms:
Microsoft Dynamics
Microsoft's suite of ERP and CRM applications. Dynamics 365 is the cloud version; older versions include Dynamics AX, NAV, GP, and SL.
Examples:
Dynamics 365 Finance & Operations, Dynamics 365 Business Central, Dynamics NAV
Why It Matters:
Strong Azure integration and familiar Microsoft ecosystem. Good API availability. Many distribution and manufacturing companies use Business Central or NAV.
Related Terms:
Epicor P21
ERP system specifically designed for wholesale distribution businesses. Focuses on inventory management, purchasing, pricing, and warehouse operations.
Examples:
P21 (on-premise), Epicor Prophet 21 (cloud version)
Why It Matters:
Very common in distribution. If prospect uses P21, they likely have distribution-specific workflows and expect industry-specific features from AI solutions.
Related Terms:
NetSuite
Cloud-based ERP platform owned by Oracle, popular with mid-market companies. Includes financials, CRM, e-commerce, and inventory management in one platform.
Why It Matters:
Cloud-native with good API access (SuiteTalk REST/SOAP, SuiteScript). Strong for companies with e-commerce operations. Easier integration than traditional Oracle products.
Related Terms:
Infor
ERP vendor offering industry-specific solutions. CloudSuite is the modern cloud version; legacy products include Infor LN, M3, and SyteLine.
Examples:
Infor CloudSuite Industrial, Infor LN (manufacturing), Infor M3 (distribution)
Why It Matters:
Infor has many products from acquisitions. Ask which specific Infor system they use as capabilities and integration approaches vary widely.
Related Terms:
Snowflake
Cloud data warehouse platform built for analytics. Separates storage and compute, allowing independent scaling. Supports structured and semi-structured data.
Why It Matters:
If prospect has Snowflake, they likely have strong data infrastructure (Level 3+). Good for AI integration via Snowpark for Python/Java processing.
Related Terms:
Amazon Redshift
AWS-managed data warehouse service for analytics. Integrates tightly with other AWS services. Based on PostgreSQL but optimized for analytics queries.
Why It Matters:
Strong indicator of AWS ecosystem. If using Redshift, they likely use other AWS services (S3, Lambda, SageMaker), which simplifies cloud AI integration.
Related Terms:
Google BigQuery
Google Cloud's serverless data warehouse for analytics. Pay-per-query pricing model. Scales automatically without infrastructure management.
Why It Matters:
Serverless architecture means no upfront capacity planning. Strong for companies with spiky or unpredictable analytics workloads. Tight integration with Google Cloud AI services.
Related Terms:
Databricks
Unified analytics platform built on Apache Spark. Combines data engineering, data science, and ML capabilities. Strong for processing large-scale data.
Why It Matters:
If using Databricks, prospect likely has data science/ML capability. Indicates Level 4-5 data readiness. Strong signal for Tier 2+ AI readiness.
Related Terms:
MDM (Master Data Management)
Processes and tools to ensure consistency and accuracy of critical business data (customers, products, suppliers) across systems.
Why It Matters:
Strong MDM indicates mature data governance (Level 3+). Weak or absent MDM causes AI quality issues due to duplicate/inconsistent records.
Related Terms:
Tableau
Business intelligence and data visualization platform acquired by Salesforce. Allows non-technical users to create interactive dashboards and reports.
Why It Matters:
If using Tableau, they have analytics culture and stakeholder buy-in for data-driven decisions. Good signal for AI adoption readiness.
Related Terms:
Power BI
Microsoft's business intelligence platform for creating reports and dashboards. Integrates tightly with Excel, Azure, and Microsoft 365.
Why It Matters:
Common in Microsoft shops. Strong Excel integration means users comfortable with data analysis. Often indicates Microsoft/Azure ecosystem for AI integration.
Related Terms:
MuleSoft
Enterprise integration platform (owned by Salesforce) for connecting applications, data, and devices via APIs. Uses "Anypoint Platform" for API management.
Why It Matters:
If using MuleSoft, expect formal API governance and documented integration patterns. Longer approval cycles but cleaner, more maintainable integrations.
Related Terms:
Dell Boomi
Cloud-based integration platform as a service (iPaaS) for connecting cloud and on-premise applications. Low-code approach to building integrations.
Why It Matters:
Indicates hybrid cloud strategy (on-premise + cloud). Boomi can simplify AI integration by providing pre-built connectors for common systems.
Related Terms:
Informatica
Enterprise data integration and management platform. Provides ETL, data quality, MDM, and API management capabilities.
Why It Matters:
Strong indicator of mature data operations (Level 3+). If using Informatica, they likely have dedicated data engineering team.
Related Terms:
Apache Kafka
Distributed event streaming platform for real-time data pipelines. Handles millions of events per second. Used for real-time analytics and data synchronization.
Why It Matters:
If using Kafka, prospect has real-time data infrastructure and DevOps expertise. Good for Tier 3+ implementations requiring low-latency AI inference.
Related Terms:
RabbitMQ
Message broker software for routing and queuing messages between applications. Implements AMQP (Advanced Message Queuing Protocol).
Why It Matters:
Indicates asynchronous processing architecture. Useful for decoupling AI inference from core business processes (e.g., send order to queue, process with AI, return result).
Related Terms:
API Gateway
Entry point for API requests that handles authentication, rate limiting, request routing, and monitoring. Acts as reverse proxy to backend services.
Examples:
AWS API Gateway, Azure API Management, Kong, Apigee
Why It Matters:
If using API Gateway, expect formal API management with rate limits, authentication requirements, and monitoring. Plan for API key management and throttling.
Related Terms:
Talend
Open-source data integration platform providing ETL, data quality, and data governance capabilities. Can be deployed on-premise or in cloud.
Why It Matters:
Common in organizations building custom data pipelines. Indicates technical capability to integrate AI via custom data flows.
Related Terms:
Fivetran
Automated data pipeline service that replicates data from various sources (databases, SaaS apps) into data warehouses. Handles schema changes automatically.
Why It Matters:
Indicates modern ELT approach (Extract-Load-Transform) and cloud data warehouse. Strong signal of Level 3+ data readiness.
Related Terms:
AWS (Amazon Web Services)
Leading cloud platform offering compute (EC2), storage (S3), databases (RDS), and AI/ML services (SageMaker, Bedrock).
Why It Matters:
Most popular cloud provider. If using AWS, consider Bedrock for LLM access, SageMaker for ML, and native service integrations.
Related Terms:
Microsoft Azure
Microsoft's cloud platform with strong enterprise integration. Azure OpenAI Service provides access to GPT models with enterprise security and Microsoft compliance.
Why It Matters:
Strong for Microsoft shops (Dynamics, Office 365, Active Directory). Azure OpenAI is popular for enterprises wanting GPT with Microsoft data residency guarantees.
Related Terms:
GCP (Google Cloud Platform)
Google's cloud platform with strengths in data analytics (BigQuery), Kubernetes, and AI/ML (Vertex AI, Gemini).
Why It Matters:
Strong analytics capabilities via BigQuery. Vertex AI provides unified ML platform. Consider if prospect already using Google Workspace.
Related Terms:
Docker
Platform for packaging applications and dependencies into containers that run consistently across environments. Industry standard for containerization.
Why It Matters:
If using Docker, indicates DevOps maturity and ability to deploy containerized AI models. Simplifies deployment across dev/test/prod environments.
Related Terms:
Kubernetes (K8s)
Open-source container orchestration platform for automating deployment, scaling, and management of containerized applications. Industry standard for production containers.
Why It Matters:
If using Kubernetes, indicates mature DevOps and ability to run complex distributed systems. Good for scalable ML inference at Tier 3+.
Related Terms:
Datadog
Cloud-based monitoring and analytics platform for infrastructure, applications, and logs. Provides real-time observability across systems.
Why It Matters:
Strong monitoring culture. Request ability to send AI system metrics to Datadog for unified observability.
Related Terms:
New Relic
Application performance monitoring (APM) and observability platform. Tracks application health, errors, and user experience.
Why It Matters:
Indicates performance-focused culture. Discuss AI inference latency monitoring and integration with New Relic.
Related Terms:
Splunk
Platform for searching, monitoring, and analyzing machine-generated data (logs, metrics, events). Strong for security monitoring and troubleshooting.
Why It Matters:
Common in enterprises with security/compliance requirements. Plan for AI system log integration with Splunk for audit trails.
Related Terms:
Active Directory (AD)
Microsoft's directory service for Windows networks. Manages user authentication, authorization, and group policies in enterprises.
Why It Matters:
Nearly universal in Windows enterprises. Your AI solution should support AD/LDAP authentication for seamless SSO integration.
Related Terms:
LDAP (Lightweight Directory Access Protocol)
Protocol for accessing and maintaining directory information services. Used for centralized authentication in enterprises.
Why It Matters:
Common authentication method for on-premise and hybrid systems. Support LDAP if not offering SAML/OAuth SSO.
Related Terms:
HITRUST
Security framework and certification combining requirements from HIPAA, PCI-DSS, ISO, and other standards. Common in healthcare and financial services.
Why It Matters:
HITRUST certification signals very high security requirements. Expect extensive security documentation and potentially source code audits.
Related Terms:
DLP (Data Loss Prevention)
Security tools that detect and prevent unauthorized data transmission outside the organization. Monitors emails, file transfers, and API calls.
Why It Matters:
If DLP is in place, your AI solution's data transmission will be monitored. Discuss data encryption, data minimization, and DLP integration.
Related Terms:
RBAC (Role-Based Access Control)
Security approach where access permissions are assigned based on user roles rather than individuals. Simplifies management at scale.
Why It Matters:
Your AI solution should support RBAC to align with enterprise security policies. Expect to map roles like Admin, Power User, Viewer.
Related Terms:
SOC (Security Operations Center)
Centralized team and facility for monitoring, detecting, analyzing, and responding to cybersecurity incidents.
Why It Matters:
If SOC exists, expect security incident response requirements, SIEM integration, and potentially 24/7 security monitoring of AI systems.
Related Terms:
SIEM (Security Information and Event Management)
Security system that collects and analyzes security events from across the organization to detect threats in real-time.
Examples:
Splunk, IBM QRadar, LogRhythm, Microsoft Sentinel
Why It Matters:
If SIEM exists, your AI system's security events must integrate. Discuss log format requirements and alert integration.
Related Terms:
Blue-Green Deployment
Deployment strategy maintaining two identical production environments (Blue and Green). Deploy to inactive environment, test, then switch traffic over.
Why It Matters:
Indicates mature deployment practices and zero-downtime requirements. Your AI updates should support this pattern with instant rollback capability.
Related Terms:
Canary Deployment
Deployment strategy where new version is gradually rolled out to small subset of users before full deployment. Allows real-world testing with minimal risk.
Why It Matters:
Your AI solution should support percentage-based traffic routing for canary releases. Expect requests for gradual rollout features.
Related Terms:
GitLab
DevOps platform providing source control (Git), CI/CD pipelines, issue tracking, and deployment automation in one integrated platform.
Why It Matters:
If using GitLab, they have strong DevOps practices. Discuss how your AI solution integrates with GitLab CI/CD pipelines.
Related Terms:
GitHub Actions
CI/CD platform integrated into GitHub for automating build, test, and deployment workflows. Uses YAML configuration files.
Why It Matters:
Popular for modern DevOps. If using GitHub, expect to provide GitHub Actions workflows for deploying/testing your AI solution.
Related Terms:
Jenkins
Open-source automation server for building CI/CD pipelines. Highly customizable but requires maintenance and plugin management.
Why It Matters:
Common in enterprises but indicates potentially legacy CI/CD. Discuss integration points and Jenkins pipeline compatibility.
Related Terms:
Terraform
Infrastructure as Code (IaC) tool for provisioning and managing cloud resources using declarative configuration files. Cloud-agnostic.
Why It Matters:
If using Terraform, they manage infrastructure as code. Consider providing Terraform modules for deploying your AI solution.
Related Terms:
Ansible
Open-source automation tool for configuration management, application deployment, and task automation. Uses YAML playbooks.
Why It Matters:
Common for on-premise or hybrid deployments. Consider providing Ansible playbooks for automated deployment of your solution.
Related Terms:
CAB (Change Advisory Board)
Committee that evaluates and approves proposed changes to IT systems. Reviews risk, impact, timing, and rollback plans before deployment.
Why It Matters:
If CAB exists, expect 2-6 week approval cycles for production changes. Plan timelines accordingly and prepare comprehensive change documentation.
Related Terms:
UAT (User Acceptance Testing)
Final testing phase where actual business users verify that software meets requirements before production deployment.
Why It Matters:
UAT timelines often underestimated. Budget 2-4 weeks for UAT, and ensure clear acceptance criteria are documented upfront.
Related Terms:
RTO (Recovery Time Objective)
Maximum acceptable time that an application can be down after a failure. Measured in hours or days.
Why It Matters:
If RTO is low (< 4 hours), expect high availability requirements, disaster recovery planning, and potentially multi-region deployment.
Related Terms:
RPO (Recovery Point Objective)
Maximum acceptable amount of data loss measured in time. Determines backup frequency. RPO of 1 hour means backups every hour.
Why It Matters:
Low RPO (< 1 hour) requires real-time or near-real-time data replication. Discuss backup strategies and data replication architecture.
Related Terms:
CoE (Center of Excellence)
Team providing leadership, best practices, research, support, and training for a specific focus area. AI CoEs drive enterprise AI strategy and governance.
Why It Matters:
If CoE exists, they'll be key stakeholders in evaluating your solution. Expect technical depth and alignment with enterprise AI standards.
Related Terms:
P1/P2/P3 (Priority 1/2/3)
Incident severity classifications. P1 = system down (respond in minutes), P2 = degraded (hours), P3 = minor issue (days).
Why It Matters:
Understand their P1/P2/P3 definitions and response SLAs. Your support model must align with their expectations for each priority level.
Related Terms:
Runbook
Documented procedures for routine operations, troubleshooting, and incident response. Step-by-step instructions for maintaining systems.
Why It Matters:
Expect to provide runbooks for your AI solution covering deployment, monitoring, troubleshooting, and common issues. Critical for handoff to operations team.
Related Terms:
Escrow
Legal arrangement where source code is held by neutral third party and released to customer if vendor fails to meet obligations (bankruptcy, abandonment).
Why It Matters:
Large enterprises often require source code escrow for mission-critical systems. Understand costs ($5K-$50K/year) and legal implications.
Related Terms:
DMZ (Demilitarized Zone)
Network segment that sits between internal network and internet, providing buffer layer for security. Hosts public-facing services while protecting internal systems.
Why It Matters:
If AI solution needs to communicate with both external APIs and internal ERP, DMZ architecture affects deployment. Discuss firewall rules and network topology.
Related Terms:
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