--- title: "Resources | Shipshape Data" description: "Practical guides on data foundations, governance and AI delivery from Shipshape Data. Written by the team that builds these systems for clients." canonical: https://shipshapedata.com/resources/ language: en-GB --- # Guides for getting your data shipshape. > Practical guides on data foundations, governance and AI delivery from Shipshape Data. Written by the team that builds these systems for clients. Canonical page: https://shipshapedata.com/resources/ On this page: - Data & architecture - Data cleaning: what it involves - Data contracts: what they are and how they work - What is a data dictionary? - Data ingestion explained: batch, streaming, and CDC - Data orchestration: how data pipelines get scheduled, retried, and rerun - Data reconciliation: why your systems disagree and how to fix it - Data warehousing: how it works and when you need it - Star schema vs snowflake schema: choosing the right dimensional model - Batch versus real-time processing: choosing how data moves - 10 best data lineage tools for enterprise (2026) - 12 open-source data pipeline tools for modern ETL (2026) - Cloud migration explained: strategy, phases, steps, and costs - Data lake vs data warehouse: key differences for AI, ML, and BI - 7 data pipeline design patterns for modern analytics (2026) - ETL vs ELT: key differences, performance, and use cases - What is a data lakehouse? Architecture, benefits, and use cases - What is data integration? Definition, types, and use cases - What is a data pipeline? Definition, types, and key examples - Data pipeline architecture: a complete guide - What is data engineering? Skills, tools, use cases, and ROI - Data lakehouse vs data warehouse: which architecture wins? - Data engineering vs data science: roles, skills, and salary - Collibra data quality: features, use cases, and benefits - 9 best data pipeline monitoring tools for enterprise teams - 12 open-source data observability tools compared (2026) - Data quality management: dimensions, frameworks, and practices - Data silos: definition, problems, and how to eliminate them - Data strategy: definition, pillars, frameworks, and steps - What is data lineage? Definition, benefits, and examples - The complete guide to Informatica data quality (IDQ/CDQ) - Data lakehouse architecture: definition, layers, and diagrams - Data quality: what it is, why it matters, and how to improve it - How to improve data quality: 7 practical, proven strategies - Tableau data lineage: what it is and how to trace impact - Cloud migration roadmap: a step-by-step plan for enterprises - Databricks data quality: tools, rules, and monitoring at scale - Data engineering consulting services: what they include - Bigeye data observability: features, use cases, and reviews - AWS data engineering: skills, services, and learning path - Azure data engineering: skills, tools, and career roadmap - Data quality framework: components, steps, and practices - Great Expectations data quality: how to automate validation - Monte Carlo data observability: features, pricing, and reviews - Data quality dimensions: definitions, examples, and metrics - Soda data quality: how to implement, integrate, and compare - AI & integration - RAG architecture: how to build a pipeline that holds up - AI due diligence: what private equity investors need to check - Predictive maintenance: how it works and why it matters - AI sales assistants: what they do and where they break - Dynamic content optimisation: how it works and where it goes wrong - Latency in AI systems: why responses take as long as they do - AI workflow orchestration: coordinating the steps behind an AI feature - Fine-tuning: when it's the right tool, and when it's the expensive one - Context windows: how much a language model can consider at once - Multimodal interactions: text, images, audio, and documents, working together - Agentic AI: what it is and when it is worth building - Preventing hallucination in AI systems - Conversational AI: chat interfaces over your own data and processes - Feature engineering: turning raw data into the inputs a model can learn from - Model drift: why a model that worked at launch degrades over time - Model validation and testing: how you know it's ready to deploy - What is model deployment? From training to production - Generative AI agents: definition, architecture, and use cases - MLOps architecture: components, design principles, and workflow - What is MLOps? Benefits, lifecycle, and best practices - What is prompt engineering? Techniques, examples, and tips - Retrieval-augmented generation: how RAG works for teams - What is a vector database? How embeddings power AI search - Embeddings: how they work, APIs, and enterprise use cases - Generative AI explained: what it is, with examples - Feature store in MLOps: what it is and why it matters - Qdrant vector database: architecture, indexing, and scale - Chroma vector database: features, setup, and use cases - Pinecone vector database: features, use cases, and setup - Chain of thought prompting: definition, examples, and templates - How to use OpenAI Evals to test and tune LLMs in production - How to evaluate large language models: metrics and benchmarks - Model monitoring in production: metrics, drift, and alerts - 13 prompt engineering best practices for better LLM output - 12 best MLOps tools for tracking, deployment, and monitoring - Meta Llama model access: official registration and platforms - Databricks LLM evaluation: MLflow metrics and best practices - AWS SageMaker model monitoring: how to set up drift alerts - Cohere prompt engineering: techniques, tips, and examples - Anthropic prompt engineering guide: master Claude models - Prompt engineering: a practical guide with tips and examples - AI foundations - Industrial AI: what it is and where it works - Smart manufacturing: what it means and how to start - Artificial general intelligence: what AGI would mean and what it doesn't change yet - Deep learning: what more layers buy you - Natural language processing: getting computers to work with human language - Machine learning: how systems learn patterns from data - AI augmentation: extending what people can do rather than replacing them - Federated learning: how it works and when it's the right answer - Latent space: what it is and why it is hard to read - AI democratisation: widening access without losing control - AI as a service: what you're buying - Large language models: what an LLM is and how it works - The AI workforce: how AI changes the shape of work and teams - Artificial intelligence: a grounded definition for a business audience - Ensemble learning: bagging, boosting, and stacking explained - AI implementation strategy: from first use case to live - AI maturity models: what each stage looks like - AI-driven decision support: what it is and how to build it - Inference in AI: what happens when a model runs - Neural networks: how they work, with simple business examples - What is supervised learning? Basics, algorithms, and examples - Unsupervised learning: a complete guide for business - Reinforcement learning: definition, how it works, and examples - Overfitting: what it is, why it happens, and how to fix it - Hyperparameters: a plain-English guide to tuning - Predictive analytics: what it is and how it works, with examples - Enterprise AI: what it is, benefits, use cases, and platforms - Narrow AI: definition, examples, and enterprise use cases - Unstructured data - Data augmentation: manufacturing useful variety when you cannot get more real data - Unstructured data: definition, examples, and AI use cases - What is a knowledge graph? Turning data into context - Synthetic data: definition, generation, use cases, and privacy - AI governance & ethics - EU AI Act summary: risk tiers, obligations, and timeline - Black box AI: why models stay opaque and how to reduce it - Human in the loop: keeping people in AI decision processes - What is AI governance? Principles, risks, and how to start - What is responsible AI? Principles, practices, and governance - Ethical AI: principles, governance, and practical steps - AI bias: causes, types, examples, and how to reduce it - The complete guide to the NIST AI Risk Management Framework - Explainability in AI: what it is and why it builds trust - OECD AI principles: the five values for trustworthy AI - AI governance checklist: 12 steps for enterprise AI teams - AI governance vs data governance: key differences and overlap - Responsible AI governance framework: principles and steps - AI governance best practices: framework, policies, and controls - AI risk management: NIST AI RMF, steps, and best practices - AI governance framework: from principles to implementation - Model cards: what they are, examples, and how to create one - Business intelligence - Demand forecasting: what it is and how to get it right - Inventory optimisation: holding the right stock, in the right place - Supply chain analytics: what it is and how to use it - Supply chain optimisation: what it means and how to approach it - A/B testing: comparing two versions properly - Business intelligence: what it is and how the stack fits together - MicroStrategy business intelligence: architecture explained - Business intelligence vs data analytics: a practical guide - Business intelligence architecture: basics, design, and examples - Self-service business intelligence: definition, tools, and tips - Qlik business intelligence: platform, features, and dashboards - Oracle business intelligence: features, editions, and pricing - 9 best business intelligence tools for enterprises in 2026 - Attribution modelling: what it is, types, and how to choose - 11 best business intelligence software in 2026 (free and paid) - How to choose cloud business intelligence solutions - SAP business intelligence: what it is, tools, and comparisons - Data governance & compliance - Compliance automation: controls as code and continuous evidence - Data privacy under GDPR and CCPA: a practical guide - Master data management: what it is and how to start - 16 master data governance solutions for enterprise (2026) - The DAMA data governance framework: principles and best practices - Managed data security services: what they are and what you get - 12 best data governance tools compared for enterprise (2026) - Data security governance framework: components and template - What is GDPR compliance? Principles, requirements, and checklist - Collibra data governance: features, benefits, and use cases - 12 data governance best practices to implement in 2026 - Data governance consulting services: what to expect in 2026 - What is a data protection impact assessment (DPIA)? - What is data security? Principles, types, and best practices - What is data governance? Pillars, roles, examples, and ROI - GDPR data subject rights: the eight rights and how to use them - Data quality governance: definition, models, and strategy --- Shipshape Data is a London AI consultancy: we build the data foundation your AI depends on, then the AI on top. Site guide for agents: https://shipshapedata.com/llms.txt | Contact: hello@shipshapedata.com