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Practice terms
The profession: roles, skills, teams, governance and ethics, the vendor market and the job market.
Analytics engineer A role that builds and maintains the transformed, tested data models analysts and dashboards rely on. Analytics translator A role that bridges business stakeholders and technical data teams, turning business problems into analytics questions. Chief data officer (CDO) The senior executive responsible for an organization's data strategy, governance and analytics capability. Data analyst A practitioner who explores and reports on data, typically with SQL and BI tools, to answer specific business questions. Data engineer A practitioner who builds and maintains the pipelines and infrastructure that make data reliably available for analysis. Data literacy The ability to read, interpret, question, and communicate with data accurately enough to make sound decisions. Data scientist A practitioner who combines statistics, programming and domain knowledge to build models and extract insight from data. Analytics certification A credential, from a vendor, platform, or professional body, that verifies a specific analytics skill or tool proficiency. Data portfolio A curated collection of a practitioner's own projects and analyses, used to demonstrate skill to employers or clients. Data visualization literacy The ability to accurately read, interpret, and critically evaluate charts and graphs, and to recognize when one is misleading. Exploratory data analysis An open-ended first pass through a dataset to understand its structure and spot patterns before formal analysis. Analytics center of excellence (CoE) A central group that sets standards, tools and training for analytics work across an organization's business units. Analytics maturity model A staged framework describing how an organization's use of data and analytics develops from ad hoc to fully embedded. Analytics ROI A measure of the financial return an organization gets from its investment in analytics people, tools and data. Data strategy An organization's documented plan for how data will be collected, governed and used to support its business goals. Data-driven decision-making (DDDM) Grounding organizational decisions primarily in data and analysis rather than relying mainly on intuition or precedent. Hub-and-spoke analytics model An operating model that pairs a central standards-setting analytics team with analysts embedded in business units. Algorithmic bias Systematic, unfair skew in a model's outputs that disadvantages particular groups, usually traced to skewed training data or design choices. Anonymization Altering data so no individual can be re-identified from it, even by combining it with other available information. Business glossary A controlled list of business terms and their agreed definitions, kept separate from the technical fields that implement them. California Consumer Privacy Act (CCPA) A California state law giving residents rights over their personal data and requiring businesses to disclose how it is collected and sold. Consent management platform (CMP) Software that collects, records, and enforces a person's consent choices for cookies and data collection across a site or app. Consent mode A Google framework that adjusts how its tags behave based on a visitor's cookie consent choices. Data clean room A controlled environment where two or more parties analyze combined data together without either side seeing the other's raw records. Data ethics The study and practice of using data responsibly, addressing fairness, consent, transparency, and harm beyond what regulation strictly requires. Data governance The policies, roles and processes that determine how data is defined, owned, accessed and kept compliant. Data masking Replacing sensitive values with realistic but fake substitutes so data can be used for testing or analysis without exposure. Data residency The requirement or practice of storing and processing data within a specific country or region's physical or legal boundaries. Data retention policy A documented rule for how long different categories of data are kept before they are deleted or archived. Data sovereignty The principle that data is subject to the laws of the country in which it is collected or stored, regardless of who owns the system. Data stewardship The assignment of named people who are accountable for the quality, definition, and appropriate use of specific data domains. Differential privacy A mathematical technique for publishing aggregate statistics while guaranteeing no individual record can be inferred from the results. EU AI Act The European Union's regulation classifying AI systems by risk level and imposing obligations that scale with that risk. Explainable AI (XAI) Techniques and models that make a machine learning system's predictions understandable to humans, rather than a black box. First-party data Data a company collects directly from its own customers and properties, rather than acquiring it from outside sources. General Data Protection Regulation (GDPR) The European Union's comprehensive data protection law governing how personal data of people in the EU is collected and used. Guardrails Rules, filters, or checks placed around a language model to keep its inputs and outputs within acceptable, safe bounds. Hallucination When a language model generates confident, fluent output that is factually wrong or unsupported by its source material. Health Insurance Portability and Accountability Act (HIPAA) The U.S. law that sets privacy and security rules for protected health information handled by healthcare providers and insurers. LLM evaluation Systematically testing a language model's outputs for quality, accuracy, safety, and consistency before and after deployment. Master data management (MDM) The discipline of maintaining one authoritative, consistent record for core entities like customers or products. Personally identifiable information (PII) Any data that can identify a specific individual, alone or combined with other information, and is subject to privacy regulation. Pseudonymization Replacing identifying data with a reversible token, keeping the mapping back to real identities separate and restricted. Responsible AI A set of practices for developing and deploying AI systems that are fair, transparent, safe, and accountable to the people they affect. Risk assessment instrument (RAI) A statistical tool that scores a person's likelihood of reoffending or failing to appear, used in bail, sentencing, or parole. Server-side tagging Sending tracking events through a first-party server rather than firing vendor tags directly from the browser. Open-core model A business model where a product's core is open-source and free, while advanced features are sold as a proprietary, paid layer on top. Proof of concept (PoC) A small-scale, time-boxed test of whether a proposed tool or approach actually works for a specific use case before wider investment. Request for proposal (RFP) A formal document an organization sends to vendors, describing requirements and asking for a structured proposal, price, and timeline. Total cost of ownership (TCO) The full cost of using a system over its lifetime, including licensing, infrastructure, implementation, and staff time, not just list price. Vendor lock-in The difficulty and cost of switching away from a vendor once an organization is deeply dependent on that vendor's proprietary systems. Take-home assignment A practical exercise given to a job candidate to complete independently, testing real analytical work rather than interview answers.