A data quality assessment is a structured process of profiling and scoring your data against defined quality dimensions — accuracy, completeness, consistency, timeliness, uniqueness, and validity — to identify where it falls short of what the business needs. The output is a factual, prioritised picture of data health that tells you exactly which systems, fields, […]
The dimensions of data quality are the measurable criteria used to judge whether data is fit for use: accuracy, completeness, consistency, timeliness, uniqueness, and validity. Each dimension captures a different way data can fail — a record can be complete but wrong, or accurate but outdated — so a full data quality picture requires assessing […]
Data quality management is the set of policies, roles, processes, and tools an organisation uses to make sure its data is accurate, complete, consistent, timely, unique, and valid for the purposes it’s used for — from daily operations to regulatory reporting to AI models. It matters because every downstream decision, report, customer interaction, and automated […]
MDM vs data warehousing comes down to a simple distinction: master data management governs and consolidates the core business entities — customers, products, suppliers — into one trusted, current version, while a data warehouse stores and organises historical transactional and operational data for reporting and analysis. MDM answers “who or what is this, definitively?” A […]
A golden record is the single, consolidated, trusted version of a business entity — a customer, product, supplier, or location — that results from a golden record master data management process. It’s built by matching duplicate and fragmented records from across an organisation’s systems, resolving conflicts between them using defined rules, and publishing the result […]
Master data management (MDM) is the discipline of identifying, cleaning, consolidating, and governing an organisation’s core business data — customers, products, suppliers, locations, employees, assets — so that every system and every person in the business works from one consistent, trusted version of that information. Instead of a customer’s address living differently in the CRM, […]
Data stewardship vs data ownership comes down to accountability versus execution: a data owner is the accountable business leader responsible for a data domain’s outcomes, while a data steward is the person who manages that data’s definitions, quality, and day-to-day decisions on the owner’s behalf. Confusing the two — or leaving both undefined — is […]
A data governance roadmap is a phased plan for building governance capability incrementally — starting with high-priority data domains and expanding scope over time — rather than attempting to govern all enterprise data at once. Enterprises that try to launch governance as a single big-bang program typically stall under the weight of competing priorities and […]
POPIA compliance data governance means using structured data governance controls — clear ownership, documented policies, data quality standards, and access management — to satisfy POPIA’s legal requirements in a way that is auditable and sustainable, rather than reactive. Compliance teams can write a privacy policy in a week; they cannot know where every instance of […]
Data governance is the system of decision rights, accountability, policies, and processes that determines how an organization manages its data as a business asset. A data governance framework is the structured way that system gets documented and applied — defining who owns which data, what quality standards it must meet, who can access it, and […]